Method: Proposes Semantically Expanded Prompt Tuning (SEPT) that uses LLM-generated semantic neighbors to regularize prompt embedding space with a novel semantic expansion loss promoting intra-class compactness and inter-class separability.

Result: SEPT consistently improves generalization performance across multiple prompt tuning baselines for audio-language models, enhancing both base-to-new generalization and cross-dataset transferability.

Conclusion: SEPT effectively addresses the Base-New Tradeoff in ALMs by preserving semantic structure in prompt embedding space, establishing a benchmark for prompt generalization in audio-language models.

Abstract: Prompt tuning has achieved remarkable progress in vision-language models (VLMs) and is recently being adopted for audio-language models (ALMs). However, its generalization ability in ALMs remains largely underexplored. We observe that conventional prompt tuning for ALMs also suffers from the Base-New Tradeoff, and we identify that this issue stems from the disrupted semantic structure of the embedding space. To address this issue, we propose Semantically Expanded Prompt Tuning (SEPT)-a plug-and-play framework that explicitly regularizes the prompt embedding space by incorporating semantic neighbors generated by large language models. SEPT introduces a novel semantic expansion loss with margin constraints that promote intra-class compactness and inter-class separability, thereby enhancing the semantic structure of the prompt embedding space. For comprehensive evaluation, we establish the first benchmark setup for prompt generalization in ALMs, covering both base-to-new generalization and cross-dataset transferability. Extensive experiments demonstrate that SEPT consistently improves generalization performance across multiple prompt tuning baselines, while maintaining computational cost during inference. Codes are available in https://github.com/jhyukjang/SEPT.

Method: Two-stage Flow Matching architecture: 1) Semantic Planner generates compact semantic features capturing global identity and temporal sequence of sound events, 2) Acoustic Synthesizer produces high-fidelity acoustic latents conditioned on the semantic plan. Also introduces training-free text-guided editing by steering semantic generation trajectory via velocity field differences.

Result: Extensive experiments show SemanticAudio surpasses existing mainstream approaches in semantic alignment. The framework enables precise attribute-level modifications without retraining.

Conclusion: Operating in semantic space rather than acoustic space improves text-audio alignment and enables flexible editing capabilities. The decoupled design offers better control over audio generation.

Abstract: In recent years, Text-to-Audio Generation has achieved remarkable progress, offering sound creators powerful tools to transform textual inspirations into vivid audio. However, existing models predominantly operate directly in the acoustic latent space of a Variational Autoencoder (VAE), often leading to suboptimal alignment between generated audio and textual descriptions. In this paper, we introduce SemanticAudio, a novel framework that conducts both audio generation and editing directly in a high-level semantic space. We define this semantic space as a compact representation capturing the global identity and temporal sequence of sound events, distinct from fine-grained acoustic details. SemanticAudio employs a two-stage Flow Matching architecture: the Semantic Planner first generates these compact semantic features to sketch the global semantic layout, and the Acoustic Synthesizer subsequently produces high-fidelity acoustic latents conditioned on this semantic plan. Leveraging this decoupled design, we further introduce a training-free text-guided editing mechanism that enables precise attribute-level modifications on general audio without retraining. Specifically, this is achieved by steering the semantic generation trajectory via the difference of velocity fields derived from source and target text prompts. Extensive experiments demonstrate that SemanticAudio surpasses existing mainstream approaches in semantic alignment. Demo available at: https://semanticaudio1.github.io/

Method: Three key components: 1) Non-Markov multi-round data construction with rollback editing and name-based personalization, 2) History-conditioned training with token-level caching to prevent identity drift, 3) Reconstruction-based DiT detokenizer and multi-stage fine-tuning curriculum for high-fidelity image generation.

Result: The framework yields substantial improvements in multi-round consistency and instruction compliance while maintaining strong single-round editing and personalization capabilities.

Conclusion: Explicitly training for non-Markov interactions is crucial for true conversational image generation, enabling models to maintain consistency across multiple rounds of interaction by properly referencing and preserving earlier visual states.

Abstract: Conversational image generation requires a model to follow user instructions across multiple rounds of interaction, grounded in interleaved text and images that accumulate as chat history. While recent multimodal large language models (MLLMs) can generate and edit images, most existing multi-turn benchmarks and training recipes are effectively Markov: the next output depends primarily on the most recent image, enabling shortcut solutions that ignore long-range history. In this work we formalize and target the more challenging non-Markov setting, where a user may refer back to earlier states, undo changes, or reference entities introduced several rounds ago. We present (i) non-Markov multi-round data construction strategies, including rollback-style editing that forces retrieval of earlier visual states and name-based multi-round personalization that binds names to appearances across rounds; (ii) a history-conditioned training and inference framework with token-level caching to prevent multi-round identity drift; and (iii) enabling improvements for high-fidelity image reconstruction and editable personalization, including a reconstruction-based DiT detokenizer and a multi-stage fine-tuning curriculum. We demonstrate that explicitly training for non-Markov interactions yields substantial improvements in multi-round consistency and instruction compliance, while maintaining strong single-round editing and personalization.

Method: Created a dataset of 900 challenging, handcrafted tasks structured as causal chains where each step depends on previous completion. Tasks are grounded in the open web with objectively verifiable answer sets, testing systematic collation, de-duplication/entity resolution, and stopping criteria reasoning.

Result: Evaluation of state-of-the-art agent architectures reveals significant performance limitations: even advanced models struggle to balance high recall with precision, showing failure modes like premature stopping (under-retrieval) and hedging behaviors (overly wide net of low-confidence answers).

Conclusion: DeepSearchQA serves as an essential diagnostic tool highlighting critical headroom in current agent designs and driving future research toward more robust deep-research capabilities in information-seeking agents.

Abstract: We introduce DeepSearchQA, a 900-prompt benchmark for evaluating agents on difficult multi-step information-seeking tasks across 17 different fields. Unlike traditional benchmarks that target single answer retrieval or broad-spectrum factuality, DeepSearchQA features a dataset of challenging, handcrafted tasks designed to evaluate an agent’s ability to execute complex search plans to generate exhaustive answer lists. This shift in design explicitly tests three critical, yet under-evaluated capabilities: 1) systematic collation of fragmented information from disparate sources, 2) de-duplication and entity resolution to ensure precision, and 3) the ability to reason about stopping criteria within an open-ended search space. Each task is structured as a causal chain, where discovering information for one step is dependent on the successful completion of the previous one, stressing long-horizon planning and context retention. All tasks are grounded in the open web with objectively verifiable answer sets. Our comprehensive evaluation of state-of-the-art agent architectures reveals significant performance limitations: even the most advanced models struggle to balance high recall with precision. We observe distinct failure modes ranging from premature stopping (under-retrieval) to hedging behaviors, where agents cast an overly wide net of low-confidence answers to artificially boost recall. These findings highlight critical headroom in current agent designs and position DeepSearchQA as an essential diagnostic tool for driving future research toward more robust, deep-research capabilities.

Method: 1) Developed string alignment algorithm supporting multi-reference labeling, arbitrary-length insertions, and better word alignment. 2) Collected DiverseSpeech-Ru dataset of longform in-the-wild Russian speech with multi-reference labeling. 3) Relabeled popular Russian test sets with multiple references. 4) Created tools for streaming ASR evaluation and transcription visualization. 5) Provided uniform wrappers for offline and streaming ASR models.

Result: Demonstrated that models often adapt to dataset-specific labeling, creating illusion of metric improvement. The improved alignment algorithm works better for non-Latin languages and longform speech. New tools enable better evaluation of streaming ASR and visual comparison of transcriptions.

Conclusion: The proposed evaluation improvements address limitations in current ASR assessment, particularly for non-Latin languages and streaming scenarios. The tools and datasets provide more robust evaluation frameworks for speech recognition research.

Abstract: We propose several improvements to the speech recognition evaluation. First, we propose a string alignment algorithm that supports both multi-reference labeling, arbitrary-length insertions and better word alignment. This is especially useful for non-Latin languages, those with rich word formation, to label cluttered or longform speech. Secondly, we collect a novel test set DiverseSpeech-Ru of longform in-the-wild Russian speech with careful multi-reference labeling. We also perform multi-reference relabeling of popular Russian tests set and study fine-tuning dynamics on its corresponding train set. We demonstrate that the model often adopts to dataset-specific labeling, causing an illusion of metric improvement. Based on the improved word alignment, we develop tools to evaluate streaming speech recognition and to align multiple transcriptions to compare them visually. Additionally, we provide uniform wrappers for many offline and streaming speech recognition models. Our code will be made publicly available.

Method: Developed a contextually ensembled translation framework with human-in-the-loop validation that leverages multiple translation systems to translate English reasoning benchmarks (MGSM, MATH-500, CommonSenseQA, OpenBookQA) into Urdu while preserving contextual and structural integrity.

Result: Created UrduBench and conducted comprehensive evaluation showing multi-step and symbolic reasoning tasks pose significant challenges in Urdu, with stable language alignment being critical for robust reasoning. Performance differences were analyzed across datasets, difficulty levels, model architectures, scaling settings, and language consistency.

Conclusion: Established a scalable methodology for standardized reasoning evaluation in Urdu that provides empirical insights into multilingual reasoning failures, with broad applicability to other low-resource languages.

Abstract: Recent advances in large language models (LLMs) have led to strong reasoning capabilities; however, evaluating such models in low-resource languages remains challenging due to the lack of standardized benchmarks. In particular, Urdu reasoning evaluation has been limited by the sensitivity of machine translation and an emphasis on general language tasks rather than reasoning benchmarks. In this paper, we propose a contextually ensembled translation framework with human-in-the-loop validation that leverages multiple translation systems to develop Urdu reasoning benchmarks while preserving contextual and structural integrity. Using this framework, we translate widely adopted reasoning and question-answering benchmarks, including MGSM, MATH-500, CommonSenseQA, and OpenBookQA, into Urdu, collectively referred to as UrduBench, and conduct a comprehensive evaluation of both reasoning-oriented and instruction-tuned LLMs across multiple prompting strategies. Our analysis reveals performance differences across (1) four datasets, (2) five task difficulty levels, (3) diverse model architectures, (4) multiple model scaling settings, and (5) language consistency tests. We find that multi-step and symbolic reasoning tasks pose significant challenges in Urdu, and that stable language alignment is a critical prerequisite for robust reasoning. Overall, our work establishes a scalable methodology for standardized reasoning evaluation in Urdu and provides empirical insights into multilingual reasoning failures. This experimental setup is also broadly applicable to other low-resource languages. The code and datasets will be publicly released.

Method: Two position-invariant fine-tuning strategies: (1) zero-padding approach adapted from SSL pre-training, and (2) speed perturbations with soft-DTW loss to make training less sensitive to positional correlations.

Result: The soft-DTW-based approach achieves faster convergence and improved downstream performance compared to standard MSE fine-tuning, demonstrating the importance of position-invariant fine-tuning.

Conclusion: Position-invariant fine-tuning strategies, particularly soft-DTW with speed perturbations, are crucial for effective SSL-based speech enhancement to prevent exploitation of positional embeddings and improve downstream task performance.

Abstract: Integrating front-end speech enhancement (SE) models with self-supervised learning (SSL)-based speech models is effective for downstream tasks in noisy conditions. SE models are commonly fine-tuned using SSL representations with mean squared error (MSE) loss between enhanced and clean speech. However, MSE is prone to exploiting positional embeddings in SSL models, allowing the objective to be minimised through positional correlations instead of content-related information. This work frames the problem as a general limitation of self-supervised representation fine-tuning and investigates it through representation-guided SE. Two strategies are considered: (1) zero-padding, previously explored in SSL pre-training but here examined in the fine-tuning setting, and (2) speed perturbations with a soft-DTW loss. Experiments show that the soft-DTW-based approach achieves faster convergence and improved downstream performance, underscoring the importance of position-invariant fine-tuning in SSL-based speech modelling.

Method: 1) Runtime scheduler estimates token difficulty, 2) Adaptive chunking partitions sequences into variable-length chunks based on complexity, 3) Rank-ladder mechanism selects per-chunk LoRA rank and scaling, 4) Boundary-safe composition module preserves output consistency, 5) Policy-driven KV-cache strategies for efficiency.

Result: Achieves up to 34% lower latency and 38% memory reduction compared to baseline LoRA on Wikitext-103 and SQuAD datasets, while maintaining or improving task performance metrics (BLEU, EM, perplexity).

Conclusion: ChunkWise LoRA provides a practical, efficient fine-tuning solution that dynamically adapts to token complexity, offering significant computational savings while maintaining performance, with full compatibility to existing transformer architectures and inference frameworks.

Abstract: Recent advances in low-rank adaptation (LoRA) have enabled efficient fine-tuning of large language models (LLMs) with minimal additional parameters. However, existing LoRA methods apply static rank configurations uniformly across all input tokens, ignoring variation in token complexity and computational requirements. In this work, we propose ChunkWise LoRA, a dynamic and adaptive approach that partitions sequences into variable-length chunks based on token complexity and assigns each chunk a tailored low-rank configuration. Our system introduces a runtime scheduler that estimates token difficulty, performs adaptive chunking, and selects per-chunk LoRA rank and scaling using a rank-ladder mechanism. To preserve output consistency, we further introduce a boundary-safe composition module and integrate policy-driven KV-cache strategies. Experiments on benchmark datasets such as Wikitext-103 and SQuAD demonstrate that ChunkWise LoRA achieves up to 34% lower latency and 38% memory reduction compared to baseline LoRA, while maintaining or improving task performance metrics like BLEU, EM, and perplexity. The proposed framework remains fully compatible with existing transformer architectures and inference frameworks, providing a practical solution for real-world deployment of parameter-efficient LLMs.

Method: Compare data mixing (training on mixed datasets) vs model merging (combining task-specific models) approaches across Qwen Coder and DeepSeek Coder families at 2B and 7B scales, fine-tuned for code generation and summarization tasks.

Result: Model merging achieves best overall performance at larger scale (7B), retaining 96% of specialized model performance on code generation while maintaining summarization capabilities. Merged Qwen Coder 7B achieved 92.7% Pass@1 on HumanEval vs 90.9% for task-specific model. Data mixing preferred at smaller scale (2B).

Conclusion: Careful merging and mixing strategies can effectively combine task-specific capabilities without significant performance degradation, making them ideal for resource-constrained deployment scenarios.

Abstract: Recent research advocates deploying smaller, specialized code LLMs in agentic frameworks alongside frontier models, sparking interest in efficient strategies for multi-task learning that balance performance, constraints, and costs. We compare two approaches for creating small, multi-task code LLMs: data mixing versus model merging. We conduct extensive experiments across two model families (Qwen Coder and DeepSeek Coder) at two scales (2B and 7B parameters), fine-tuning them for code generation and code summarization tasks. Our evaluation on HumanEval, MBPP, and CodeXGlue benchmarks reveals that model merging achieves the best overall performance at larger scale across model families, retaining 96% of specialized model performance on code generation tasks while maintaining summarization capabilities. Notably, merged models can even surpass individually fine-tuned models, with our best configuration of Qwen Coder 2.5 7B model achieving 92.7% Pass@1 on HumanEval compared to 90.9% for its task-specific fine-tuned equivalent. At a smaller scale we find instead data mixing to be a preferred strategy. We further introduce a weight analysis technique to understand how different tasks affect model parameters and their implications for merging strategies. The results suggest that careful merging and mixing strategies can effectively combine task-specific capabilities without significant performance degradation, making them ideal for resource-constrained deployment scenarios.

Method: Integrates universal phone recognition with language-specific phoneme interpretation using contrastive phonological feature distances for phone-to-phoneme mapping and sequence alignment. Produces three metrics: phoneme error rate (PER), phonological feature error rate (PFER), and phoneme coverage (PhonCov).

Result: Analysis on English, Spanish, Italian, and Tamil shows PER benefits from mapping+alignment, PFER from alignment alone, and PhonCov from mapping. Framework captures clinically meaningful patterns of intelligibility degradation consistent with established dysarthric speech observations.

Conclusion: Proposed multilingual framework effectively assesses dysarthria intelligibility across languages by combining universal and language-specific approaches, with different metrics benefiting from different components of the framework.

Abstract: The growing prevalence of neurological disorders associated with dysarthria motivates the need for automated intelligibility assessment methods that are applicalbe across languages. However, most existing approaches are either limited to a single language or fail to capture language-specific factors shaping intelligibility. We present a multilingual phoneme-production assessment framework that integrates universal phone recognition with language-specific phoneme interpretation using contrastive phonological feature distances for phone-to-phoneme mapping and sequence alignment. The framework yields three metrics: phoneme error rate (PER), phonological feature error rate (PFER), and a newly proposed alignment-free measure, phoneme coverage (PhonCov). Analysis on English, Spanish, Italian, and Tamil show that PER benefits from the combination of mapping and alignment, PFER from alignment alone, and PhonCov from mapping. Further analyses demonstrate that the proposed framework captures clinically meaningful patterns of intelligibility degradation consistent with established observations of dysarthric speech.

Method: Used six LLMs (Gemini 3 Flash, GPT-4o, DeepSeek v3.2, GLM-4.7, etc.) to classify ethnicity from names without additional training. Tested on stratified samples from Florida/NC voter files with self-reported race, Lebanese voter registration with religious sect, Indian MPs from reserved constituencies, and Indian land records. Applied extended reasoning and included metadata like party registration. Fine-tuned small transformer models on LLM labels for large-scale deployment.

Result: LLM-based classification achieved up to 84.7% accuracy vs BISG’s 68.2% on balanced samples. With metadata, reached 86.7% accuracy. Reduced income bias where BISG misclassified wealthy minorities as White. Validated internationally: Lebanese religious sect (64.3%), Indian MPs (99.2%), Indian caste (74.0%). Successfully recovered known population distributions across India, Uganda, Nepal, Armenia, Chile, and Costa Rica.

Conclusion: LLMs provide superior ethnicity inference from names compared to traditional methods, with international applicability, reduced bias, and potential for cost-effective local deployment via fine-tuned smaller models.

Abstract: I demonstrate that large language models can infer ethnicity from names with accuracy exceeding that of Bayesian Improved Surname Geocoding (BISG) without additional training data, enabling inference outside the United States and to contextually appropriate classification categories. Using stratified samples from Florida and North Carolina voter files with self-reported race, LLM-based classification achieves up to 84.7% accuracy, outperforming BISG (68.2%) on balanced samples. I test six models including Gemini 3 Flash, GPT-4o, and open-source alternatives such as DeepSeek v3.2 and GLM-4.7. Enabling extended reasoning can improve accuracy by 1-3 percentage points, though effects vary across contexts; including metadata such as party registration reaches 86.7%. LLM classification also reduces the income bias inherent in BISG, where minorities in wealthier neighborhoods are systematically misclassified as White. I further validate using Lebanese voter registration with religious sect (64.3% accuracy), Indian MPs from reserved constituencies (99.2%), and Indian land records with caste classification (74.0%). Aggregate validation across India, Uganda, Nepal, Armenia, Chile, and Costa Rica using original full-count voter rolls demonstrates that the method recovers known population distributions where naming conventions are distinctive. For large-scale applications, small transformer models fine-tuned on LLM labels exceed BISG accuracy while enabling local deployment at no cost.

Method: Proposes OG-MAR framework that: 1) summarizes respondent-specific values from World Values Survey, 2) constructs global cultural ontology via competency questions, 3) retrieves ontology-consistent relations and demographically similar profiles to instantiate multiple value-persona agents, and 4) synthesizes outputs using a judgment agent enforcing ontology consistency and demographic proximity.

Result: Experiments on regional social-survey benchmarks across four LLM backbones show OG-MAR improves cultural alignment and robustness over competitive baselines while producing more transparent reasoning traces.

Conclusion: OG-MAR provides an effective framework for enhancing cultural alignment in LLMs through structured ontology-guided reasoning and multi-agent synthesis.

Abstract: Large Language Models (LLMs) increasingly support culturally sensitive decision making, yet often exhibit misalignment due to skewed pretraining data and the absence of structured value representations. Existing methods can steer outputs, but often lack demographic grounding and treat values as independent, unstructured signals, reducing consistency and interpretability. We propose OG-MAR, an Ontology-Guided Multi-Agent Reasoning framework. OG-MAR summarizes respondent-specific values from the World Values Survey (WVS) and constructs a global cultural ontology by eliciting relations over a fixed taxonomy via competency questions. At inference time, it retrieves ontology-consistent relations and demographically similar profiles to instantiate multiple value-persona agents, whose outputs are synthesized by a judgment agent that enforces ontology consistency and demographic proximity. Experiments on regional social-survey benchmarks across four LLM backbones show that OG-MAR improves cultural alignment and robustness over competitive baselines, while producing more transparent reasoning traces.

Method: Uses ensemble of pre-trained language models that have learned semantic relationships from large text corpora. The method runs locally on open-source models without external API calls, completing linkage tasks in minutes without any training labels.

Result: On benchmarks spanning city names, person names, organizations, multilingual political parties, and bibliographic records, EnsembleLink matches or exceeds methods requiring extensive labeling.

Conclusion: EnsembleLink provides a practical, accurate solution for record linkage without labeled data, addressing methodological gaps in social science research by leveraging semantic understanding from pre-trained language models.

Abstract: Record linkage, the process of matching records that refer to the same entity across datasets, is essential to empirical social science but remains methodologically underdeveloped. Researchers treat it as a preprocessing step, applying ad hoc rules without quantifying the uncertainty that linkage errors introduce into downstream analyses. Existing methods either achieve low accuracy or require substantial labeled training data. I present EnsembleLink, a method that achieves high accuracy without any training labels. EnsembleLink leverages pre-trained language models that have learned semantic relationships (e.g., that “South Ozone Park” is a neighborhood in “New York City” or that “Lutte ouvriere” refers to the Trotskyist “Workers’ Struggle” party) from large text corpora. On benchmarks spanning city names, person names, organizations, multilingual political parties, and bibliographic records, EnsembleLink matches or exceeds methods requiring extensive labeling. The method runs locally on open-source models, requiring no external API calls, and completes typical linkage tasks in minutes.

Method: Leverages large-scale speech training data and the audio understanding capabilities of foundation model Qwen3-Omni. The family includes two ASR models (1.7B and 0.6B parameters) supporting 52 languages/dialects, plus a non-autoregressive forced alignment model for text-speech timestamp alignment in 11 languages.

Result: The 1.7B version achieves SOTA among open-sourced ASR models and competes with proprietary APIs. The 0.6B version offers best accuracy-efficiency trade-off with 92ms average TTFT and can transcribe 2000 seconds of speech in 1 second at concurrency 128. The forced alignment model outperforms three strongest competitors in timestamp accuracy with better efficiency and versatility.

Conclusion: Qwen3-ASR family provides state-of-the-art speech recognition and forced alignment capabilities with strong efficiency, released under Apache 2.0 license to accelerate ASR and audio understanding research.

Abstract: In this report, we introduce Qwen3-ASR family, which includes two powerful all-in-one speech recognition models and a novel non-autoregressive speech forced alignment model. Qwen3-ASR-1.7B and Qwen3-ASR-0.6B are ASR models that support language identification and ASR for 52 languages and dialects. Both of them leverage large-scale speech training data and the strong audio understanding ability of their foundation model Qwen3-Omni. We conduct comprehensive internal evaluation besides the open-sourced benchmarks as ASR models might differ little on open-sourced benchmark scores but exhibit significant quality differences in real-world scenarios. The experiments reveal that the 1.7B version achieves SOTA performance among open-sourced ASR models and is competitive with the strongest proprietary APIs while the 0.6B version offers the best accuracy-efficiency trade-off. Qwen3-ASR-0.6B can achieve an average TTFT as low as 92ms and transcribe 2000 seconds speech in 1 second at a concurrency of 128. Qwen3-ForcedAligner-0.6B is an LLM based NAR timestamp predictor that is able to align text-speech pairs in 11 languages. Timestamp accuracy experiments show that the proposed model outperforms the three strongest force alignment models and takes more advantages in efficiency and versatility. To further accelerate the community research of ASR and audio understanding, we release these models under the Apache 2.0 license.

Method: 1) Define a frozen encoder that maps outputs to a 3D space Z. 2) Outer loop selects target z* in Z. 3) Use retrieval-grounded policy trained with sequence-level RL to generate outputs whose coordinates land near z* under standard autoregressive decoding.

Result: On stories: sweeping Z yields 3.1x higher LLM-scored diversity than prompt-chaining. On code: Bayesian optimization over Z improves objectives withheld from the controller while preserving validity under matched inference budgets.

Conclusion: OS-Search enables efficient exploration and optimization in output spaces, decoupling target selection from generation and enabling parallel sweeps and black-box optimization without path-dependent search.

Abstract: We introduce Output-Space Search (OS-Search), which turns LLM generation into endpoint search. An outer loop selects a target z* in a frozen encoder-defined 3D output space Z, and a retrieval-grounded policy trained with sequence-level RL generates outputs whose coordinates land near z* under standard autoregressive decoding. This enables parallel sweeps and black-box optimization in Z without path-dependent token/program search. On stories, sweeping Z (text) yields 3.1x higher LLM-scored diversity than prompt-chaining. On code, Bayesian optimization over Z (code) improves an objective withheld from the controller under matched inference budgets while preserving validity.

Method: Cross-linguistic corpus analysis of 186 languages to establish three key properties of function words, followed by counterfactual language modeling and ablation experiments with neural learners to test which properties contribute most to hierarchical structure acquisition.

Result: All three properties (high frequency, structural association, boundary alignment) are present across studied languages. Language variants preserving all three properties are more easily acquired by neural learners, with frequency and structural association contributing more strongly than boundary alignment. Different learning conditions lead to systematically different reliance on function words.

Conclusion: The statistical distribution of function words provides crucial support for learning hierarchical structure from linear input, with different properties playing distinct roles in the acquisition process, and similar performance can arise from different internal mechanisms.

Abstract: What statistical conditions support learning hierarchical structure from linear input? In this paper, we address this question by focusing on the statistical distribution of function words. Function words have long been argued to play a crucial role in language acquisition due to their distinctive distributional properties, including high frequency, reliable association with syntactic structure, and alignment with phrase boundaries. We use cross-linguistic corpus analysis to first establish that all three properties are present across 186 studied languages. Next, we use a combination of counterfactual language modeling and ablation experiments to show that language variants preserving all three properties are more easily acquired by neural learners, with frequency and structural association contributing more strongly than boundary alignment. Follow-up probing and ablation analyses further reveal that different learning conditions lead to systematically different reliance on function words, indicating that similar performance can arise from distinct internal mechanisms.

Method: Comprehensive analysis of embedding scaling vs. expert scaling regimes, systematic characterization of architectural factors (parameter budgeting, model width/depth interplay), integration of system optimizations and speculative decoding, and development of LongCat-Flash-Lite model with 68.5B parameters (~3B activated) trained from scratch.

Result: Embedding scaling achieves superior Pareto frontier compared to expert scaling in specific regimes, LongCat-Flash-Lite surpasses parameter-equivalent MoE baselines and shows exceptional competitiveness in agentic and coding domains despite allocating over 30B parameters to embeddings.

Conclusion: Embedding scaling represents a potent alternative to MoE for sparsity scaling, offering better efficiency in certain regimes and practical inference speedups when combined with appropriate system optimizations.

Abstract: While Mixture-of-Experts (MoE) architectures have become the standard for sparsity scaling in large language models, they increasingly face diminishing returns and system-level bottlenecks. In this work, we explore embedding scaling as a potent, orthogonal dimension for scaling sparsity. Through a comprehensive analysis and experiments, we identify specific regimes where embedding scaling achieves a superior Pareto frontier compared to expert scaling. We systematically characterize the critical architectural factors governing this efficacy – ranging from parameter budgeting to the interplay with model width and depth. Moreover, by integrating tailored system optimizations and speculative decoding, we effectively convert this sparsity into tangible inference speedups. Guided by these insights, we introduce LongCat-Flash-Lite, a 68.5B parameter model with ~3B activated trained from scratch. Despite allocating over 30B parameters to embeddings, LongCat-Flash-Lite not only surpasses parameter-equivalent MoE baselines but also exhibits exceptional competitiveness against existing models of comparable scale, particularly in agentic and coding domains.

Method: Systematic study across multiple domains reveals errors concentrate at specific token positions rather than being uniformly distributed. Analysis shows erroneous predictions stem from internal competition mechanisms where certain attention heads (erroneous processing heads or ep heads) amplify incorrect reasoning while suppressing correct ones. The authors propose test-time correction of reasoning - a lightweight intervention that dynamically identifies and deactivates ep heads during inference.

Result: Extensive experiments across different tasks and LLMs show the proposed method consistently improves reasoning hop generalization. Removing individual ep heads during inference often restores correct predictions, demonstrating the effectiveness of targeted intervention at the attention head level.

Conclusion: The research provides insights into why LLMs fail at reasoning hop generalization and offers a practical solution through test-time correction. The findings highlight the importance of understanding internal mechanisms in LLMs and demonstrate that targeted interventions at the attention head level can significantly improve reasoning performance beyond training distributions.

Abstract: Chain-of-thought (CoT) reasoning has become the standard paradigm for enabling Large Language Models (LLMs) to solve complex problems. However, recent studies reveal a sharp performance drop in reasoning hop generalization scenarios, where the required number of reasoning steps exceeds training distributions while the underlying algorithm remains unchanged. The internal mechanisms driving this failure remain poorly understood. In this work, we conduct a systematic study on tasks from multiple domains, and find that errors concentrate at token positions of a few critical error types, rather than being uniformly distributed. Closer inspection reveals that these token-level erroneous predictions stem from internal competition mechanisms: certain attention heads, termed erroneous processing heads (ep heads), tip the balance by amplifying incorrect reasoning trajectories while suppressing correct ones. Notably, removing individual ep heads during inference can often restore the correct predictions. Motivated by these insights, we propose test-time correction of reasoning, a lightweight intervention method that dynamically identifies and deactivates ep heads in the reasoning process. Extensive experiments across different tasks and LLMs show that it consistently improves reasoning hop generalization, highlighting both its effectiveness and potential.

Method: Uses Pythia (truly open LLM with publicly available pretraining data) to create robust evaluation benchmark. Also proposes novel method leveraging pretraining data to build more honest LLMs that know when to say “I don’t know.”

Result: Develops a more robust evaluation benchmark for LLM honesty and proposes a method to improve LLM honesty by better utilizing pretraining data knowledge boundaries.

Conclusion: Addresses the critical problem of LLM hallucination by creating better evaluation methods and techniques for building LLMs that are more honest about their knowledge limitations.

Abstract: Large language models (LLMs) are highly capable of answering questions, but they are often unaware of their own knowledge boundary, i.e., knowing what they know and what they don’t know. As a result, they can generate factually incorrect responses on topics they do not have enough knowledge of, commonly known as hallucination. Rather than hallucinating, a language model should be more honest and respond with “I don’t know” when it does not have enough knowledge about a topic. Many methods have been proposed to improve LLM honesty, but their evaluations lack robustness, as they do not take into account the knowledge that the LLM has ingested during its pretraining. In this paper, we propose a more robust evaluation benchmark dataset for LLM honesty by utilizing Pythia, a truly open LLM with publicly available pretraining data. In addition, we also propose a novel method for harnessing the pretraining data to build a more honest LLM.

Method: Extends MGSM dataset using GSM-Symbolic approach, creating five instantiations per MGSM question by varying names, digits, and irrelevant context. Evaluates across nine languages using various proprietary and open models.

Result: Low-resource languages suffer large performance drops on digit instantiations different from original test set. Proprietary models like Gemini 2.5 Flash and GPT-4.1 are less robust to digit instantiation, while Claude 4.0 Sonnet is more robust. Among open models, GPT-OSS 120B and DeepSeek V3 show stronger robustness.

Conclusion: Evaluating each problem using at least five digit-varying instantiations provides more robust and realistic assessment of math reasoning capabilities across languages.

Abstract: Large language models have made substantial progress in mathematical reasoning. However, benchmark development for multilingual evaluation has lagged behind English in both difficulty and recency. Recently, GSM-Symbolic showed a strong evidence of high variance when models are evaluated on different instantiations of the same question; however, the evaluation was conducted only in English. In this paper, we introduce MGSM-Pro, an extension of MGSM dataset with GSM-Symbolic approach. Our dataset provides five instantiations per MGSM question by varying names, digits and irrelevant context. Evaluations across nine languages reveal that many low-resource languages suffer large performance drops when tested on digit instantiations different from those in the original test set. We further find that some proprietary models, notably Gemini 2.5 Flash and GPT-4.1, are less robust to digit instantiation, whereas Claude 4.0 Sonnet is more robust. Among open models, GPT-OSS 120B and DeepSeek V3 show stronger robustness. Based on these findings, we recommend evaluating each problem using at least five digit-varying instantiations to obtain a more robust and realistic assessment of math reasoning.

Method: SHARP models harm as multivariate random variable, decomposes into bias, fairness, ethics, and epistemic reliability dimensions, uses union-of-failures aggregation reparameterized as additive cumulative log-risk, and employs risk-sensitive statistics like Conditional Value at Risk (CVaR95)

Result: Evaluation of 11 frontier LLMs on 901 socially sensitive prompts shows models with similar average risk can have >2x differences in tail exposure and volatility; bias has strongest tail severities, epistemic/fairness risks intermediate, ethical misalignment consistently lower

Conclusion: Responsible LLM evaluation and governance requires moving beyond scalar averages to multidimensional, tail-sensitive risk profiling to capture worst-case behavior in high-stakes applications

Abstract: Large language models (LLMs) are increasingly deployed in high-stakes domains, where rare but severe failures can result in irreversible harm. However, prevailing evaluation benchmarks often reduce complex social risk to mean-centered scalar scores, thereby obscuring distributional structure, cross-dimensional interactions, and worst-case behavior. This paper introduces Social Harm Analysis via Risk Profiles (SHARP), a framework for multidimensional, distribution-aware evaluation of social harm. SHARP models harm as a multivariate random variable and integrates explicit decomposition into bias, fairness, ethics, and epistemic reliability with a union-of-failures aggregation reparameterized as additive cumulative log-risk. The framework further employs risk-sensitive distributional statistics, with Conditional Value at Risk (CVaR95) as a primary metric, to characterize worst-case model behavior. Application of SHARP to eleven frontier LLMs, evaluated on a fixed corpus of n=901 socially sensitive prompts, reveals that models with similar average risk can exhibit more than twofold differences in tail exposure and volatility. Across models, dimension-wise marginal tail behavior varies systematically across harm dimensions, with bias exhibiting the strongest tail severities, epistemic and fairness risks occupying intermediate regimes, and ethical misalignment consistently lower; together, these patterns reveal heterogeneous, model-dependent failure structures that scalar benchmarks conflate. These findings indicate that responsible evaluation and governance of LLMs require moving beyond scalar averages toward multidimensional, tail-sensitive risk profiling.

Method: Developed MoCo library with 26 model collaboration methods spanning routing, text, logit, and parameter-level exchanges. Integrated 25 evaluation datasets across reasoning, QA, code, safety domains. Conducted extensive experiments comparing collaboration strategies against single-model baselines.

Result: Most collaboration strategies outperform models without collaboration in 61.0% of (model, data) settings on average, with the most effective methods outperforming by up to 25.8%. The library enables analysis of scaling properties, training/inference efficiency, and problem-solving capabilities where single LMs struggle.

Conclusion: MoCo provides a valuable toolkit for advancing model collaboration research, demonstrating that collaborative systems can significantly outperform single models and paving the way for more open, modular, decentralized AI systems.

Abstract: Advancing beyond single monolithic language models (LMs), recent research increasingly recognizes the importance of model collaboration, where multiple LMs collaborate, compose, and complement each other. Existing research on this topic has mostly been disparate and disconnected, from different research communities, and lacks rigorous comparison. To consolidate existing research and establish model collaboration as a school of thought, we present MoCo: a one-stop Python library of executing, benchmarking, and comparing model collaboration algorithms at scale. MoCo features 26 model collaboration methods, spanning diverse levels of cross-model information exchange such as routing, text, logit, and model parameters. MoCo integrates 25 evaluation datasets spanning reasoning, QA, code, safety, and more, while users could flexibly bring their own data. Extensive experiments with MoCo demonstrate that most collaboration strategies outperform models without collaboration in 61.0% of (model, data) settings on average, with the most effective methods outperforming by up to 25.8%. We further analyze the scaling of model collaboration strategies, the training/inference efficiency of diverse methods, highlight that the collaborative system solves problems where single LMs struggle, and discuss future work in model collaboration, all made possible by MoCo. We envision MoCo as a valuable toolkit to facilitate and turbocharge the quest for an open, modular, decentralized, and collaborative AI future.

Method: Auto-regressive generation approach with iterative margin loss during contrastive training to learn compact, well-structured representations using only dozens of visual tokens instead of thousands.

Result: Achieves 30-155x reduction in token count while maintaining highly competitive performance across various backbones and benchmarks, with improved training efficiency and test-time scalability.

Conclusion: CausalEmbed introduces a flexible test-time scaling strategy for multi-vector VDR representations and demonstrates the potential of generative paradigms in multimodal document retrieval.

Abstract: Although Multimodal Large Language Models (MLLMs) have shown remarkable potential in Visual Document Retrieval (VDR) through generating high-quality multi-vector embeddings, the substantial storage overhead caused by representing a page with thousands of visual tokens limits their practicality in real-world applications. To address this challenge, we propose an auto-regressive generation approach, CausalEmbed, for constructing multi-vector embeddings. By incorporating iterative margin loss during contrastive training, CausalEmbed encourages the embedding models to learn compact and well-structured representations. Our method enables efficient VDR tasks using only dozens of visual tokens, achieving a 30-155x reduction in token count while maintaining highly competitive performance across various backbones and benchmarks. Theoretical analysis and empirical results demonstrate the unique advantages of auto-regressive embedding generation in terms of training efficiency and scalability at test time. As a result, CausalEmbed introduces a flexible test-time scaling strategy for multi-vector VDR representations and sheds light on the generative paradigm within multimodal document retrieval.

Method: Introduces a pretraining method that streams documents and uses RL to improve the next K generated tokens at each step. A strong post-trained model judges candidate generations (including model rollouts, original suffix, and rewritten suffix) for quality, safety, and factuality. Early training relies on original/rewritten suffixes; later training rewards high-quality rollouts.

Result: The method achieves 36.2% and 18.5% relative improvements over standard pretraining in factuality and safety, and up to 86.3% win rate improvements in overall generation quality.

Conclusion: The approach successfully builds higher quality, safer, and more factual models from the ground up by addressing safety and factuality issues during pretraining rather than relying solely on post-training alignment.

Abstract: Ensuring safety, factuality and overall quality in the generations of large language models is a critical challenge, especially as these models are increasingly deployed in real-world applications. The prevailing approach to addressing these issues involves collecting expensive, carefully curated datasets and applying multiple stages of fine-tuning and alignment. However, even this complex pipeline cannot guarantee the correction of patterns learned during pretraining. Therefore, addressing these issues during pretraining is crucial, as it shapes a model’s core behaviors and prevents unsafe or hallucinated outputs from becoming deeply embedded. To tackle this issue, we introduce a new pretraining method that streams documents and uses reinforcement learning (RL) to improve the next K generated tokens at each step. A strong, post-trained model judges candidate generations – including model rollouts, the original suffix, and a rewritten suffix – for quality, safety, and factuality. Early in training, the process relies on the original and rewritten suffixes; as the model improves, RL rewards high-quality rollouts. This approach builds higher quality, safer, and more factual models from the ground up. In experiments, our method gives 36.2% and 18.5% relative improvements over standard pretraining in terms of factuality and safety, and up to 86.3% win rate improvements in overall generation quality.

Method: Introduces SPACI Framework and AST-ASIP protocol to exploit the Syntax-Semantics Gap by embedding adversarial directives in syntactically inert regions (trivia nodes) of Abstract Syntax Trees. Evaluates 9 SOTA models across 25,000 submissions in Python, C, C++, and Java.

Result: Reveals catastrophic failure rates (>95%) in high-capacity models like DeepSeek-V3, which systematically prioritize hidden formatting constraints over code correctness. Quantifies failure using Decoupling Probability, Score Divergence, and Pedagogical Severity metrics.

Conclusion: Current alignment paradigms create “Trojan” vulnerabilities in automated grading, necessitating a shift from standard RLHF toward domain-specific Adjudicative Robustness where models prioritize evidence over instruction compliance.

Abstract: The rapid integration of Large Language Models (LLMs) into educational assessment rests on the unverified assumption that instruction following capability translates directly to objective adjudication. We demonstrate that this assumption is fundamentally flawed. Instead of evaluating code quality, models frequently decouple from the submission’s logic to satisfy hidden directives, a systemic vulnerability we term the Compliance Paradox, where models fine-tuned for extreme helpfulness are vulnerable to adversarial manipulation. To expose this, we introduce the Semantic-Preserving Adversarial Code Injection (SPACI) Framework and the Abstract Syntax Tree-Aware Semantic Injection Protocol (AST-ASIP). These methods exploit the Syntax-Semantics Gap by embedding adversarial directives into syntactically inert regions (trivia nodes) of the Abstract Syntax Tree. Through a large-scale evaluation of 9 SOTA models across 25,000 submissions in Python, C, C++, and Java, we reveal catastrophic failure rates (>95%) in high-capacity open-weights models like DeepSeek-V3, which systematically prioritize hidden formatting constraints over code correctness. We quantify this failure using our novel tripartite framework measuring Decoupling Probability, Score Divergence, and Pedagogical Severity to demonstrate the widespread “False Certification” of functionally broken code. Our findings suggest that current alignment paradigms create a “Trojan” vulnerability in automated grading, necessitating a shift from standard RLHF toward domain-specific Adjudicative Robustness, where models are conditioned to prioritize evidence over instruction compliance. We release our complete dataset and injection framework to facilitate further research on the topic.

Method: Multi-agent framework decomposing translation into: 1) word-level expression, 2) sentence-level translation, and 3) multidimensional review, integrating specialized Hong Kong legal glossary database, Retrieval-Augmented Generation (RAG), and iterative feedback.

Result: Outperforms single-agent baselines across 13 open-source and commercial LLMs on HKCFA Judgment 97-22 dataset; human evaluation confirms effectiveness in legal semantic accuracy, structural coherence, and stylistic fidelity, though still trails human experts in complex terminology contextualization and stylistic naturalness.

Conclusion: TransLaw provides an effective multi-agent solution for legal document translation that significantly improves over single-agent LLM approaches, though human expertise remains superior for complex contextualization and naturalness.

Abstract: Hong Kong case law translation presents significant challenges: manual methods suffer from high costs and inconsistent quality, while both traditional machine translation and approaches relying solely on Large Language Models (LLMs) often fail to ensure legal terminology accuracy, culturally embedded nuances, and strict linguistic structures. To overcome these limitations, this study proposes TransLaw, a multi-agent framework that decomposes translation into word-level expression, sentence-level translation, and multidimensional review, integrating a specialized Hong Kong legal glossary database, Retrieval-Augmented Generation (RAG), and iterative feedback. Experiments on our newly constructed HKCFA Judgment 97-22 dataset, benchmarking 13 open-source and commercial LLMs, demonstrate that TransLaw significantly outperforms single-agent baselines across all evaluated models. Human evaluation confirms the framework’s effectiveness in terms of legal semantic accuracy, structural coherence, and stylistic fidelity, while noting that it still trails human experts in contextualizing complex terminology and stylistic naturalness.

Method: Proposes two approaches: one-shot ranking and incremental ranking. Introduces a new evaluation framework inspired by information retrieval metrics and constructs a unified benchmark by aggregating existing fact verification datasets.

Result: Incremental ranking strategies better capture complementary evidence, LLM-based methods outperform shallower baselines, and evidence ranking reduces reading effort and improves verification compared to evidence selection.

Conclusion: Evidence ranking provides a foundational step toward more interpretable, efficient, and user-aligned information verification systems.

Abstract: Attribution and fact verification are critical challenges in natural language processing for assessing information reliability. While automated systems and Large Language Models (LLMs) aim to retrieve and select concise evidence to support or refute claims, they often present users with either insufficient or overly redundant information, leading to inefficient and error-prone verification. To address this, we propose Evidence Ranking, a novel task that prioritizes presenting sufficient information as early as possible in a ranked list. This minimizes user reading effort while still making all available evidence accessible for sequential verification. We compare two approaches for the new ranking task: one-shot ranking and incremental ranking. We introduce a new evaluation framework, inspired by information retrieval metrics, and construct a unified benchmark by aggregating existing fact verification datasets. Extensive experiments with diverse models show that incremental ranking strategies better capture complementary evidence and that LLM-based methods outperform shallower baselines, while still facing challenges in balancing sufficiency and redundancy. Compared to evidence selection, we conduct a controlled user study and demonstrate that evidence ranking both reduces reading effort and improves verification. This work provides a foundational step toward more interpretable, efficient, and user-aligned information verification systems.

Method: Multi-agent self-play where agents share same policy and engage in structured conversations (propose, critique, revise). Critiques that help improve solutions earn diagnostic rewards, enabling joint optimization of generation and judging through self-play.

Result: Experiments on five benchmarks show CoNL achieves consistent improvements over self-rewarding baselines while maintaining stable training.

Conclusion: CoNL provides a framework for training LLMs on non-verifiable tasks through unified generation, evaluation, and meta-evaluation without external judges or ground truth.

Abstract: Training large language models (LLMs) for non-verifiable tasks, such as creative writing, dialogue, and ethical reasoning, remains challenging due to the absence of ground-truth labels. While LLM-as-Judge approaches offer a scalable alternative to human feedback, they face a fundamental limitation: performance is constrained by the evaluator’s own quality. If the judge cannot recognize good solutions, it cannot provide useful training signals, and evaluation biases (e.g., favoring verbosity over quality) remain unaddressed. This motivates meta-evaluation: the ability to evaluate and improve the evaluator itself. We introduce CoNL, a framework that unifies generation, evaluation, and meta-evaluation through multi-agent self-play. Our key insight: critique quality can be measured by whether it helps others improve their solutions. In CoNL, multiple agents sharing the same policy engage in structured conversations to propose, critique, and revise solutions. Critiques that enable solution improvements earn a diagnostic reward, creating explicit supervision for meta-evaluation and enabling joint optimization of generation and judging capabilities through self-play, without external judges or ground truth. Experiments on five benchmarks show that CoNL achieves consistent improvements over self-rewarding baselines while maintaining stable training.

Method: SOUP (Single-sample Mix-policy Unified Paradigm) unifies off- and on-policy learning at the token level. It uses historical policy prefixes for the beginning of sequences and on-policy continuations for the rest, with token-level importance ratios to leverage off-policy information while maintaining stability.

Result: Extensive experiments show SOUP consistently outperforms standard on-policy training and existing off-policy extensions. Analysis confirms that fine-grained, single-sample mix-policy training improves both exploration and final performance in LLM RL.

Conclusion: SOUP provides an effective framework for combining off-policy and on-policy learning in RL for language models, addressing exploration limitations while maintaining training stability through token-level policy mixing.

Abstract: On-policy reinforcement learning (RL) methods widely used for language model post-training, like Group Relative Policy Optimization (GRPO), often suffer from limited exploration and early saturation due to low sampling diversity. While off-policy data can help, current approaches that mix entire trajectories cause significant policy mismatch and instability. In this work, we propose the $\textbf{S}$ingle-sample Mix-p$\textbf{O}$licy $\textbf{U}$nified $\textbf{P}$aradigm (SOUP), a framework that unifies off- and on-policy learning within individual samples at the token level. It confines off-policy influence to the prefix of a generated sequence sampled from historical policies, while the continuation is generated on-policy. Through token-level importance ratios, SOUP effectively leverages off-policy information while preserving training stability. Extensive experiments demonstrate that SOUP consistently outperforms standard on-policy training and existing off-policy extensions. Our further analysis clarifies how our fine-grained, single-sample mix-policy training can improve both exploration and final performance in LLM RL.

Method: Created DimStance resource with 11,746 target aspects in 7,365 texts across five languages (English, German, Chinese, Nigerian Pidgin, Swahili) and two domains (politics, environmental protection). Formulated dimensional stance regression task and benchmarked pretrained and large language models under regression and prompting settings.

Result: Fine-tuned LLM regressors showed competitive performance, but there were persistent challenges in low-resource languages and limitations of token-based generation. Cross-lingual valence-arousal patterns were analyzed.

Conclusion: DimStance provides a foundation for multilingual, emotion-aware stance analysis and benchmarking, enabling more nuanced understanding of stance expressions through dimensional affective modeling.

Abstract: Stance detection is an established task that classifies an author’s attitude toward a specific target into categories such as Favor, Neutral, and Against. Beyond categorical stance labels, we leverage a long-established affective science framework to model stance along real-valued dimensions of valence (negative-positive) and arousal (calm-active). This dimensional approach captures nuanced affective states underlying stance expressions, enabling fine-grained stance analysis. To this end, we introduce DimStance, the first dimensional stance resource with valence-arousal (VA) annotations. This resource comprises 11,746 target aspects in 7,365 texts across five languages (English, German, Chinese, Nigerian Pidgin, and Swahili) and two domains (politics and environmental protection). To facilitate the evaluation of stance VA prediction, we formulate the dimensional stance regression task, analyze cross-lingual VA patterns, and benchmark pretrained and large language models under regression and prompting settings. Results show competitive performance of fine-tuned LLM regressors, persistent challenges in low-resource languages, and limitations of token-based generation. DimStance provides a foundation for multilingual, emotion-aware, stance analysis and benchmarking.

Method: Used a hybrid pipeline integrating direct text parsing, optical character recognition (OCR), and automated reconstruction to extract word-definition pairs from trusted reference works and educational sources.

Result: Created MURAD dataset with 96,243 word-definition pairs covering linguistics, Islamic studies, mathematics, physics, psychology, and engineering, with metadata identifying source domains.

Conclusion: The dataset supports computational linguistics research, reverse dictionary modeling, semantic retrieval, and educational tools, aiming to advance Arabic NLP and promote reproducible research on Arabic lexical semantics.

Abstract: Arabic is a linguistically and culturally rich language with a vast vocabulary that spans scientific, religious, and literary domains. Yet, large-scale lexical datasets linking Arabic words to precise definitions remain limited. We present MURAD (Multi-domain Unified Reverse Arabic Dictionary), an open lexical dataset with 96,243 word-definition pairs. The data come from trusted reference works and educational sources. Extraction used a hybrid pipeline integrating direct text parsing, optical character recognition, and automated reconstruction. This ensures accuracy and clarity. Each record aligns a target word with its standardized Arabic definition and metadata that identifies the source domain. The dataset covers terms from linguistics, Islamic studies, mathematics, physics, psychology, and engineering. It supports computational linguistics and lexicographic research. Applications include reverse dictionary modeling, semantic retrieval, and educational tools. By releasing this resource, we aim to advance Arabic natural language processing and promote reproducible research on Arabic lexical semantics.

Method: Introduces Landmark (LMK) pooling which partitions sequences into chunks, inserts landmark tokens between chunks, and forms final representation by mean-pooling the landmark token embeddings.

Result: LMK pooling matches existing methods on short-context retrieval tasks and yields substantial improvements on long-context tasks, improving long-context extrapolation without sacrificing local salient features.

Conclusion: LMK pooling is a practical and scalable alternative to existing pooling methods that addresses systematic weaknesses in current pooling strategies for sequence representation learning.

Abstract: Representation learning is central to many downstream tasks such as search, clustering, classification, and reranking. State-of-the-art sequence encoders typically collapse a variable-length token sequence to a single vector using a pooling operator, most commonly a special [CLS] token or mean pooling over token embeddings. In this paper, we identify systematic weaknesses of these pooling strategies: [CLS] tends to concentrate information toward the initial positions of the sequence and can under-represent distributed evidence, while mean pooling can dilute salient local signals, sometimes leading to worse short-context performance. To address these issues, we introduce Landmark (LMK) pooling, which partitions a sequence into chunks, inserts landmark tokens between chunks, and forms the final representation by mean-pooling the landmark token embeddings. This simple mechanism improves long-context extrapolation without sacrificing local salient features, at the cost of introducing a small number of special tokens. We empirically demonstrate that LMK pooling matches existing methods on short-context retrieval tasks and yields substantial improvements on long-context tasks, making it a practical and scalable alternative to existing pooling methods.

Method: Three-stage training procedure: 1) Align task-specific model’s output embedding space with LLM’s input embedding space, 2) Use Mixup in the aligned embedding space with controllable mixing ratios, 3) Reconstruct mixed embeddings into human-interpretable sentences via LLM inversion. Also addresses manifold intrusion in text Mixup with mitigation strategy.

Result: Extensive experiments show effectiveness in both few-shot and fully supervised scenarios. The approach improves augmentation performance while providing interpretable outputs and controllable mixing ratios.

Conclusion: inversedMixup successfully unifies Mixup’s controllability with LLM-based interpretability, enabling human-readable text augmentation with controlled mixing. Provides first empirical evidence of manifold intrusion in text Mixup and effective mitigation.

Abstract: Mixup generates augmented samples by linearly interpolating inputs and labels with a controllable ratio. However, since it operates in the latent embedding level, the resulting samples are not human-interpretable. In contrast, LLM-based augmentation methods produce sentences via prompts at the token level, yielding readable outputs but offering limited control over the generation process. Inspired by recent advances in LLM inversion, which reconstructs natural language from embeddings and helps bridge the gap between latent embedding space and discrete token space, we propose inversedMixup, a unified framework that combines the controllability of Mixup with the interpretability of LLM-based generation. Specifically, inversedMixup adopts a three-stage training procedure to align the output embedding space of a task-specific model with the input embedding space of an LLM. Upon successful alignment, inversedMixup can reconstruct mixed embeddings with a controllable mixing ratio into human-interpretable augmented sentences, thereby improving the augmentation performance. Additionally, inversedMixup provides the first empirical evidence of the manifold intrusion phenomenon in text Mixup and introduces a simple yet effective strategy to mitigate it. Extensive experiments demonstrate the effectiveness and generalizability of our approach in both few-shot and fully supervised scenarios.

Method: Note2Chat converts real-world medical notes into doctor-patient dialogues using decision tree-guided generation and refinement. Uses three-stage fine-tuning: supervised learning, simulated data augmentation, and preference learning. Introduces a novel single-turn reasoning paradigm that reframes history taking as sequence of single-turn reasoning problems.

Result: Substantially improves clinical reasoning with gains of +16.9 F1 and +21.0 Top-1 diagnostic accuracy over GPT-4o.

Conclusion: The framework effectively addresses the gap in dynamic clinical reasoning by leveraging widely available medical notes, offering improved interpretability, local supervision, dynamic adaptation, and sample efficiency.

Abstract: Effective clinical history taking is a foundational yet underexplored component of clinical reasoning. While large language models (LLMs) have shown promise on static benchmarks, they often fall short in dynamic, multi-turn diagnostic settings that require iterative questioning and hypothesis refinement. To address this gap, we propose \method{}, a note-driven framework that trains LLMs to conduct structured history taking and diagnosis by learning from widely available medical notes. Instead of relying on scarce and sensitive dialogue data, we convert real-world medical notes into high-quality doctor-patient dialogues using a decision tree-guided generation and refinement pipeline. We then propose a three-stage fine-tuning strategy combining supervised learning, simulated data augmentation, and preference learning. Furthermore, we propose a novel single-turn reasoning paradigm that reframes history taking as a sequence of single-turn reasoning problems. This design enhances interpretability and enables local supervision, dynamic adaptation, and greater sample efficiency. Experimental results show that our method substantially improves clinical reasoning, achieving gains of +16.9 F1 and +21.0 Top-1 diagnostic accuracy over GPT-4o. Our code and dataset can be found at https://github.com/zhentingsheng/Note2Chat.

Method: ASTRA integrates two components: 1) a pipeline using static tool-call graph topology to synthesize diverse trajectories, and 2) an environment synthesis framework converting decomposed QA traces into code-executable, rule-verifiable environments for deterministic multi-turn RL. Combines SFT with online RL using trajectory-level rewards.

Result: ASTRA-trained models achieve state-of-the-art performance on multiple agentic tool-use benchmarks at comparable scales, approaching closed-source systems while preserving core reasoning ability.

Conclusion: ASTRA provides a fully automated end-to-end framework for training robust tool-augmented language model agents through scalable data synthesis and verifiable reinforcement learning.

Abstract: Large language models (LLMs) are increasingly used as tool-augmented agents for multi-step decision making, yet training robust tool-using agents remains challenging. Existing methods still require manual intervention, depend on non-verifiable simulated environments, rely exclusively on either supervised fine-tuning (SFT) or reinforcement learning (RL), and struggle with stable long-horizon, multi-turn learning. To address these challenges, we introduce ASTRA, a fully automated end-to-end framework for training tool-augmented language model agents via scalable data synthesis and verifiable reinforcement learning. ASTRA integrates two complementary components. First, a pipeline that leverages the static topology of tool-call graphs synthesizes diverse, structurally grounded trajectories, instilling broad and transferable tool-use competence. Second, an environment synthesis framework that captures the rich, compositional topology of human semantic reasoning converts decomposed question-answer traces into independent, code-executable, and rule-verifiable environments, enabling deterministic multi-turn RL. Based on this method, we develop a unified training methodology that integrates SFT with online RL using trajectory-level rewards to balance task completion and interaction efficiency. Experiments on multiple agentic tool-use benchmarks demonstrate that ASTRA-trained models achieve state-of-the-art performance at comparable scales, approaching closed-source systems while preserving core reasoning ability. We release the full pipelines, environments, and trained models at https://github.com/LianjiaTech/astra.

Method: KromHC parametrizes residual matrices using Kronecker products of smaller doubly stochastic matrices. It enforces manifold constraints across factor residual matrices along each mode of the tensorized residual stream, guaranteeing exact double stochasticity while reducing parameter complexity to O(n²C).

Result: Comprehensive experiments show KromHC matches or outperforms state-of-the-art mHC variants while requiring significantly fewer trainable parameters.

Conclusion: KromHC provides an efficient solution to hyper-connection challenges by guaranteeing exact double stochasticity with reduced parameter complexity, making hyper-connections more practical for neural network architectures.

Abstract: The success of Hyper-Connections (HC) in neural networks (NN) has also highlighted issues related to its training instability and restricted scalability. The Manifold-Constrained Hyper-Connections (mHC) mitigate these challenges by projecting the residual connection space onto a Birkhoff polytope, however, it faces two issues: 1) its iterative Sinkhorn-Knopp (SK) algorithm does not always yield exact doubly stochastic residual matrices; 2) mHC incurs a prohibitive $\mathcal{O}(n^3C)$ parameter complexity with $n$ as the width of the residual stream and $C$ as the feature dimension. The recently proposed mHC-lite reparametrizes the residual matrix via the Birkhoff-von-Neumann theorem to guarantee double stochasticity, but also faces a factorial explosion in its parameter complexity, $\mathcal{O} \left( nC \cdot n! \right)$. To address both challenges, we propose \textbf{KromHC}, which uses the \underline{Kro}necker products of smaller doubly stochastic matrices to parametrize the residual matrix in \underline{mHC}. By enforcing manifold constraints across the factor residual matrices along each mode of the tensorized residual stream, KromHC guarantees exact double stochasticity of the residual matrices while reducing parameter complexity to $\mathcal{O}(n^2C)$. Comprehensive experiments demonstrate that KromHC matches or even outperforms state-of-the-art (SOTA) mHC variants, while requiring significantly fewer trainable parameters. The code is available at \texttt{https://github.com/wz1119/KromHC}.

Method: Use language models to simulate CLI by manipulating L1 language dominance and L2 proficiency (controlled via L2 age of exposure). Investigate impact of pretraining on L1 languages with varying syntactic distance from L2. Analyze cross-linguistic priming to study how activating L1 structures impacts L2 processing.

Result: Results align with psycholinguistic studies: language dominance and proficiency are strong predictors of CLI. Grammatical structure priming is bidirectional, but ungrammatical structure priming is sensitive to language dominance. Provides mechanistic evidence that L1 is co-activated during L2 processing and directly influences neural circuitry for L2.

Conclusion: Language models can serve as a computational framework to inform theories of human crosslinguistic influence, offering controlled experimentation to isolate underlying drivers of bilingual language processing.

Abstract: Despite the centrality of crosslinguistic influence (CLI) to bilingualism research, human studies often yield conflicting results due to inherent experimental variance. We address these inconsistencies by using language models (LMs) as controlled statistical learners to systematically simulate CLI and isolate its underlying drivers. Specifically, we study the effect of varying the L1 language dominance and the L2 language proficiency, which we manipulate by controlling the L2 age of exposure – defined as the training step at which the L2 is introduced. Furthermore, we investigate the impact of pretraining on L1 languages with varying syntactic distance from the L2. Using cross-linguistic priming, we analyze how activating L1 structures impacts L2 processing. Our results align with evidence from psycholinguistic studies, confirming that language dominance and proficiency are strong predictors of CLI. We further find that while priming of grammatical structures is bidirectional, the priming of ungrammatical structures is sensitive to language dominance. Finally, we provide mechanistic evidence of CLI in LMs, demonstrating that the L1 is co-activated during L2 processing and directly influences the neural circuitry recruited for the L2. More broadly, our work demonstrates that LMs can serve as a computational framework to inform theories of human CLI.

Method: ILRR guides generation by dynamically aligning the internal activations of generated sequences with those of a reference throughout denoising. It introduces Spatially Modulated Steering for handling long texts with shorter references by regulating guidance intensity across the sequence.

Result: ILRR achieves effective attribute steering on LLaDA and MDLM architectures with minimal computational overhead (one additional parallel forward pass per step). It improves attribute accuracy by 10-60% points over comparable baselines while maintaining high generation quality.

Conclusion: ILRR provides an effective, learning-free framework for controlling discrete diffusion language models using reference sequences, enabling flexible attribute steering with minimal computational cost.

Abstract: Discrete Diffusion Language Models (DLMs) offer a promising non-autoregressive alternative for text generation, yet effective mechanisms for inference-time control remain relatively underexplored. Existing approaches include sampling-level guidance procedures or trajectory optimization mechanisms. In this work, we introduce Iterative Latent Representation Refinement (ILRR), a learning-free framework for steering DLMs using a single reference sequence. ILRR guides generation by dynamically aligning the internal activations of the generated sequence with those of a given reference throughout the denoising process. This approach captures and transfers high-level semantic properties, with a tunable steering scale enabling flexible control over attributes such as sentiment. We further introduce Spatially Modulated Steering, an extension that enables steering long texts using shorter references by regulating guidance intensity across the sequence. Empirically, we demonstrate that ILRR achieves effective attribute steering on LLaDA and MDLM architectures with a minor computational overhead, requiring only one additional parallel forward pass per denoising step. Under the same compute budget, ILRR improves attribute accuracy over comparable baselines by 10$%$ to 60$%$ points, while maintaining high generation quality.

Method: Proposes a post-training adaptation algorithm that identifies and replaces low-utility tokens with more relevant ones based on frequency in an adaptation corpus, optimizing token inventory for target vocabulary size.

Result: Adapted tokenizers compress test corpora more effectively than baselines with same vocabulary size across multiple languages and tasks (generation and classification).

Conclusion: The method provides lightweight vocabulary fine-tuning for domain/task-specific optimization, improving tokenization efficiency without retraining the entire model.

Abstract: Subword tokenization methods, such as Byte-Pair Encoding (BPE), significantly impact the performance and efficiency of large language models (LLMs). The standard approach involves training a general-purpose tokenizer that uniformly processes all textual data during both training and inference. However, the use of a generic set of tokens can incur inefficiencies when applying the model to specific domains or languages. To address this limitation, we propose a post-training adaptation strategy that selectively replaces low-utility tokens with more relevant ones based on their frequency in an adaptation corpus. Our algorithm identifies the token inventory that most effectively encodes the adaptation corpus for a given target vocabulary size. Extensive experiments on generation and classification tasks across multiple languages demonstrate that our adapted tokenizers compress test corpora more effectively than baselines using the same vocabulary size. This method serves as a lightweight adaptation mechanism, akin to a vocabulary fine-tuning process, enabling optimized tokenization for specific domains or tasks. Our code and data are available at https://github.com/vijini/Adapt-BPE.git.

Method: Treats semantic progression as stochastic trajectories in high-dimensional state space, uses Allan deviation (from precision metrology) to analyze stability of meaning by treating ordered sentence embeddings as displacement signals.

Result: Reveals two dynamical regimes: short-time power-law scaling differentiating creative literature from technical texts, and long-time crossover to stability-limited noise floor. LLMs mimic local scaling statistics but show systematic reduction in stability horizon.

Conclusion: Establishes semantic coherence as measurable physical property, offers framework to differentiate nuanced dynamics of human cognition from algorithmic models, with implications for AI text detection and understanding cognitive processes.

Abstract: While language progresses through a sequence of semantic states, the underlying dynamics of this progression remain elusive. Here, we treat the semantic progression of written text as a stochastic trajectory in a high-dimensional state space. We utilize Allan deviation, a tool from precision metrology, to analyze the stability of meaning by treating ordered sentence embeddings as a displacement signal. Our analysis reveals two distinct dynamical regimes: short-time power-law scaling, which differentiates creative literature from technical texts, and a long-time crossover to a stability-limited noise floor. We find that while large language models successfully mimic the local scaling statistics of human text, they exhibit a systematic reduction in their stability horizon. These results establish semantic coherence as a measurable physical property, offering a framework to differentiate the nuanced dynamics of human cognition from the patterns generated by algorithmic models.

Method: FIT uses three key components: rigorous data filtering to identify target knowledge, importance-aware updates to preserve non-target knowledge, and targeted layer attribution to focus unlearning on specific model layers. The framework also introduces the PCH benchmark covering personal information, copyright, and harmful content.

Result: Extensive experiments on four open-source LLMs with hundreds of deletion requests show FIT achieves the strongest trade-off between forgetting quality and utility preservation, outperforms existing methods on MMLU, CommonsenseQA, and GSM8K benchmarks, and remains resistant to relearning and quantization recovery attacks.

Conclusion: FIT provides an effective solution for continual unlearning in LLMs, addressing the practical challenge of handling sequential deletion requests while maintaining model utility and preventing knowledge recovery.

Abstract: Large language models (LLMs) demonstrate impressive capabilities across diverse tasks but raise concerns about privacy, copyright, and harmful materials. Existing LLM unlearning methods rarely consider the continual and high-volume nature of real-world deletion requests, which can cause utility degradation and catastrophic forgetting as requests accumulate. To address this challenge, we introduce \fit, a framework for continual unlearning that handles large numbers of deletion requests while maintaining robustness against both catastrophic forgetting and post-unlearning recovery. \fit mitigates degradation through rigorous data \underline{F}iltering, \underline{I}mportance-aware updates, and \underline{T}argeted layer attribution, enabling stable performance across long sequences of unlearning operations and achieving a favorable balance between forgetting effectiveness and utility retention. To support realistic evaluation, we present \textbf{PCH}, a benchmark covering \textbf{P}ersonal information, \textbf{C}opyright, and \textbf{H}armful content in sequential deletion scenarios, along with two symmetric metrics, Forget Degree (F.D.) and Retain Utility (R.U.), which jointly assess forgetting quality and utility preservation. Extensive experiments on four open-source LLMs with hundreds of deletion requests show that \fit achieves the strongest trade-off between F.D. and R.U., surpasses existing methods on MMLU, CommonsenseQA, and GSM8K, and remains resistant against both relearning and quantization recovery attacks.

Method: RSE actively distills raw reasoning trajectories into a shared experience bank, enabling positive recycling of intermediate conclusions to shortcut redundant derivations and negative recycling of failure patterns to prune dead ends, all without additional training.

Result: RSE consistently outperforms strong baselines on HMMT24, HMMT25, IMO-Bench, and HLE benchmarks with comparable computational cost, achieving state-of-the-art scaling efficiency.

Conclusion: RSE transforms test-time search from isolated trials into a cumulative process, significantly improving reasoning efficiency by recycling search experience without requiring model retraining.

Abstract: Test-Time Scaling enhances the reasoning capabilities of Large Language Models by allocating additional inference compute to broaden the exploration of the solution space. However, existing search strategies typically treat rollouts as disposable samples, where valuable intermediate insights are effectively discarded after each trial. This systemic memorylessness leads to massive computational redundancy, as models repeatedly re-derive discovered conclusions and revisit known dead ends across extensive attempts. To bridge this gap, we propose \textbf{Recycling Search Experience (RSE)}, a self-guided, training-free strategy that turns test-time search from a series of isolated trials into a cumulative process. By actively distilling raw trajectories into a shared experience bank, RSE enables positive recycling of intermediate conclusions to shortcut redundant derivations and negative recycling of failure patterns to prune encountered dead ends. Theoretically, we provide an analysis that formalizes the efficiency gains of RSE, validating its advantage over independent sampling in solving complex reasoning tasks. Empirically, extensive experiments on HMMT24, HMMT25, IMO-Bench, and HLE show that RSE consistently outperforms strong baselines with comparable computational cost, achieving state-of-the-art scaling efficiency.

Method: DAVID-GRPO introduces a budget-efficient RL framework that: (1) stabilizes early learning with minimal supervision, (2) assigns retrieval credit based on evidence recall, and (3) improves exploration by resampling truncated near-miss trajectories.

Result: Evaluated on agents up to 1.5B parameters trained on only four RTX 3090 GPUs, DAVID-GRPO consistently outperforms prior RL methods designed for large-scale settings on six multi-hop QA benchmarks.

Conclusion: With the right inductive biases, small agents can achieve low training cost with high accuracy, challenging the trade-off between resource constraints and reasoning performance.

Abstract: While reinforcement learning (RL) has empowered multi-turn reasoning agents with retrieval and tools, existing successes largely depend on extensive on-policy rollouts in high-cost, high-accuracy regimes. Under realistic resource constraints that cannot support large models or dense explorations, however, small language model agents fall into a low-cost, low-accuracy regime, where limited rollout budgets lead to sparse exploration, sparse credit assignment, and unstable training. In this work, we challenge this trade-off and show that small language models can achieve strong multi-hop reasoning under resource constraints. We introduce DAVID-GRPO, a budget-efficient RL framework that (i) stabilizes early learning with minimal supervision, (ii) assigns retrieval credit based on evidence recall, and (iii) improves exploration by resampling truncated near-miss trajectories. Evaluated on agents up to 1.5B parameters trained on only four RTX 3090 GPUs, DAVID-GRPO consistently outperforms prior RL methods designed for large-scale settings on six multi-hop QA benchmarks. These results show that with the right inductive biases, small agents can achieve low training cost with high accuracy.

Method: TAPPA analyzes attention patterns from a temporally continuous perspective, characterizing them as predictable (with clear regularities) or unpredictable (effectively random). The distinction is explained by query self-similarity along the temporal dimension. The framework provides mathematical analysis of three representative cases through the joint effect of queries, keys, and RoPE.

Result: TAPPA’s insights were validated on KV cache compression and LLM pruning tasks. A simple metric motivated by TAPPA consistently improved performance over baseline methods in these tasks.

Conclusion: TAPPA provides a unifying framework for understanding attention patterns in LLMs, offering both theoretical insights and practical applications for inference acceleration through KV cache compression and model pruning.

Abstract: Attention patterns play a crucial role in both training and inference of large language models (LLMs). Prior works have identified individual patterns such as retrieval heads, sink heads, and diagonal traces, yet these observations remain fragmented and lack a unifying explanation. To bridge this gap, we introduce \textbf{Temporal Attention Pattern Predictability Analysis (TAPPA), a unifying framework that explains diverse attention patterns by analyzing their underlying mathematical formulations} from a temporally continuous perspective. TAPPA both deepens the understanding of attention behavior and guides inference acceleration approaches. Specifically, TAPPA characterizes attention patterns as predictable patterns with clear regularities and unpredictable patterns that appear effectively random. Our analysis further reveals that this distinction can be explained by the degree of query self-similarity along the temporal dimension. Focusing on the predictable patterns, we further provide a detailed mathematical analysis of three representative cases through the joint effect of queries, keys, and Rotary Positional Embeddings (RoPE). We validate TAPPA by applying its insights to KV cache compression and LLM pruning tasks. Across these tasks, a simple metric motivated by TAPPA consistently improves performance over baseline methods. The code is available at https://github.com/MIRALab-USTC/LLM-TAPPA.

Method: TACLer uses model-tailored curriculum RL with two components: 1) tailored curriculum learning that progressively increases data complexity based on model proficiency, and 2) hybrid Thinking/NoThinking reasoning that balances accuracy and efficiency by selectively enabling reasoning modes.

Result: TACLer reduces training compute by over 50% compared to long thinking models, cuts inference token usage by over 42%, and improves accuracy by over 9% on base models, outperforming state-of-the-art baselines across four math datasets.

Conclusion: The proposed curriculum RL framework effectively improves both learning efficiency and reasoning performance for LLMs, offering a practical solution to the computational challenges of long chain-of-thought reasoning.

Abstract: Large Language Models (LLMs) have shown remarkable performance on complex reasoning tasks, especially when equipped with long chain-of-thought (CoT) reasoning. However, eliciting long CoT typically requires large-scale reinforcement learning (RL) training, while often leading to overthinking with redundant intermediate steps. To improve learning and reasoning efficiency, while preserving or even enhancing performance, we propose TACLer, a model-tailored curriculum reinforcement learning framework that gradually increases the complexity of the data based on the model’s proficiency in multi-stage RL training. TACLer features two core components: (i) tailored curriculum learning that determines what knowledge the model lacks and needs to learn in progressive stages; (ii) a hybrid Thinking/NoThinking reasoning paradigm that balances accuracy and efficiency by enabling or disabling the Thinking mode. Our experiments show that TACLer yields a twofold advantage in learning and reasoning: (i) it reduces computational cost, cutting training compute by over 50% compared to long thinking models and reducing inference token usage by over 42% relative to the base model; and (ii) it improves accuracy by over 9% on the base model, consistently outperforming state-of-the-art Nothinking and Thinking baselines across four math datasets with complex problems.

Method: Proposes a parameter-efficient framework that structures LLM latent spaces by combining contrastive learning with an ordinal ranking objective to capture graded distinctions between concrete actions and ambiguous claims. Incorporates gated feature modulation to filter disclosure noise and utilizes MetaGradNorm to stabilize multi-objective optimization.

Result: Experiments in cross-category settings demonstrate superior robustness over standard baselines while revealing a trade-off between representational rigidity and generalization.

Conclusion: The framework effectively addresses greenwashing detection in sustainability reports by creating more robust representations that can distinguish between concrete actions and vague claims, though there’s a trade-off between rigidity and generalization.

Abstract: Sustainability reports are critical for ESG assessment, yet greenwashing and vague claims often undermine their reliability. Existing NLP models lack robustness to these practices, typically relying on surface-level patterns that generalize poorly. We propose a parameter-efficient framework that structures LLM latent spaces by combining contrastive learning with an ordinal ranking objective to capture graded distinctions between concrete actions and ambiguous claims. Our approach incorporates gated feature modulation to filter disclosure noise and utilizes MetaGradNorm to stabilize multi-objective optimization. Experiments in cross-category settings demonstrate superior robustness over standard baselines while revealing a trade-off between representational rigidity and generalization.

Method: Front-load procedural data (0.1% of training) from formal languages/algorithms (e.g., Dyck sequences for balanced brackets) before standard pretraining on natural language, code, and math datasets. Analyze algorithmic skills, scale to 1.3B models, and study attention/MLP mechanisms.

Result: Procedural pretraining significantly improves performance: Needle-in-a-haystack accuracy jumps from 10% to 98% with Dyck sequences. Models reach same loss with only 55-86% of original data. Attention layers benefit structured domains (code), MLP layers benefit language.

Conclusion: Procedural pretraining is a simple, lightweight method to improve LLM performance and accelerate training, suggesting promise in disentangling knowledge acquisition from reasoning, similar to human learning progression.

Abstract: Pretraining directly on web-scale corpora is the de facto paradigm for building language models. We study an alternative setting where the model is initially exposed to abstract structured data, as a means to ease the subsequent acquisition of rich semantic knowledge, much like humans learn simple logic and mathematics before higher reasoning. We specifically focus on procedural data, generated by formal languages and other simple algorithms, as such abstract data. We first diagnose the algorithmic skills that different forms of procedural data can improve, often significantly. For example, on context recall (Needle-in-a-haystack), the accuracy jumps from 10 to 98% when pretraining on Dyck sequences (balanced brackets). Second, we study how these gains are reflected in pretraining larger models (up to 1.3B). We find that front-loading as little as 0.1% procedural data significantly outperforms standard pretraining on natural language, code, and informal mathematics (C4, CodeParrot, and DeepMind-Math datasets). Notably, this procedural pretraining enables the models to reach the same loss value with only 55, 67, 86% of the original data. Third, we explore the mechanisms behind and find that procedural pretraining instils non-trivial structure in both attention and MLP layers. The former is particularly important for structured domains (e.g. code), and the latter for language. Finally, we lay a path for combining multiple forms of procedural data. Our results show that procedural pretraining is a simple, lightweight means to improving performance and accelerating language model pretraining, ultimately suggesting the promise of disentangling knowledge acquisition from reasoning in LLMs.

Method: Proposes Central Entity-Guided Graph Optimization for Community Detection (CE-GOCD): (1) uses paper titles as central entities for targeted subgraph retrieval, (2) enhances semantic discovery via subgraph pruning/completion, (3) applies community detection to group papers with shared themes.

Result: Evaluated on three NLP literature QA datasets; demonstrates superiority over other retrieval-augmented baselines, confirming framework effectiveness.

Conclusion: CE-GOCD effectively models semantic substructures in academic knowledge graphs to improve LLM comprehension of scientific literature for QA tasks.

Abstract: Large Language Models (LLMs) are increasingly used for question answering over scientific research papers. Existing retrieval augmentation methods often rely on isolated text chunks or concepts, but overlook deeper semantic connections between papers. This impairs the LLM’s comprehension of scientific literature, hindering the comprehensiveness and specificity of its responses. To address this, we propose Central Entity-Guided Graph Optimization for Community Detection (CE-GOCD), a method that augments LLMs’ scientific question answering by explicitly modeling and leveraging semantic substructures within academic knowledge graphs. Our approach operates by: (1) leveraging paper titles as central entities for targeted subgraph retrieval, (2) enhancing implicit semantic discovery via subgraph pruning and completion, and (3) applying community detection to distill coherent paper groups with shared themes. We evaluated the proposed method on three NLP literature-based question-answering datasets, and the results demonstrate its superiority over other retrieval-augmented baseline approaches, confirming the effectiveness of our framework.

Method: Proposes Temporal Guidance (TeGu) using multi-token prediction to create weaker amateur predictions for self-contrast. Introduces lightweight Conditional MTP Projector (cMTPP) to standardize implementation without multiple independent networks.

Result: TeGu achieves significant performance improvements across various model series and benchmarks while maintaining low additional memory consumption and computational overhead.

Conclusion: TeGu provides an effective and efficient contrastive decoding approach that leverages temporal dimension guidance with multi-token prediction, offering better performance than existing methods with minimal computational cost.

Abstract: Contrastive Decoding (CD) enhances the generation quality of large language models (LLMs) but incurs significant additional computational overhead due to the need for an auxiliary model. Existing internal self-contrastive decoding methods, such as Decoding by Contrasting Layers (DoLa), focus on discrepancies across different layers, which are notably unstable on small-scale models. In this work, based on the observation that LLMs exhibit local preferences, we propose a novel contrastive guidance strategy along the temporal dimension, namely Temporal Guidance (TeGu). Our method ingeniously leverages Multi-Token Prediction (MTP) to construct weaker amateur predictions for model self-contrast. To standardize the implementation of this mechanism, we further introduce a lightweight Conditional MTP Projector (cMTPP), which avoids maintaining multiple independent networks as required by other MTP modules. Across various model series and benchmarks, TeGu achieves significant performance improvements while maintaining low additional memory consumption and computational overhead.

Method: Proposes CoFrGeNets (Continued Fraction Generative Networks) - a new function class inspired by continued fractions. Designs novel architectural components that replace Multi-head Attention and Feed-Forward Networks in Transformer blocks. Derives custom gradient formulations for more accurate and efficient optimization than standard PyTorch gradients.

Result: Tested on GPT2-xl (1.5B) and Llama3 (3.2B) architectures. Models achieve competitive/superior performance on downstream tasks (classification, Q&A, reasoning, text understanding) with 2/3 to 1/2 the parameters and shorter pre-training time compared to original models.

Conclusion: CoFrGeNets offer efficient alternatives to Transformer components that maintain performance with fewer parameters. The approach is easy to integrate into existing workflows and future hardware-optimized implementations could further improve efficiency.

Abstract: Transformers are arguably the preferred architecture for language generation. In this paper, inspired by continued fractions, we introduce a new function class for generative modeling. The architecture family implementing this function class is named CoFrGeNets - Continued Fraction Generative Networks. We design novel architectural components based on this function class that can replace Multi-head Attention and Feed-Forward Networks in Transformer blocks while requiring much fewer parameters. We derive custom gradient formulations to optimize the proposed components more accurately and efficiently than using standard PyTorch-based gradients. Our components are a plug-in replacement requiring little change in training or inference procedures that have already been put in place for Transformer-based models thus making our approach easy to incorporate in large industrial workflows. We experiment on two very different transformer architectures GPT2-xl (1.5B) and Llama3 (3.2B), where the former we pre-train on OpenWebText and GneissWeb, while the latter we pre-train on the docling data mix which consists of nine different datasets. Results show that the performance on downstream classification, Q& A, reasoning and text understanding tasks of our models is competitive and sometimes even superior to the original models with $\frac{2}{3}$ to $\frac{1}{2}$ the parameters and shorter pre-training time. We believe that future implementations customized to hardware will further bring out the true potential of our architectures.

Method: Systematic analysis of ChatGPT on 4 different medical information extraction tasks across 6 benchmark datasets, evaluating performance, explainability, confidence, faithfulness, and uncertainty.

Result: ChatGPT performs worse than fine-tuned models on MedIE tasks, provides high-quality explanations but is over-confident, shows high faithfulness to original text, and generation uncertainty affects extraction results.

Conclusion: While ChatGPT shows promise in explainability and faithfulness, its performance limitations, over-confidence, and generation uncertainty hinder its practical application in medical information extraction tasks.

Abstract: Large Language Models (LLMs) like ChatGPT have demonstrated amazing capabilities in comprehending user intents and generate reasonable and useful responses. Beside their ability to chat, their capabilities in various natural language processing (NLP) tasks are of interest to the research community. In this paper, we focus on assessing the overall ability of ChatGPT in 4 different medical information extraction (MedIE) tasks across 6 benchmark datasets. We present the systematically analysis by measuring ChatGPT’s performance, explainability, confidence, faithfulness, and uncertainty. Our experiments reveal that: (a) ChatGPT’s performance scores on MedIE tasks fall behind those of the fine-tuned baseline models. (b) ChatGPT can provide high-quality explanations for its decisions, however, ChatGPT is over-confident in its predcitions. (c) ChatGPT demonstrates a high level of faithfulness to the original text in the majority of cases. (d) The uncertainty in generation causes uncertainty in information extraction results, thus may hinder its applications in MedIE tasks.

Method: Hierarchical diffusion model with differentiable tokenizer (Segment Splitter) using Probabilistic Attention for soft masking, hierarchical compression from characters to document-level representations, and Denoising Diffusion Mixed Model (DDMM) for stable latent-space denoising.

Result: Zonkey generates coherent, variable-length text from noise, demonstrating emergent linguistic hierarchies (word boundaries, sentence starts) without supervision and showing promising alignment to data distributions compared to entropy-based tokenizers.

Conclusion: The approach advances toward fully gradient-based LLMs with potential for better domain adaptation and scalable generation, moving beyond fixed tokenization constraints.

Abstract: Large language models (LLMs) have revolutionized natural language processing, yet they remain constrained by fixed, non-differentiable tokenizers like Byte Pair Encoding (BPE), which hinder end-to-end optimization and adaptability to noisy or domain-specific data. We introduce Zonkey, a hierarchical diffusion model that addresses these limitations through a fully trainable pipeline from raw characters to document-level representations. At its core is a differentiable tokenizer (Segment Splitter) that learns probabilistic beginning-of-sequence (BOS) decisions, enabling adaptive splits that emerge as linguistically meaningful (e.g., word boundaries at spaces, sentence starts at periods) without explicit supervision. This differentiability is enabled by our novel Probabilistic Attention mechanism, which incorporates position-specific existence probabilities to simulate soft masking over theoretically infinite sequences while preserving gradients. Sequences decay probabilistically rather than relying on end-of-sequence tokens, supporting variable-length outputs. Hierarchical levels compress sequences into higher abstractions (e.g., character n-grams to word-like vectors, then sentence-like), with reconstruction via our Denoising Diffusion Mixed Model (DDMM) for stable and efficient denoising in latent space. A Stitcher ensures overlap invariance across segments. Trained end-to-end on Wikipedia, Zonkey generates coherent, variable-length text from noise, demonstrating emergent hierarchies and promising qualitative alignment to data distributions compared to entropy-based learnable tokenizers. Our approach advances toward fully gradient-based LLMs, with potential for better domain adaptation and scalable generation. We release the source code for training and reproducing our experiments.

Method: Proposes KID (Knowledge-Injected Dual-Head Learning) framework with label-constrained distillation to decompose meme understanding into structured reasoning chains linking visual evidence, background knowledge, and classification labels. Uses dual-head architecture jointly optimizing semantic generation and classification objectives.

Result: Achieves SOTA performance on five multilingual datasets (English, Chinese, Bengali), improving over previous best methods by 2.1%–19.7% across primary evaluation metrics. Ablation studies confirm effectiveness of knowledge injection and dual-head joint learning.

Conclusion: KID framework effectively addresses implicit toxicity in memes through knowledge-grounded reasoning, demonstrating robust and generalizable meme understanding across multiple languages and resource settings.

Abstract: Internet memes have become pervasive carriers of digital culture on social platforms. However, their heavy reliance on metaphors and sociocultural context also makes them subtle vehicles for harmful content, posing significant challenges for automated content moderation. Existing approaches primarily focus on intra-modal and inter-modal signal analysis, while the understanding of implicit toxicity often depends on background knowledge that is not explicitly present in the meme itself. To address this challenge, we propose KID, a Knowledge-Injected Dual-Head Learning framework for knowledge-grounded harmful meme detection. KID adopts a label-constrained distillation paradigm to decompose complex meme understanding into structured reasoning chains that explicitly link visual evidence, background knowledge, and classification labels. These chains guide the learning process by grounding external knowledge in meme-specific contexts. In addition, KID employs a dual-head architecture that jointly optimizes semantic generation and classification objectives, enabling aligned linguistic reasoning while maintaining stable decision boundaries. Extensive experiments on five multilingual datasets spanning English, Chinese, and low-resource Bengali demonstrate that KID achieves SOTA performance on both binary and multi-label harmful meme detection tasks, improving over previous best methods by 2.1%–19.7% across primary evaluation metrics. Ablation studies further confirm the effectiveness of knowledge injection and dual-head joint learning, highlighting their complementary contributions to robust and generalizable meme understanding. The code and data are available at https://github.com/PotatoDog1669/KID.

Method: Proposes Adversarial Memory Adaptation (AMA) with three agents: 1) Challenger generates QA pairs from dialogues, 2) Memory answers these questions simulating inference, 3) Evaluator assesses responses and performs error analysis, 4) Adapter analyzes errors and performs dual-level updates on construction strategy and content.

Result: Extensive experiments on long dialogue benchmark LoCoMo demonstrate AMA’s effectiveness, showing it can be integrated into various existing memory systems to enhance adaptability to downstream tasks.

Conclusion: AMA provides task-aware supervision signals during the offline phase, aligning memory construction and update with task objectives, thereby improving downstream task performance for conversational agents handling long conversations.

Abstract: Conversational agents struggle to handle long conversations due to context window limitations. Therefore, memory systems are developed to leverage essential historical information. Existing memory systems typically follow a pipeline of offline memory construction and update, and online retrieval. Despite the flexible online phase, the offline phase remains fixed and task-independent. In this phase, memory construction operates under a predefined workflow and fails to emphasize task relevant information. Meanwhile, memory updates are guided by generic metrics rather than task specific supervision. This leads to a misalignment between offline memory preparation and task requirements, which undermines downstream task performance. To this end, we propose an Adversarial Memory Adaptation mechanism (AMA) that aligns memory construction and update with task objectives by simulating task execution. Specifically, first, a challenger agent generates question answer pairs based on the original dialogues. The constructed memory is then used to answer these questions, simulating downstream inference. Subsequently, an evaluator agent assesses the responses and performs error analysis. Finally, an adapter agent analyzes the error cases and performs dual level updates on both the construction strategy and the content. Through this process, the memory system receives task aware supervision signals in advance during the offline phase, enhancing its adaptability to downstream tasks. AMA can be integrated into various existing memory systems, and extensive experiments on long dialogue benchmark LoCoMo demonstrate its effectiveness.

Method: RAG-E adapts Integrated Gradients for retriever analysis, introduces PMCSHAP (Monte Carlo-stabilized Shapley Value approximation) for generator attribution, and uses the Weighted Attribution-Relevance Gap (WARG) metric to measure generator-retriever alignment.

Result: Empirical analysis on TREC CAsT and FoodSafeSum shows critical misalignments: 47.4-66.7% of queries have generators ignoring top-ranked documents, and 48.1-65.9% rely on less relevant documents.

Conclusion: RAG output quality depends on component interplay, not just individual performance, and RAG-E provides auditing capabilities to identify and address these misalignments in RAG systems.

Abstract: Retrieval-Augmented Generation (RAG) systems combine dense retrievers and language models to ground LLM outputs in retrieved documents. However, the opacity of how these components interact creates challenges for deployment in high-stakes domains. We present RAG-E, an end-to-end explainability framework that quantifies retriever-generator alignment through mathematically grounded attribution methods. Our approach adapts Integrated Gradients for retriever analysis, introduces PMCSHAP, a Monte Carlo-stabilized Shapley Value approximation, for generator attribution, and introduces the Weighted Attribution-Relevance Gap (WARG) metric to measure how well a generator’s document usage aligns with a retriever’s ranking. Empirical analysis on TREC CAsT and FoodSafeSum reveals critical misalignments: for 47.4% to 66.7% of queries, generators ignore the retriever’s top-ranked documents, while 48.1% to 65.9% rely on documents ranked as less relevant. These failure modes demonstrate that RAG output quality depends not solely on individual component performance but on their interplay, which can be audited via RAG-E.

Method: Proposes Distribution-Aware Reward Estimation (DARE) that shifts from single majority outcomes to full empirical rollout distributions. Includes exploration bonus and distribution pruning for better exploration and denoising.

Result: DARE improves optimization stability and final performance over baselines, achieving 25.3% relative improvement on AIME 2024 and 5.3% on AMC benchmarks.

Conclusion: Using full rollout distributions rather than majority voting provides more informative and robust reward estimation for test-time RL in LLMs, leading to better reasoning performance.

Abstract: Test-time reinforcement learning (TTRL) enables large language models (LLMs) to self-improve on unlabeled inputs, but its effectiveness critically depends on how reward signals are estimated without ground-truth supervision. Most existing TTRL methods rely on majority voting (MV) over rollouts to produce deterministic rewards, implicitly assuming that the majority rollout provides a reliable learning signal. We show that this assumption is fragile: MV reduces the rollout distribution into a single outcome, discarding information about non-majority but correct actions candidates, and yields systematically biased reward estimates. To address this, we propose Distribution-AwareReward Estimation (DARE), which shifts reward estimation from a single majority outcome to the full empirical rollout distribution. DARE further augments this distribution-based reward with an exploration bonus and a distribution pruning mechanism for non-majority rollout exploration and reward denoise, yielding a more informative and robust reward estimation. Extensive experiments on challenging reasoning benchmarks show that DARE improves optimization stability and final performance over recent baselines, achieving relative improvements of 25.3% on challenging AIME 2024 and 5.3% on AMC.

Method: Created MilSCORE dataset with expert-authored multi-hop questions grounded in complex simulated military planning scenarios, evaluating LLMs’ ability to combine tactical and spatial reasoning across multiple sources.

Result: Substantial headroom on MilSCORE benchmark, indicating current systems struggle with realistic scenario-level long-context planning; baseline results reported for contemporary vision-language models.

Conclusion: MilSCORE serves as a challenging testbed for future work on multi-modal long-context reasoning, highlighting limitations of current systems in realistic geospatial planning tasks.

Abstract: As large language models (LLMs) are applied to increasingly longer and more complex tasks, there is a growing need for realistic long-context benchmarks that require selective reading and integration of heterogeneous, multi-modal information sources. This need is especially acute for geospatial planning problems, such as those found in planning for large-scale military operations, which demand fast and accurate reasoning over maps, orders, intelligence reports, and other distributed data. To address this gap, we present MilSCORE (Military Scenario Contextual Reasoning), to our knowledge the first scenario-level dataset of expert-authored, multi-hop questions grounded in a complex, simulated military planning scenario used for training. MilSCORE is designed to evaluate high-stakes decision-making and planning, probing LLMs’ ability to combine tactical and spatial reasoning across multiple sources and to reason over long-horizon, geospatially rich context. The benchmark includes a diverse set of question types across seven categories targeting both factual recall and multi-step reasoning about constraints, strategy, and spatial analysis. We provide an evaluation protocol and report baseline results for a range of contemporary vision-language models. Our findings highlight substantial headroom on MilSCORE, indicating that current systems struggle with realistic, scenario-level long-context planning, and positioning MilSCORE as a challenging testbed for future work.

Method: Proposes GiG framework with Graph-in-Graph architecture: 1) Uses GNN to encode environmental states into embeddings, 2) Organizes embeddings into action-connected execution trace graphs in experience memory bank, 3) Clusters graph embeddings for retrieval of structure-aware priors, 4) Introduces bounded lookahead module using symbolic transition logic for grounded action projection.

Result: Outperforms state-of-the-art baselines on three embodied planning benchmarks: achieves Pass@1 performance gains of up to 22% on Robotouille Synchronous, 37% on Asynchronous, and 15% on ALFWorld with comparable or lower computational cost.

Conclusion: GiG framework effectively addresses long-horizon planning challenges for embodied agents by leveraging graph-structured memory and symbolic reasoning, enabling better strategy coherence and constraint adherence in dynamic environments.

Abstract: While Large Language Models (LLMs) have demonstrated strong zero-shot reasoning capabilities, their deployment as embodied agents still faces fundamental challenges in long-horizon planning. Unlike open-ended text generation, embodied agents must decompose high-level intent into actionable sub-goals while strictly adhering to the logic of a dynamic, observed environment. Standard LLM planners frequently fail to maintain strategy coherence over extended horizons due to context window limitation or hallucinate transitions that violate constraints. We propose GiG, a novel planning framework that structures embodied agents’ memory using a Graph-in-Graph architecture. Our approach employs a Graph Neural Network (GNN) to encode environmental states into embeddings, organizing these embeddings into action-connected execution trace graphs within an experience memory bank. By clustering these graph embeddings, the framework enables retrieval of structure-aware priors, allowing agents to ground current decisions in relevant past structural patterns. Furthermore, we introduce a novel bounded lookahead module that leverages symbolic transition logic to enhance the agents’ planning capabilities through the grounded action projection. We evaluate our framework on three embodied planning benchmarks-Robotouille Synchronous, Robotouille Asynchronous, and ALFWorld. Our method outperforms state-of-the-art baselines, achieving Pass@1 performance gains of up to 22% on Robotouille Synchronous, 37% on Asynchronous, and 15% on ALFWorld with comparable or lower computational cost.

Method: Geometric approach to analyze rewrite-based detection, then introduces novel algorithm that adaptively learns distance between original and rewritten text rather than using fixed distance.

Result: Extensive experiments with 100+ settings show superior performance over baselines, achieving 57.8% to 80.6% relative improvements over strongest baseline across different target LLMs (GPT, Claude, Gemini).

Conclusion: Adaptively learned distance functions are more effective for LLM-generated content detection than fixed distances, with the proposed approach demonstrating strong generalization across different LLMs.

Abstract: Modern large language models (LLMs) such as GPT, Claude, and Gemini have transformed the way we learn, work, and communicate. Yet, their ability to produce highly human-like text raises serious concerns about misinformation and academic integrity, making it an urgent need for reliable algorithms to detect LLM-generated content. In this paper, we start by presenting a geometric approach to demystify rewrite-based detection algorithms, revealing their underlying rationale and demonstrating their generalization ability. Building on this insight, we introduce a novel rewrite-based detection algorithm that adaptively learns the distance between the original and rewritten text. Theoretically, we demonstrate that employing an adaptively learned distance function is more effective for detection than using a fixed distance. Empirically, we conduct extensive experiments with over 100 settings, and find that our approach demonstrates superior performance over baseline algorithms in the majority of scenarios. In particular, it achieves relative improvements from 57.8% to 80.6% over the strongest baseline across different target LLMs (e.g., GPT, Claude, and Gemini).

Method: SONIC compresses historical segments into compact Nexus tokens using a learning-based framework with dynamic budget training, allowing flexible adaptation to varying memory constraints without retraining.

Result: At compression ratios of 80% and 50%, SONIC outperforms baselines like H2O and StreamingLLM on four multi-turn benchmarks, achieving 35.55% average score improvement on MTBench101 and accelerating inference by 50.1% compared to full-context generation.

Conclusion: SONIC effectively sustains coherent multi-turn dialogues while enhancing deployment efficiency through learning-based KV cache compression.

Abstract: The linear growth of Key-Value (KV) cache remains a bottleneck for multi-turn LLM deployment. Existing KV cache compression methods often fail to account for the structural properties of multi-turn dialogues, relying on heuristic eviction that risks losing critical context. We propose \textbf{SONIC}, a learning-based framework that compresses historical segments into compact and semantically rich \textbf{Nexus} tokens. By integrating dynamic budget training, SONIC allows flexible adaptation to varying memory constraints without retraining. Experiments show that at compression ratios of 80% and 50%, SONIC consistently outperforms baselines such as H2O and StreamingLLM on four diverse multi-turn benchmarks. Specifically, on the widely used MTBench101 benchmark, SONIC achieves an average score improvement of 35.55% over state-of-the-art baselines, validating its effectiveness in sustaining coherent multi-turn dialogues. Furthermore, SONIC enhances deployment efficiency, accelerating the overall inference process by 50.1% compared to full-context generation.

Method: GPT-based architecture with selective fine-tuning strategy: freezes majority of GPT-2 backbone, trains only final Transformer block, final layer normalization, and lightweight classification head. Evaluated on MIMIC-IV-Note radiology reports with CheXpert-style labels.

Result: Model shows stable convergence and strong classification performance across varying dataset sizes, particularly effective for non-mention and negated findings. Achieves efficient adaptation with significantly reduced computational complexity.

Conclusion: Selective fine-tuning of pretrained generative language models provides efficient and effective pathway for clinical text classification, enabling scalable adaptation to real-world EHR data while reducing computational burden.

Abstract: The increasing availability of unstructured clinical narratives in electronic health records (EHRs) has created new opportunities for automated disease characterization, cohort identification, and clinical decision support. However, modeling long, domain-specific clinical text remains challenging due to limited labeled data, severe class imbalance, and the high computational cost of adapting large pretrained language models. This study presents a GPT-based architecture for clinical text classification that adapts a pretrained decoder-only Transformer using a selective fine-tuning strategy. Rather than updating all model parameters, the majority of the GPT-2 backbone is frozen, and training is restricted to the final Transformer block, the final layer normalization, and a lightweight classification head. This approach substantially reduces the number of trainable parameters while preserving the representational capacity required to model complex clinical language. The proposed method is evaluated on radiology reports from the MIMIC-IV-Note dataset using uncertainty-aware CheXpert-style labels derived directly from report text. Experiments cover multiple problem formulations, including multi-label classification of radiographic findings, binary per-label classification under different uncertainty assumptions, and aggregate disease outcome prediction. Across varying dataset sizes, the model exhibits stable convergence behavior and strong classification performance, particularly in settings dominated by non-mention and negated findings. Overall, the results indicate that selective fine-tuning of pretrained generative language models provides an efficient and effective pathway for clinical text classification, enabling scalable adaptation to real-world EHR data while significantly reducing computational complexity.

Method: On-policy Verbal Distillation (OVD) replaces token-level probability matching with trajectory matching using discrete verbal scores (0-9) from teacher models, avoiding token-level alignment and reducing memory consumption.

Result: OVD substantially outperforms existing methods with up to +12.9% absolute improvement in average EM on Web Q&A tasks and up to +25.7% gain on math benchmarks, while exhibiting superior training efficiency.

Conclusion: OVD provides an effective memory-efficient framework for knowledge distillation that enables better exploration and performance on reasoning tasks by using verbal feedback instead of token-level alignment.

Abstract: Knowledge distillation offers a promising path to transfer reasoning capabilities from large teacher models to efficient student models; however, existing token-level on-policy distillation methods require token-level alignment between the student and teacher models, which restricts the student model’s exploration ability, prevent effective use of interactive environment feedback, and suffer from severe memory bottlenecks in reinforcement learning. We introduce On-policy Verbal Distillation (OVD), a memory-efficient framework that replaces token-level probability matching with trajectory matching using discrete verbal scores (0–9) from teacher models. OVD dramatically reduces memory consumption while enabling on-policy distillation from teacher models with verbal feedback, and avoids token-level alignment, allowing the student model to freely explore the output space. Extensive experiments on Web question answering and mathematical reasoning tasks show that OVD substantially outperforms existing methods, delivering up to +12.9% absolute improvement in average EM on Web Q&A tasks and a up to +25.7% gain on math benchmarks (when trained with only one random samples), while also exhibiting superior training efficiency. Our project page is available at https://OVD.github.io

Method: Token-level hallucination control using self-checking decoding: 1) Internal verification at each reasoning step to detect hallucinated tokens before propagation, 2) Candidate fragment evaluation in latent space with explicit hallucination risk scoring, 3) Iterative pruning and regeneration to dynamically correct detected errors.

Result: Experiments on HALU datasets show Token-Guard substantially reduces hallucinations and improves generation accuracy. Offers scalable, modular solution for reliable LLM outputs.

Conclusion: Token-Guard provides effective hallucination control without resource-intensive retrieval or large-scale fine-tuning, enabling more reliable LLM generation through token-level self-checking mechanisms.

Abstract: Large Language Models (LLMs) often hallucinate, generating content inconsistent with the input. Retrieval-Augmented Generation (RAG) and Reinforcement Learning with Human Feedback (RLHF) can mitigate hallucinations but require resource-intensive retrieval or large-scale fine-tuning. Decoding-based methods are lighter yet lack explicit hallucination control. To address this, we present Token-Guard, a token-level hallucination control method based on self-checking decoding. Token-Guard performs internal verification at each reasoning step to detect hallucinated tokens before they propagate. Candidate fragments are further evaluated in a latent space with explicit hallucination risk scoring, while iterative pruning and regeneration dynamically correct detected errors. Experiments on HALU datasets show Token-Guard substantially reduces hallucinations and improves generation accuracy, offering a scalable, modular solution for reliable LLM outputs. Our code is publicly available.

Method: Introduces Mechanistic Data Attribution (MDA), a scalable framework using Influence Functions to attribute interpretable units to training samples. Conducts experiments on Pythia family models with targeted interventions (removing/augmenting high-influence samples) versus random interventions.

Result: Targeted interventions significantly modulate emergence of interpretable heads while random interventions show no effect. Repetitive structural data (LaTeX, XML) acts as mechanistic catalyst. Interventions on induction head formation cause concurrent changes in in-context learning capability, providing causal evidence for functional link between induction heads and ICL.

Conclusion: Proposes mechanistic data augmentation pipeline that accelerates circuit convergence across model scales, providing principled methodology for steering LLM developmental trajectories. Demonstrates causal connections between specific training data patterns and emergent model capabilities.

Abstract: While Mechanistic Interpretability has identified interpretable circuits in LLMs, their causal origins in training data remain elusive. We introduce Mechanistic Data Attribution (MDA), a scalable framework that employs Influence Functions to trace interpretable units back to specific training samples. Through extensive experiments on the Pythia family, we causally validate that targeted intervention–removing or augmenting a small fraction of high-influence samples–significantly modulates the emergence of interpretable heads, whereas random interventions show no effect. Our analysis reveals that repetitive structural data (e.g., LaTeX, XML) acts as a mechanistic catalyst. Furthermore, we observe that interventions targeting induction head formation induce a concurrent change in the model’s in-context learning (ICL) capability. This provides direct causal evidence for the long-standing hypothesis regarding the functional link between induction heads and ICL. Finally, we propose a mechanistic data augmentation pipeline that consistently accelerates circuit convergence across model scales, providing a principled methodology for steering the developmental trajectories of LLMs.

Method: Introduces MVES with tiered evaluation components for general LLM apps, RAG, and agentic workflows; synthesizes evaluation methods (automated checks, human rubrics, LLM-as-judge); conducts reproducible experiments with Ollama using Llama 3 8B and Qwen 2.5 7B.

Result: Experiments show generic “improved” prompt templates trade off behaviors: extraction pass rate decreased from 100% to 90% and RAG compliance from 93.3% to 80% for Llama 3, while instruction-following improved.

Conclusion: Evaluation-driven prompt iteration and careful claim calibration are needed rather than universal prompt recipes; provides reproducible test suites and harnesses.

Abstract: Evaluating Large Language Model (LLM) applications differs from traditional software testing because outputs are stochastic, high-dimensional, and sensitive to prompt and model changes. We present an evaluation-driven workflow - Define, Test, Diagnose, Fix - that turns these challenges into a repeatable engineering loop. We introduce the Minimum Viable Evaluation Suite (MVES), a tiered set of recommended evaluation components for (i) general LLM applications, (ii) retrieval-augmented generation (RAG), and (iii) agentic tool-use workflows. We also synthesize common evaluation methods (automated checks, human rubrics, and LLM-as-judge) and discuss known judge failure modes. In reproducible local experiments (Ollama; Llama 3 8B Instruct and Qwen 2.5 7B Instruct), we observe that a generic “improved” prompt template can trade off behaviors: on our small structured suites, extraction pass rate decreased from 100% to 90% and RAG compliance from 93.3% to 80% for Llama 3 when replacing task-specific prompts with generic rules, while instruction-following improved. These findings motivate evaluation-driven prompt iteration and careful claim calibration rather than universal prompt recipes. All test suites, harnesses, and results are included for reproducibility.

Method: Reformulates diffusion process with causal attention masks for dense per-token supervision, introduces soft-tailed masking to preserve local context, and uses context-aware reweighting from signal-to-noise principles for dynamic parallel decoding with KV-caching.

Result: Outperforms existing discrete diffusion baselines while reducing training latency by 3× compared to block diffusion methods, achieving ARM-level data efficiency with parallel generation benefits.

Conclusion: CARD establishes a robust paradigm for efficient LLMs by unifying training efficiency of ARMs with parallel inference capabilities of diffusion models.

Abstract: In this work, we propose Causal Autoregressive Diffusion (CARD), a novel framework that unifies the training efficiency of ARMs with the high-throughput inference of diffusion models. CARD reformulates the diffusion process within a strictly causal attention mask, enabling dense, per-token supervision in a single forward pass. To address the optimization instability of causal diffusion, we introduce a soft-tailed masking schema to preserve local context and a context-aware reweighting mechanism derived from signal-to-noise principles. This design enables dynamic parallel decoding, where the model leverages KV-caching to adaptively generate variable-length token sequences based on confidence. Empirically, CARD outperforms existing discrete diffusion baselines while reducing training latency by 3 $\times$ compared to block diffusion methods. Our results demonstrate that CARD achieves ARM-level data efficiency while unlocking the latency benefits of parallel generation, establishing a robust paradigm for next-generation efficient LLMs.

Method: The paper proposes using masked diffusion language models (MDLMs) which iteratively refine all tokens in parallel, decoupling computation order from output structure. They validate this on GSM8K, Math500, and introduce ReasonOrderQA benchmark with controlled difficulty and order-level evaluation. They analyze how MDLMs achieve order robustness by examining token stabilization patterns during diffusion.

Result: When prompts request answers before reasoning, AR models exhibit large accuracy gaps (up to 67% relative drop) compared to standard chain-of-thought ordering, while MDLMs remain stable (≤14% relative drop). MDLMs achieve order robustness by stabilizing simpler tokens (reasoning steps) earlier in diffusion than complex ones (final answers), enabling reasoning before answer commitment.

Conclusion: MDLMs offer order robustness that AR models lack, maintaining accuracy when output structure conflicts with natural reasoning order. The paper identifies failure conditions where this advantage weakens, outlining limits for order robustness.

Abstract: Autoregressive (AR) language models enforce a fixed left-to-right generation order, creating a fundamental limitation when the required output structure conflicts with natural reasoning (e.g., producing answers before explanations due to presentation or schema constraints). In such cases, AR models must commit to answers before generating intermediate reasoning, and this rigid constraint forces premature commitment. Masked diffusion language models (MDLMs), which iteratively refine all tokens in parallel, offer a way to decouple computation order from output structure. We validate this capability on GSM8K, Math500, and ReasonOrderQA, a benchmark we introduce with controlled difficulty and order-level evaluation. When prompts request answers before reasoning, AR models exhibit large accuracy gaps compared to standard chain-of-thought ordering (up to 67% relative drop), while MDLMs remain stable ($\leq$14% relative drop), a property we term “order robustness”. Using ReasonOrderQA, we present evidence that MDLMs achieve order robustness by stabilizing simpler tokens (e.g., reasoning steps) earlier in the diffusion process than complex ones (e.g., final answers), enabling reasoning tokens to stabilize before answer commitment. Finally, we identify failure conditions where this advantage weakens, outlining the limits required for order robustness.

Method: Proposes Leviathan architecture with a continuous embedding generator instead of discrete lookup tables. Evaluates on Pile dataset under isoparametric settings compared to standard LLaMA-style architecture.

Result: Leviathan consistently outperforms standard architecture. Empirical power-law fit shows superior effective parameter capacity - behaves like a dense model with 1.47 to 2.11× more parameters across studied regime.

Conclusion: Continuous embedding generators can significantly improve parameter efficiency in small language models, challenging the assumption that parameters are interchangeable in transformer scaling laws.

Abstract: Transformer scaling law analyses typically treat parameters as interchangeable; an abstraction that accurately predicts loss-compute relationships. Yet, in sub-billion-parameter small language models (SLMs), embedding matrices dominate the parameter budget. This work argues that this allocation is as suboptimal as it is counterintuitive. Leviathan is an architecture with a continuous embedding generator to replace the discrete lookup tables of canonical models. Evaluating on the Pile dataset under isoparametric settings, Leviathan consistently outperforms a standard, LLaMA-style architecture. By means of an empirical power-law fit, Leviathan exhibits a markedly superior effective parameter capacity. Across the regime studied, Leviathan behaves as a dense model with $1.47$ to $2.11 \times$ more parameters.

Method: Proposed SUSTAINSCORE metric to quantify interference by measuring performance drop after inserting self-evident constraints extracted from original successful outputs. Tested on LLMs across mathematics, multi-hop QA, and code generation tasks, analyzed attention patterns, and investigated different post-training paradigms.

Result: Adding self-evident constraints leads to substantial performance drops even for advanced models like Claude-Sonnet-4.5. Failed cases allocate significantly more attention to constraints than successful ones. Different alignment strategies show varying susceptibility to interference.

Conclusion: Instruction following can paradoxically harm task performance, revealing limitations in current alignment approaches. The SUSTAINSCORE metric provides a tool to study this phenomenon and evaluate alignment strategies.

Abstract: Instruction following aims to align Large Language Models (LLMs) with human intent by specifying explicit constraints on how tasks should be performed. However, we reveal a counterintuitive phenomenon: instruction following can paradoxically interfere with LLMs’ task-solving capability. We propose a metric, SUSTAINSCORE, to quantify the interference of instruction following with task solving. It measures task performance drop after inserting into the instruction a self-evident constraint, which is naturally met by the original successful model output and extracted from it. Experiments on current LLMs in mathematics, multi-hop QA, and code generation show that adding the self-evident constraints leads to substantial performance drops, even for advanced models such as Claude-Sonnet-4.5. We validate the generality of the interference across constraint types and scales. Furthermore, we identify common failure patterns, and by investigating the mechanisms of interference, we observe that failed cases allocate significantly more attention to constraints compared to successful ones. Finally, we use SUSTAINSCORE to conduct an initial investigation into how distinct post-training paradigms affect the interference, presenting empirical observations on current alignment strategies. We will release our code and data to facilitate further research

Method: Created MasalBench, a comprehensive benchmark for assessing LLMs’ contextual understanding of Persian proverbs and cross-cultural analogical reasoning to equivalent English proverbs. Evaluated eight state-of-the-art LLMs on this benchmark.

Result: LLMs performed well in identifying Persian proverbs in context (accuracies above 0.90), but performance dropped significantly when identifying equivalent English proverbs (best model achieved 0.79 accuracy). This reveals limitations in cultural knowledge and analogical reasoning.

Conclusion: Current LLMs have significant limitations in cross-cultural understanding and analogical reasoning for low-resource languages. MasalBench provides a framework for assessing these capabilities and highlights the need for better cultural knowledge integration in LLMs.

Abstract: In recent years, multilingual Large Language Models (LLMs) have become an inseparable part of daily life, making it crucial for them to master the rules of conversational language in order to communicate effectively with users. While previous work has evaluated LLMs’ understanding of figurative language in high-resource languages, their performance in low-resource languages remains underexplored. In this paper, we introduce MasalBench, a comprehensive benchmark for assessing LLMs’ contextual and cross-cultural understanding of Persian proverbs, which are a key component of conversation in this low-resource language. We evaluate eight state-of-the-art LLMs on MasalBench and find that they perform well in identifying Persian proverbs in context, achieving accuracies above 0.90. However, their performance drops considerably when tasked with identifying equivalent English proverbs, with the best model achieving 0.79 accuracy. Our findings highlight the limitations of current LLMs in cultural knowledge and analogical reasoning, and they provide a framework for assessing cross-cultural understanding in other low-resource languages. MasalBench is available at https://github.com/kalhorghazal/MasalBench.

Method: Introduces G²-Reader with two complementary graphs: Content Graph preserves document-native structure and cross-modal semantics, while Planning Graph maintains an agentic directed acyclic graph of sub-questions to track intermediate findings and guide stepwise navigation for evidence completion.

Result: On VisDoMBench across five multimodal domains, G²-Reader with Qwen3-VL-32B-Instruct achieves 66.21% average accuracy, outperforming strong baselines and standalone GPT-5 (53.08%).

Conclusion: The dual-graph approach effectively addresses structural preservation and retrieval guidance challenges in multimodal document question answering, demonstrating significant performance improvements over existing methods.

Abstract: Retrieval-augmented generation is a practical paradigm for question answering over long documents, but it remains brittle for multimodal reading where text, tables, and figures are interleaved across many pages. First, flat chunking breaks document-native structure and cross-modal alignment, yielding semantic fragments that are hard to interpret in isolation. Second, even iterative retrieval can fail in long contexts by looping on partial evidence or drifting into irrelevant sections as noise accumulates, since each step is guided only by the current snippet without a persistent global search state. We introduce $G^2$-Reader, a dual-graph system, to address both issues. It evolves a Content Graph to preserve document-native structure and cross-modal semantics, and maintains a Planning Graph, an agentic directed acyclic graph of sub-questions, to track intermediate findings and guide stepwise navigation for evidence completion. On VisDoMBench across five multimodal domains, $G^2$-Reader with Qwen3-VL-32B-Instruct reaches 66.21% average accuracy, outperforming strong baselines and a standalone GPT-5 (53.08%).

Method: Proposes VTC-R1 paradigm that renders intermediate reasoning segments into compact images as “optical memory,” iteratively feeding them into vision-language models. Uses OpenR1-Math-220K dataset for training and fine-tunes VLMs like Glyph and Qwen3-VL.

Result: Achieves 3.4x token compression, outperforms standard long-context reasoning on MATH500, AIME25, AMC23, and GPQA-D benchmarks, with 2.7x speedup in end-to-end latency.

Conclusion: VTC-R1 offers a scalable solution for reasoning-intensive applications by efficiently compressing textual reasoning into visual representations for vision-language models.

Abstract: Long-context reasoning has significantly empowered large language models (LLMs) to tackle complex tasks, yet it introduces severe efficiency bottlenecks due to the computational complexity. Existing efficient approaches often rely on complex additional training or external models for compression, which limits scalability and discards critical fine-grained information. In this paper, we propose VTC-R1, a new efficient reasoning paradigm that integrates vision-text compression into the reasoning process. Instead of processing lengthy textual traces, VTC-R1 renders intermediate reasoning segments into compact images, which are iteratively fed back into vision-language models as “optical memory.” We construct a training dataset based on OpenR1-Math-220K achieving 3.4x token compression and fine-tune representative VLMs-Glyph and Qwen3-VL. Extensive experiments on benchmarks such as MATH500, AIME25, AMC23 and GPQA-D demonstrate that VTC-R1 consistently outperforms standard long-context reasoning. Furthermore, our approach significantly improves inference efficiency, achieving 2.7x speedup in end-to-end latency, highlighting its potential as a scalable solution for reasoning-intensive applications. Our code is available at https://github.com/w-yibo/VTC-R1.

Method: ECO (Error-Compensating Optimizer) eliminates master weights by applying updates directly to quantized parameters, quantizing weights after each step, and injecting quantization error into optimizer momentum to form an error-feedback loop without additional memory.

Result: ECO matches baselines with master weights up to near-lossless accuracy across various models (30-800M Transformers, Gemma-3 1B, 2.1B SMoE with FP8, and fine-tuning DeepSeek-MoE-16B in INT4), significantly improving the memory vs validation loss Pareto frontier.

Conclusion: ECO successfully eliminates the memory overhead of master weights while maintaining training accuracy through error-feedback mechanisms, making it particularly valuable for memory-constrained training of large models like SMoEs.

Abstract: Quantization has significantly improved the compute and memory efficiency of Large Language Model (LLM) training. However, existing approaches still rely on accumulating their updates in high-precision: concretely, gradient updates must be applied to a high-precision weight buffer, known as $\textit{master weights}$. This buffer introduces substantial memory overhead, particularly for Sparse Mixture of Experts (SMoE) models, where model parameters and optimizer states dominate memory usage. To address this, we introduce the Error-Compensating Optimizer (ECO), which eliminates master weights by applying updates directly to quantized parameters. ECO quantizes weights after each step and carefully injects the resulting quantization error into the optimizer momentum, forming an error-feedback loop with no additional memory. We prove that, under standard assumptions and a decaying learning rate, ECO converges to a constant-radius neighborhood of the optimum, while naive master-weight removal can incur an error that is inversely proportional to the learning rate. We show empirical results for pretraining small Transformers (30-800M), a Gemma-3 1B model, and a 2.1B parameter Sparse MoE model with FP8 quantization, and fine-tuning DeepSeek-MoE-16B in INT4 precision. Throughout, ECO matches baselines with master weights up to near-lossless accuracy, significantly shifting the static memory vs validation loss Pareto frontier.

Method: Fed-MedLoRA transmits only low-rank adapter parameters instead of full LLMs, reducing communication overhead. Fed-MedLoRA+ adds adaptive, data-aware aggregation to improve convergence under cross-site heterogeneity. Applied to clinical information extraction from patient narratives.

Result: Evaluated across five patient cohorts, comparing with BERT, LLaMA-3, DeepSeek-R1, and GPT-4o models in three settings: in-domain training/testing, external validation, and low-resource new-site adaptation using real-world clinical notes.

Conclusion: The framework enables efficient federated adaptation of LLMs for medical applications while addressing computational constraints and data heterogeneity challenges in real-world healthcare settings.

Abstract: Large language models (LLMs) have demonstrated strong performance on medical benchmarks, including question answering and diagnosis. To enable their use in clinical settings, LLMs are typically further adapted through continued pretraining or post-training using clinical data. However, most medical LLMs are trained on data from a single institution, which faces limitations in generalizability and safety in heterogeneous systems. Federated learning (FL) is a promising solution for enabling collaborative model development across healthcare institutions. Yet applying FL to LLMs in medicine remains fundamentally limited. First, conventional FL requires transmitting the full model during each communication round, which becomes impractical for multi-billion-parameter LLMs given the limited computational resources. Second, many FL algorithms implicitly assume data homogeneity, whereas real-world clinical data are highly heterogeneous across patients, diseases, and institutional practices. We introduce the model-agnostic and parameter-efficient federated learning framework for adapting LLMs to medical applications. Fed-MedLoRA transmits only low-rank adapter parameters, reducing communication and computation overhead, while Fed-MedLoRA+ further incorporates adaptive, data-aware aggregation to improve convergence under cross-site heterogeneity. We apply the framework to clinical information extraction (IE), which transforms patient narratives into structured medical entities and relations. Accuracy was assessed across five patient cohorts through comparisons with BERT models, and LLaMA-3 and DeepSeek-R1, GPT-4o models. Evaluation settings included (1) in-domain training and testing, (2) external validation on independent cohorts, and (3) a low-resource new-site adaptation scenario using real-world clinical notes from the Yale New Haven Health System.

Method: Two core components: (1) uncertainty-aware supervised fine-tuning to equip models with interactive reasoning capability, and (2) user-simulator-based policy optimization with composite reward aligning model behavior with user intent.

Result: PIR outperforms baselines on mathematical reasoning, code generation, and document editing with up to 32.70% higher accuracy, 22.90% higher pass rate, and 41.36 BLEU improvement, while reducing nearly half of reasoning computation and unnecessary interactions.

Conclusion: PIR provides a new reasoning paradigm that makes LLMs more reliable and efficient by enabling proactive clarification-seeking behavior, with strong generalization across various reasoning tasks.

Abstract: Reasoning-oriented Large Language Models (LLMs) have achieved remarkable progress with Chain-of-Thought (CoT) prompting, yet they remain fundamentally limited by a \emph{blind self-thinking} paradigm: performing extensive internal reasoning even when critical information is missing or ambiguous. We propose Proactive Interactive Reasoning (PIR), a new reasoning paradigm that transforms LLMs from passive solvers into proactive inquirers that interleave reasoning with clarification. Unlike existing search- or tool-based frameworks that primarily address knowledge uncertainty by querying external environments, PIR targets premise- and intent-level uncertainty through direct interaction with the user. PIR is implemented via two core components: (1) an uncertainty-aware supervised fine-tuning procedure that equips models with interactive reasoning capability, and (2) a user-simulator-based policy optimization framework driven by a composite reward that aligns model behavior with user intent. Extensive experiments on mathematical reasoning, code generation, and document editing demonstrate that PIR consistently outperforms strong baselines, achieving up to 32.70% higher accuracy, 22.90% higher pass rate, and 41.36 BLEU improvement, while reducing nearly half of the reasoning computation and unnecessary interaction turns. Further reliability evaluations on factual knowledge, question answering, and missing-premise scenarios confirm the strong generalization and robustness of PIR. Model and code are publicly available at: \href{https://github.com/SUAT-AIRI/Proactive-Interactive-R1}

Method: Create ~18M instruction templates from real user queries, match them to human-written source documents from pre-training corpora, and instantiate templates to generate billions of synthetic instruction-response training pairs.

Result: Pre-training on FineInstructions outperforms standard pre-training and other synthetic pre-training techniques on benchmarks measuring free-form response quality.

Conclusion: Large-scale synthetic instruction data enables effective pre-training solely with instruction-tuning objective, better aligning LLM training with downstream usage patterns.

Abstract: Due to limited supervised training data, large language models (LLMs) are typically pre-trained via a self-supervised “predict the next word” objective on a vast amount of unstructured text data. To make the resulting model useful to users, it is further trained on a far smaller amount of “instruction-tuning” data comprised of supervised training examples of instructions and responses. To overcome the limited amount of supervised data, we propose a procedure that can transform the knowledge in internet-scale pre-training documents into billions of synthetic instruction and answer training pairs. The resulting dataset, called FineInstructions, uses ~18M instruction templates created from real user-written queries and prompts. These instruction templates are matched to and instantiated with human-written source documents from unstructured pre-training corpora. With “supervised” synthetic training data generated at this scale, an LLM can be pre-trained from scratch solely with the instruction-tuning objective, which is far more in-distribution with the expected downstream usage of LLMs (responding to user prompts). We conduct controlled token-for-token training experiments and find pre-training on FineInstructions outperforms standard pre-training and other proposed synthetic pre-training techniques on standard benchmarks measuring free-form response quality. Our resources can be found at https://huggingface.co/fineinstructions .

Method: DynaWeb learns a world model that predicts web page representations given agent actions, creating a synthetic web environment. Agents can then “dream” by generating rollout trajectories in this simulated environment, combined with real expert trajectories for improved stability and sample efficiency.

Result: Experiments on WebArena and WebVoyager benchmarks show DynaWeb consistently and significantly improves performance of state-of-the-art open-source web agent models.

Conclusion: The framework establishes the viability of training web agents through imagination, offering a scalable and efficient approach to scale up online agentic reinforcement learning.

Abstract: The development of autonomous web agents, powered by Large Language Models (LLMs) and reinforcement learning (RL), represents a significant step towards general-purpose AI assistants. However, training these agents is severely hampered by the challenges of interacting with the live internet, which is inefficient, costly, and fraught with risks. Model-based reinforcement learning (MBRL) offers a promising solution by learning a world model of the environment to enable simulated interaction. This paper introduces DynaWeb, a novel MBRL framework that trains web agents through interacting with a web world model trained to predict naturalistic web page representations given agent actions. This model serves as a synthetic web environment where an agent policy can dream by generating vast quantities of rollout action trajectories for efficient online reinforcement learning. Beyond free policy rollouts, DynaWeb incorporates real expert trajectories from training data, which are randomly interleaved with on-policy rollouts during training to improve stability and sample efficiency. Experiments conducted on the challenging WebArena and WebVoyager benchmarks demonstrate that DynaWeb consistently and significantly improves the performance of state-of-the-art open-source web agent models. Our findings establish the viability of training web agents through imagination, offering a scalable and efficient way to scale up online agentic RL.

Method: Proposes HALO (Hybrid Attention via Layer Optimization) pipeline for distilling Transformer models into RNN-attention hybrids. Introduces HypeNet architecture with novel HyPE position encoding scheme and architectural modifications for better length generalization. Converts Qwen3 series using only 2.3B tokens.

Result: Successfully converted Qwen3 series into HypeNet, achieving performance comparable to original Transformer models while enjoying superior long-context performance and efficiency. Conversion required minimal data (2.3B tokens, <0.01% of pre-training data).

Conclusion: HALO enables efficient conversion of pre-trained Transformers into hybrid models with strong long-context capabilities, addressing the computational barrier to hybrid model adoption while maintaining performance and improving efficiency.

Abstract: Hybrid Transformer architectures, which combine softmax attention blocks and recurrent neural networks (RNNs), have shown a desirable performance-throughput tradeoff for long-context modeling, but their adoption and studies are hindered by the prohibitive cost of large-scale pre-training from scratch. Some recent studies have shown that pre-trained softmax attention blocks can be converted into RNN blocks through parameter transfer and knowledge distillation. However, these transfer methods require substantial amounts of training data (more than 10B tokens), and the resulting hybrid models also exhibit poor long-context performance, which is the scenario where hybrid models enjoy significant inference speedups over Transformer-based models. In this paper, we present HALO (Hybrid Attention via Layer Optimization), a pipeline for distilling Transformer models into RNN-attention hybrid models. We then present HypeNet, a hybrid architecture with superior length generalization enabled by a novel position encoding scheme (named HyPE) and various architectural modifications. We convert the Qwen3 series into HypeNet using HALO, achieving performance comparable to the original Transformer models while enjoying superior long-context performance and efficiency. The conversion requires just 2.3B tokens, less than 0.01% of their pre-training data

Method: Study scaling behavior in transfer learning by finetuning LLMs on machine translation tasks. Investigate how pretraining data choice and size affect downstream metrics (cross-entropy, BLEU, COMET). Analyze distribution alignment between pretraining and downstream data.

Result: With sufficient alignment, downstream metrics improve monotonically with more pretraining data, predictable via log-law. Moderate misalignment causes translation scores to fluctuate or worsen despite improving cross-entropy. Finetuning dataset size and distribution alignment significantly influence scaling behavior.

Conclusion: Provides practical insights for choosing pretraining data in transfer learning settings. Distribution alignment between pretraining and downstream data is crucial for predictable scaling behavior and optimal downstream performance.

Abstract: Scaling laws provide important insights that can guide the design of large language models (LLMs). Existing work has primarily focused on studying scaling laws for pretraining (upstream) loss. However, in transfer learning settings, in which LLMs are pretrained on an unsupervised dataset and then finetuned on a downstream task, we often also care about the downstream performance. In this work, we study the scaling behavior in a transfer learning setting, where LLMs are finetuned for machine translation tasks. Specifically, we investigate how the choice of the pretraining data and its size affect downstream performance (translation quality) as judged by: downstream cross-entropy and translation quality metrics such as BLEU and COMET scores. Our experiments indicate that the size of the finetuning dataset and the distribution alignment between the pretraining and downstream data significantly influence the scaling behavior. With sufficient alignment, both downstream cross-entropy and translation quality scores improve monotonically with more pretraining data. In such cases, we show that it is possible to predict the downstream translation quality metrics with good accuracy using a log-law. However, there are cases where moderate misalignment causes the downstream translation scores to fluctuate or get worse with more pretraining, whereas downstream cross-entropy monotonically improves. By analyzing these, we provide new practical insights for choosing appropriate pretraining data.

Method: Proposes EVA-Score which extracts all information from summaries, identifies overlapped information based on reference summaries, and calculates an information score to quantitatively measure informativeness.

Result: EVA-Score shows the highest correlation with human judgments compared to existing metrics on several datasets. Re-evaluation of LLMs on long-form summarization reveals they still lag behind human-written answers in information content.

Conclusion: EVA-Score provides a more effective way to evaluate abstractive long-form summarization by focusing on information content rather than surface similarity, with implications for improving LLM evaluation and future automatic assessment methods.

Abstract: Since LLMs emerged, more attention has been paid to abstractive long-form summarization, where longer input sequences indicate more information contained. Nevertheless, the automatic evaluation of such summaries remains underexplored. The current evaluation metrics for long-form summarization either use similarity-based metrics like ROUGE and BERTScore or LLM-based metrics using appropriate prompts or pre-defined schema. We argue that the former only relies on similarity and fails to consider informativeness while the latter lacks quantitative analysis of informative richness, and is rather subjective and hard to explain. Current evaluation metrics either use traditional metrics like ROUGE and BERTScore, which rely on surface-level similarity and fail to consider informativeness, or simple LLM-based metrics, which are not robust and easily overwhelmed by the long contexts. In this paper, we propose a new evaluation metric called EVA-Score to extract all information from the given summaries, identify overlapped information based on reference, and calculate the information score. We test EVA-Score on several datasets and the experimental results reveal that EVA-Score shows the highest correlation with humans. We also re-evaluate the performance of LLMs on long-form summarization from the information perspective. The results indicate that responses of LLMs still have a gap with the human-written answers. Moreover, we provide a detailed analysis of the effectiveness of EVA-Score, forecasting future ways to automatically evaluate abstractive long-form summarization.

Method: Extracts context-aware hidden states from intermediate layers of frozen LLMs to train data-efficient KGC classifiers. Bridges LLM-KG semantic gaps via subgraph-based entity descriptions and uses sliced mutual information to quantify task-relevant information in representations.

Result: Achieves 47% improvement over non-fine-tuned LLM baselines, and is the first to achieve fine-tuned performance with 188× memory efficiency and 26.11× speedup.

Conclusion: FLAME successfully addresses the trade-off between effectiveness and efficiency in LLM-based KGC, enabling fine-tuned performance without the computational costs of full fine-tuning.

Abstract: Traditional knowledge graph completion (KGC) methods rely solely on structural information and struggle with sparsity, while Large Language Models (LLMs) address these limitations through rich world knowledge and strong context modeling. Fine-tuning LLMs is effective but costly, while non-fine-tuned LLMs are efficient but suboptimal. To address this trade-off, we propose \textbf{FLAME}, a framework that extracts context-aware hidden states from intermediate layers of frozen LLMs to train data-efficient KGC classifiers. We bridge LLM-KG semantic gaps via subgraph-based entity descriptions and employ sliced mutual information (SMI) to quantify task-relevant information in representations. Experiments demonstrate that FLAME achieves 47% improvement over non-fine-tuned LLM baselines and, to our knowledge, is the first to achieve fine-tuned performance with $188\times$ memory efficiency and $26.11\times$ speedup.

Method: Developed Compound Question Synthesis (CQ-Syn) to create Compound-QA benchmark from existing QA datasets, annotated with proprietary LLMs and verified by humans. The benchmark includes five categories: Factual-Statement, Cause-and-Effect, Hypothetical-Analysis, Comparison-and-Selection, and Evaluation-and-Suggestion, evaluating LLMs across understanding, reasoning, and knowledge dimensions.

Result: Evaluation of nine open-source LLMs shows significantly lower performance on compound questions compared to non-compound questions. The paper explores enhancement strategies that substantially improve models’ comprehension and reasoning abilities on compound questions.

Conclusion: Compound-QA provides a valuable benchmark for evaluating LLMs on realistic complex questions, revealing current limitations and suggesting methods for improvement in handling compound reasoning tasks.

Abstract: Large language models (LLMs) demonstrate remarkable performance across various tasks, prompting researchers to develop diverse evaluation benchmarks. However, most benchmarks typically measure the ability of LLMs to respond to individual questions, neglecting the complex interactions in real-world applications. We introduce Compound Question Synthesis (CQ-Syn) to build Compound-QA, a benchmark targeting questions composed of multiple interrelated sub-questions. This benchmark is derived from existing QA datasets, annotated with proprietary LLMs, and verified by humans for accuracy. It encompasses five categories: Factual-Statement, Cause-and-Effect, Hypothetical-Analysis, Comparison-and-Selection, and Evaluation-and-Suggestion. It evaluates the LLM capability in terms of three dimensions, including understanding, reasoning, and knowledge. Evaluating nine open-source LLMs on Compound-QA reveals that their performance on compound questions is notably lower than on non-compound questions. We further explore strategies to enhance LLMs’ handling of compound questions, and our results show that these methods substantially improve models’ comprehension and reasoning abilities.

Method: Constructs and maintains a weighted graph of keywords and text labels with correlations as edges. Uses edge weighting mechanism to prioritize importance/reliability of extracted information. Dynamically retrieves relevant context using tailored minimum-cost spanning tree for each input.

Result: Empirical evaluations show GORAG outperforms existing approaches by providing more comprehensive and precise contextual information.

Conclusion: GORAG effectively addresses challenges in dynamic few-shot text classification through graph-based retrieval-augmented generation, offering better performance than existing methods.

Abstract: Text classification is vital for Web for Good applications like hate speech and misinformation detection. However, traditional models (e.g., BERT) often fail in dynamic few-shot settings where labeled data are scarce, and target labels frequently evolve. While Large Language Models (LLMs) show promise in few-shot settings, their performance is often hindered by increased input size in dynamic evolving scenarios. To address these issues, we propose GORAG, a Graph-based Online Retrieval-Augmented Generation framework for dynamic few-shot text classification. GORAG constructs and maintains a weighted graph of keywords and text labels, representing their correlations as edges. To model these correlations, GORAG employs an edge weighting mechanism to prioritize the importance and reliability of extracted information and dynamically retrieves relevant context using a tailored minimum-cost spanning tree for each input. Empirical evaluations show GORAG outperforms existing approaches by providing more comprehensive and precise contextual information. Our code is released at: https://github.com/Wyb0627/GORAG.

Method: Examines NLP problems as climate NLP tasks approximating greenwashing detection, including climate topic detection and deceptive pattern identification. Focuses on task formulation, dataset construction, and model evaluation methodologies.

Result: Reveals fragmented landscape: some subtasks achieve near-perfect performance in controlled settings, but tasks involving ambiguity, subjectivity, or reasoning remain challenging. No dataset of verified greenwashing cases currently exists.

Conclusion: Advancing automated greenwashing detection requires principled NLP methodologies combining reliable data annotations with interpretable model design. Future work should leverage third-party judgments and adopt decomposed pipelines supporting human oversight and traceable reasoning.

Abstract: Greenwashing refers to practices by corporations or governments that intentionally mislead the public about their environmental impact. This paper provides a comprehensive and methodologically grounded survey of natural language processing (NLP) approaches for detecting greenwashing in textual data, with a focus on corporate climate communication. Rather than treating greenwashing as a single, monolithic task, we examine the set of NLP problems, also known as climate NLP tasks, that researchers have used to approximate it, ranging from climate topic detection to the identification of deceptive communication patterns. Our focus is on the methodological foundations of these approaches: how tasks are formulated, how datasets are constructed, and how model evaluation influences reliability. Our review reveals a fragmented landscape: several subtasks now exhibit near-perfect performance under controlled settings, yet tasks involving ambiguity, subjectivity, or reasoning remain challenging. Crucially, no dataset of verified greenwashing cases currently exists. We argue that advancing automated greenwashing detection requires principled NLP methodologies that combine reliable data annotations with interpretable model design. Future work should leverage third-party judgments, such as verified media reports or regulatory records, to mitigate annotation subjectivity and legal risk, and adopt decomposed pipelines that support human oversight, traceable reasoning, and efficient model design.

Method: Retrieval-And-Structuring (RAS) framework that interleaves targeted retrieval planning with incremental graph construction, building question-specific knowledge graphs through iterative retrieval.

Result: On seven knowledge-intensive benchmarks, RAS outperforms strong baselines with up to 8.7% gains with proprietary LLMs and 7.0% gains with open-source LLMs.

Conclusion: Dynamic, question-specific knowledge structuring offers a robust path to improving reasoning accuracy and robustness in language model generation.

Abstract: Large language models (LLMs) have achieved impressive performance on knowledge-intensive tasks, yet they often struggle with multi-step reasoning due to the unstructured nature of retrieved context. While retrieval-augmented generation (RAG) methods provide external information, the lack of explicit organization among retrieved passages limits their effectiveness, leading to brittle reasoning pathways. Recent interpretability studies highlighting the importance of structured intermediate reasoning further align with this perspective. We propose Retrieval-And-Structuring (RAS), a framework that dynamically constructs question-specific knowledge graphs through iterative retrieval and structured knowledge building. RAS interleaves targeted retrieval planning with incremental graph construction, enabling models to assemble and reason over evolving knowledge structures tailored to each query. On seven knowledge-intensive benchmarks, RAS consistently outperforms strong baselines, achieving up to 8.7% and 7.0% gains with proprietary and open-source LLMs, respectively. Our results demonstrate that dynamic, question-specific knowledge structuring offers a robust path to improving reasoning accuracy and robustness in language model generation.

Method: Proposes Layer-wise RAG (L-RAG) that leverages intermediate representations from middle layers of LLMs (which capture next-hop information) to retrieve external knowledge, rather than generating new internal queries iteratively.

Result: Outperforms existing RAG methods on multi-hop QA datasets (MuSiQue, HotpotQA, 2WikiMultiHopQA) while maintaining inference overhead similar to standard RAG.

Conclusion: L-RAG provides an efficient alternative to iterative RAG methods for multi-hop reasoning by exploiting intermediate layer representations, achieving strong performance with lower computational cost.

Abstract: Retrieval-augmented generation (RAG) encounters challenges when addressing complex queries, particularly multi-hop questions. While several methods tackle multi-hop queries by iteratively generating internal queries and retrieving external documents, these approaches are computationally expensive. In this paper, we identify a three-stage information processing pattern in LLMs during layer-by-layer reasoning, consisting of extraction, processing, and subsequent extraction steps. This observation suggests that the representations in intermediate layers contain richer information compared to those in other layers. Building on this insight, we propose Layer-wise RAG (L-RAG). Unlike prior methods that focus on generating new internal queries, L-RAG leverages intermediate representations from the middle layers, which capture next-hop information, to retrieve external knowledge. L-RAG achieves performance comparable to multi-step approaches while maintaining inference overhead similar to that of standard RAG. Experimental results show that L-RAG outperforms existing RAG methods on open-domain multi-hop question-answering datasets, including MuSiQue, HotpotQA, and 2WikiMultiHopQA. The code is available in https://github.com/Olive-2019/L-RAG

Method: Three-pronged approach: (1) explicit evaluation of LLM-generated attacks on mental health groups, (2) network-based framework to study bias propagation, and (3) assessment of stigmatization using established frameworks. Analyzed a large-scale bias audit dataset using network centrality measures (closeness centrality) and clustering analysis (Gini coefficient).

Result: Mental health entities occupy central positions in attack narrative networks with significantly higher mean closeness centrality (p=4.06e-10) and dense clustering (Gini=0.7). Increased labeling components for mental health disorder-related targets relative to initial targets in generation chains, indicating heightened stigmatization.

Conclusion: LLMs have structural predilections to heighten harmful discourse against vulnerable mental health populations, highlighting the need for mitigation approaches to address these biases in language models.

Abstract: Large Language Models (LLMs) have been shown to demonstrate imbalanced biases against certain groups. However, the study of unprovoked targeted attacks by LLMs towards at-risk populations remains underexplored. Our paper presents three novel contributions: (1) the explicit evaluation of LLM-generated attacks on highly vulnerable mental health groups; (2) a network-based framework to study the propagation of relative biases; and (3) an assessment of the relative degree of stigmatization that emerges from these attacks. Our analysis of a recently released large-scale bias audit dataset reveals that mental health entities occupy central positions within attack narrative networks, as revealed by a significantly higher mean centrality of closeness (p-value = 4.06e-10) and dense clustering (Gini coefficient = 0.7). Drawing from an established stigmatization framework, our analysis indicates increased labeling components for mental health disorder-related targets relative to initial targets in generation chains. Taken together, these insights shed light on the structural predilections of large language models to heighten harmful discourse and highlight the need for suitable approaches for mitigation.

Method: Parameter-free, architecture-agnostic approach that compresses increasing KV cache in frequency domain. Based on observation that context information concentrates in low-frequency components. Iteratively applies frequency-domain compression during inference.

Result: Extends LLaMA-2-7B context window to 256K tokens with stable perplexity using minimal training at 8K length. Outperforms existing KV cache compression methods on LLaMA-2 and LLaMA-3 across prefilling and decoding tasks.

Conclusion: FreqKV enables robust context window extension and is effective for both understanding and generation in long contexts, providing efficient KV cache compression without losing critical information.

Abstract: Existing key-value (KV) cache compression methods for large language models (LLMs) often rely on token eviction, which risks losing critical local information in both long prefilling and decoding scenarios. When extrapolating beyond the pretrained context length, their performance degrades sharply on long-context benchmarks. Motivated by the observation in the frequency domain that the context information is concentrated in the low-frequency components, we propose FreqKV, a parameter-free and architecture-agnostic approach. It iteratively compresses the increasing KV cache in the frequency domain, allowing models to process lengthy contexts efficiently. With minimal training at 8K length, FreqKV extends the context window of LLaMA-2-7B up to 256K tokens while maintaining stable perplexity. Extensive experiments across prefilling and decoding demonstrate that FreqKV enables robust context window extension and consistently outperforms existing KV cache compression methods on LLaMA-2 and LLaMA-3, highlighting its effectiveness for both understanding and generation in long contexts.

Method: TRACE uses reinforcement learning to guide LLMs to produce structured outputs with explicit evidence citations. It employs prompting and rewards for evidence relevance, proper formatting, and accuracy, with adaptive reward merging and stabilized KL-divergence estimation for training stability.

Result: Experiments on three multi-hop QA datasets show TRACE achieves transparent, evidence-attributed outputs with 10-30% accuracy improvements, comparable to advanced commercial LLMs like OpenAI o1 and DeepSeek-R1, with strong generalization to unseen tasks.

Conclusion: TRACE successfully enhances evidence traceability in RAG systems through RL, achieving both improved transparency and accuracy while maintaining training stability and strong generalization capabilities.

Abstract: Retrieval-Augmented Generation (RAG) delivers substantial value in knowledge-intensive applications. However, its generated responses often lack transparent reasoning paths that trace back to source evidence from retrieved documents. This opacity not only compromises the interpretability of the output but also limits the model’s ability to fully exploit the provided context. To address this, we propose TRACE (Transparent RAG with evidenCE tracing), a framework designed to enhance evidence traceability in Large Language Models (LLMs) through reinforcement learning (RL). TRACE guides LLMs to produce structured outputs with explicit evidence citations by prompting and rewarding evidence relevance and proper formatting, alongside accuracy, to optimize structured traceability. To ensure training stability with multiple reward signals, we further introduce an adaptive strategy for merging rewards and adopt a stabilized KL-divergence estimator. Experiments on three multi-hop QA datasets using Qwen2.5-7B-Instruct and Llama-3.1-8B-Instruct show that TRACE achieves both transparent, evidence-attributed outputs and accuracy improvements of 10-30%. The resulting performance is comparable to advanced commercial LLMs (e.g., OpenAI o1, DeepSeek-R1). Further analyses demonstrate strong generalization capabilities to unseen tasks. Our code is publicly available now.

Method: Three components: 1) EvoSA - annotation-free data augmentation combining evolutionary operations with speech-act-aware prompting, 2) teacher-model-generated complementary-task supervision, 3) Complementary-Convergent Distillation for phase-wise knowledge transfer to student models. Constructed ReaMent dataset with 5,000 real-world dialogues.

Result: MentalMAD improves accuracy by 14.0%, macro-F1 by 27.3%, and weighted F1 by 15.1% over strongest baseline. Code and dataset publicly available.

Conclusion: The proposed framework effectively addresses challenges in mental manipulation detection through innovative data augmentation and knowledge distillation techniques.

Abstract: Mental manipulation on social media poses a covert yet serious threat to individuals’ psychological well-being and the integrity of online interactions. Detecting such behavior is challenging due to the difficult-to-annotate training data, its highly covert and multi-turn nature, and the lack of real-world datasets. To address these challenges, we propose MentalMAD, a framework that enhances large language models for mental manipulation detection. Our approach consists of three key components: EvoSA, an annotation-free data augmentation method that combines evolutionary operations with speech-act-aware prompting; teacher-model-generated complementary-task supervision; and Complementary-Convergent Distillation, a phase-wise strategy for transferring manipulation-specific knowledge to student models. We then constructed the ReaMent dataset, comprising 5,000 real-world-sourced dialogues. Extensive experiments show that MentalMAD improves accuracy by 14.0%, macro-F1 by 27.3%, and weighted F1 by 15.1% over the strongest baseline. The code and the dataset are publicly available at https://github.com/Yuansheng-Gao/MentalMAD.

Method: Proposes a pairwise-comparison framework for assessing textual creativity using shared contextual instructions to improve consistency. Introduces CreataSet dataset with 100K+ human-level and 1M+ synthetic creative instruction-response pairs across diverse open-domain tasks. Trains an LLM-based evaluator named CrEval on this dataset.

Result: CrEval demonstrates remarkable superiority over existing methods in alignment with human judgments. Experiments show the importance of integrating both human and synthetic data for training robust evaluators, and CrEval proves practically useful for boosting LLM creativity.

Conclusion: The proposed framework and CrEval evaluator address key challenges in automated creativity assessment, providing an effective solution that aligns with human judgment and can enhance LLM creativity development.

Abstract: Creativity evaluation remains a challenging frontier for large language models (LLMs). Current evaluations heavily rely on inefficient and costly human judgments, hindering progress in enhancing machine creativity. While automated methods exist, ranging from psychological testing to heuristic- or prompting-based approaches, they often lack generalizability or alignment with human judgment. To address these issues, we propose a novel pairwise-comparison framework for assessing textual creativity that leverages shared contextual instructions to improve evaluation consistency. We introduce CreataSet, a large-scale dataset with 100K+ human-level and 1M+ synthetic creative instruction-response pairs spanning diverse open-domain tasks. Through training on CreataSet, we develop an LLM-based evaluator named CrEval. CrEval demonstrates remarkable superiority over existing methods in alignment with human judgments. Experimental results underscore the indispensable significance of integrating both human and synthetic data to train highly robust evaluators, and showcase the practical utility of CrEval in boosting the creativity of LLMs.

Method: Developed a GRPO (Group Relative Policy Optimization) approach using format correctness and answer length as surrogate reward signals instead of traditional ground truth answers, combining format-only rewards for early phases with length-based rewards for later phases.

Result: The format-length surrogate approach matched standard GRPO performance in early phases and surpassed it in certain scenarios, achieving 40.0% accuracy on AIME2024 with a 7B base model, demonstrating that good answering habits can unlock existing reasoning capabilities.

Conclusion: Format and length can serve as effective surrogate signals for training LLMs in mathematical problem-solving, reducing dependence on extensive ground truth data while revealing that base models already possess reasoning skills that just need proper answering habits to be unlocked.

Abstract: Large Language Models have achieved remarkable success in natural language processing tasks, with Reinforcement Learning playing a key role in adapting them to specific applications. However, obtaining ground truth answers for training LLMs in mathematical problem-solving is often challenging, costly, and sometimes unfeasible. This research delves into the utilization of format and length as surrogate signals to train LLMs for mathematical problem-solving, bypassing the need for traditional ground truth answers. Our study shows that a reward function centered on format correctness alone can yield performance improvements comparable to the standard GRPO algorithm in early phases. Recognizing the limitations of format-only rewards in the later phases, we incorporate length-based rewards. The resulting GRPO approach, leveraging format-length surrogate signals, not only matches but surpasses the performance of the standard GRPO algorithm relying on ground truth answers in certain scenarios, achieving 40.0% accuracy on AIME2024 with a 7B base model. Through systematic exploration and experimentation, this research not only offers a practical solution for training LLMs to solve mathematical problems and reducing the dependence on extensive ground truth data collection, but also reveals the essence of why our label-free approach succeeds: the powerful base model is like an excellent student who has already mastered mathematical and logical reasoning skills, but performs poorly on the test paper, it simply needs to develop good answering habits to achieve outstanding results in exams, to unlock the capabilities it already possesses.

Method: Introduces graph probing to uncover functional connectivity of LLM neurons and relate it to language generation performance. Uses simple linear or MLP probes on neural topology across diverse LLM families and scales, identifies default networks and hub neurons, and conducts interventional experiments.

Result: Neural topology outperforms neural activation by up to 130.4% on perplexity and 67.7% on semantic regression. Performance remains predictable with just 1% of neuron connections. Identified default networks and hub neurons provide causal evidence that LLMs exploit topological information.

Conclusion: Graph probing reveals that neural topology contains orders of richer information about LLM performance than activations, enabling practical applications in model efficiency (pruning) and reliability (hallucination detection) while advancing understanding of LLM internal mechanisms.

Abstract: Probing large language models (LLMs) has yielded valuable insights into their internal mechanisms by linking neural activations to interpretable semantics. However, the complex mechanisms that link neuron’s functional co-activation with the emergent model capabilities remains largely unknown, hindering a deeper understanding and safer development of LLMs. In this work, we introduce graph probing, a method for uncovering the functional connectivity of LLM neurons and relating it to language generation performance. By probing models across diverse LLM families and scales, we discover a universal predictability of language generation and understanding performance using only neural topology, which persists even when retaining just 1% of neuron connections. Strikingly, probing on topology outperforms probing on activation by up to 130.4% and 67.7% on perplexity and space/time semantic regression respectively, suggesting that neural topology contains orders of richer information of LLM performance than neural activation, which can be easily extracted with simple linear or MLP probes. To explain the dependence between neural topology and language performance, we identify default networks and hub neurons in LLMs and provide causal evidence by interventional experiments on multiple benchmarks, showing that LLMs actually exploit these topological information. Further analyses suggest that graph probing can be effectively leveraged to improve the efficiency and reliability of LLMs through proof-of-concept applications in model pruning and hallucination detection. Codes and data for the graph probing toolbox are available at https://github.com/DavyMorgan/llm-graph-probing.

Method: NeuroFaith identifies key concepts within explanations and mechanistically tests whether these concepts actually influence the model’s predictions. It includes a linear faithfulness probe to detect unfaithful self-explanations from representation space and improve faithfulness through steering.

Result: The framework demonstrates versatility across 2-hop reasoning and classification tasks, providing a principled approach to evaluating and enhancing faithfulness of LLM free text self-explanations.

Conclusion: NeuroFaith addresses critical needs for trustworthy AI systems by providing tools to measure and improve the faithfulness of LLM self-explanations, moving beyond behavioral tests to examine internal representations.

Abstract: Large Language Models (LLMs) can generate plausible free text self-explanations to justify their answers. However, these natural language explanations may not accurately reflect the model’s actual reasoning process, pinpointing a lack of faithfulness. Existing faithfulness evaluation methods rely primarily on behavioral tests or computational block analysis without examining the semantic content of internal neural representations. This paper proposes NeuroFaith, a flexible framework that measures the faithfulness of LLM free text self-explanation by identifying key concepts within explanations and mechanistically testing whether these concepts actually influence the model’s predictions. We show the versatility of NeuroFaith across 2-hop reasoning and classification tasks. Additionally, we develop a linear faithfulness probe based on NeuroFaith to detect unfaithful self-explanations from representation space and improve faithfulness through steering. NeuroFaith provides a principled approach to evaluating and enhancing the faithfulness of LLM free text self-explanations, addressing critical needs for trustworthy AI systems.

Method: StorySim uses a programmable framework with a highly controllable Storyboard to generate synthetic stories. It enables precise manipulation of character perspectives and events to design first- and second-order ToM tasks alongside WM tasks. The framework systematically controls for mental state tracking abilities and generates novel story prompts to avoid data contamination.

Result: Experiments across state-of-the-art LLMs show: 1) Most models perform better on WM tasks than ToM tasks, 2) Models perform better reasoning with humans compared to inanimate objects, 3) Evidence of heuristic behaviors like recency bias and over-reliance on earlier story events. The framework successfully revealed systematic patterns in model reasoning capabilities.

Conclusion: StorySim provides a valuable tool for evaluating LLM reasoning capabilities, particularly for Theory of Mind and World Modeling. The framework’s programmable nature allows for precise evaluation design and reveals important patterns in model behavior, including systematic biases and differential performance across task types.

Abstract: We introduce $\texttt{StorySim}$, a programmable framework for synthetically generating stories to evaluate the theory of mind (ToM) and world modeling (WM) capabilities of large language models (LLMs). Unlike prior benchmarks that may suffer from contamination in pretraining data, $\texttt{StorySim}$ produces novel, compositional story prompts anchored by a highly controllable $\texttt{Storyboard}$, enabling precise manipulation of character perspectives and events. We use this framework to design first- and second-order ToM tasks alongside WM tasks that control for the ability to track and model mental states. Our experiments across a suite of state-of-the-art LLMs reveal that most models perform better on WM tasks than ToM tasks, and that models tend to perform better reasoning with humans compared to inanimate objects. Additionally, our framework enabled us to find evidence of heuristic behavior such as recency bias and an over-reliance on earlier events in the story. All code for generating data and evaluations is freely available.

Method: Two-step approach: 1) Simple character-level mapping from Hebrew to Arabic script, 2) Post-correction to address grammatical and orthographic errors. Also benchmarked LLMs on this task.

Result: Successfully transliterated Judeo-Arabic into Arabic script, enabling Arabic NLP tools to perform morphosyntactic tagging and machine translation that were previously infeasible on original texts.

Conclusion: Transliteration enables application of Arabic NLP tools to Judeo-Arabic texts, with code and data made publicly available for further research.

Abstract: Judeo-Arabic refers to Arabic variants historically spoken by Jewish communities across the Arab world, primarily during the Middle Ages. Unlike standard Arabic, it is written in Hebrew script by Jewish writers and for Jewish audiences. Transliterating Judeo-Arabic into Arabic script is challenging due to ambiguous letter mappings, inconsistent orthographic conventions, and frequent code-switching into Hebrew. In this paper, we introduce a two-step approach to automatically transliterate Judeo-Arabic into Arabic script: simple character-level mapping followed by post-correction to address grammatical and orthographic errors. We also present the first benchmark evaluation of LLMs on this task. Finally, we show that transliteration enables Arabic NLP tools to perform morphosyntactic tagging and machine translation, which would have not been feasible on the original texts. We make our code and data publicly available.

Method: Proposes EMSEdit with three key components: 1) Multi-step backpropagation (MSBP) to capture gradient-activation mapping patterns, 2) Multi-step edits per sample for better performance with limited data, and 3) Norm-based regularization to preserve unedited knowledge and improve training efficiency.

Result: Experiments on two datasets and three LLMs show EMSEdit consistently outperforms state-of-the-art methods in both sequential and batch editing. MSBP can be integrated into existing approaches for additional gains, and EMSEdit demonstrates robustness in multi-hop reasoning editing tasks.

Conclusion: EMSEdit provides an efficient and effective solution for model editing in LLMs, particularly valuable in low-data scenarios, with components that can enhance existing editing methods.

Abstract: Large Language Models (LLMs) power numerous AI applications, yet updating their knowledge remains costly. Model editing provides a lightweight alternative through targeted parameter modifications, with meta-learning-based model editing (MLME) demonstrating strong effectiveness and efficiency. However, we find that MLME struggles in low-data regimes and incurs high training costs due to the use of KL divergence. To address these issues, we propose $\textbf{E}$fficient $\textbf{M}$ulti-$\textbf{S}$tep $\textbf{Edit (EMSEdit)}$, which leverages multi-step backpropagation (MSBP) to effectively capture gradient-activation mapping patterns within editing samples, performs multi-step edits per sample to enhance editing performance under limited data, and introduces norm-based regularization to preserve unedited knowledge while improving training efficiency. Experiments on two datasets and three LLMs show that EMSEdit consistently outperforms state-of-the-art methods in both sequential and batch editing. Moreover, MSBP can be seamlessly integrated into existing approaches to yield additional performance gains. Further experiments on a multi-hop reasoning editing task demonstrate EMSEdit’s robustness in handling complex edits, while ablation studies validate the contribution of each design component. Our code is available at https://github.com/xpq-tech/emsedit.

Method: Represent each reasoning step via spectral analysis of token-level embeddings, cluster them into semantically coherent latent states, and model their progression as a Markov chain to capture the global structure of reasoning.

Result: The framework provides structured and interpretable views of reasoning processes, supporting semantic role identification, temporal pattern visualization, and consistency evaluation.

Conclusion: The state-aware transition framework offers a novel approach to understanding CoT reasoning dynamics at a higher semantic level, moving beyond token-level analysis to capture the structured progression of reasoning steps.

Abstract: Recent advances in chain-of-thought (CoT) prompting have enabled large language models (LLMs) to perform multi-step reasoning. However, the explainability of such reasoning remains limited, with prior work primarily focusing on local token-level attribution, such that the high-level semantic roles of reasoning steps and their transitions remain underexplored. In this paper, we introduce a state-aware transition framework that abstracts CoT trajectories into structured latent dynamics. Specifically, to capture the evolving semantics of CoT reasoning, each reasoning step is represented via spectral analysis of token-level embeddings and clustered into semantically coherent latent states. To characterize the global structure of reasoning, we model their progression as a Markov chain, yielding a structured and interpretable view of the reasoning process. This abstraction supports a range of analyses, including semantic role identification, temporal pattern visualization, and consistency evaluation.

Method: Verbalized Algorithms decompose tasks into elementary natural language operations that LLMs can handle reliably, using LLMs as binary comparison oracles within well-analyzed classical algorithms. Examples include verbalized sorting (bitonic sorting network), verbalized maximum, verbalized clustering, and verbalized submodular maximization.

Result: The approach provides theoretical guarantees: O(n) runtime for maximum, O(n log n) for sorting, and 1/(1-e) optimality for submodular maximization. Clustering and submodular maximization outperformed or improved nearest neighbor search using state-of-the-art embedding models.

Conclusion: Verbalized Algorithms offer a principled way to combine LLMs with classical algorithms, providing theoretical guarantees while leveraging LLMs’ natural language understanding capabilities for specific elementary operations.

Abstract: Instead of querying LLMs in a one-shot manner and hoping to get the right answer for a reasoning task, we propose a paradigm we call \emph{verbalized algorithms} (VAs), which combines LLMs with classical algorithms with established theoretical guarantees. VAs decompose a task into simple elementary operations on natural language strings that LLMs are able to answer reliably, and limit the scope of LLMs to those simple tasks. For example, for sorting a series of natural language strings, \emph{verbalized sorting} uses an LLM as a binary comparison oracle in a known and well-analyzed sorting algorithm (e.g., bitonic sorting network). Although this is already known as \emph{pairwise ranking} in the literature, we additionally demonstrate the effectiveness of \emph{verbalized maximum}, \emph{verbalized clustering}, and \emph{verbalized submodular maximization} for numerical reasoning, topic clustering and multi-hop Q&A RAG task, which guarantees $O(n)$ runtime, $O(n \log n)$ runtime, and $1/(1-e)$ optimality, respectively. Clustering and submodular maximization outperformed or improved the nearest neighbor search using state-of-the-art embedding models.

Method: Empirical study of SFT with varying learning rates, theoretical analysis of the trade-off, and proposal of Token-Adaptive Loss Reweighting (TALR). Also evaluated L2 regularization, LoRA, model averaging, and FLOW as comparative baselines.

Result: Smaller learning rates substantially mitigate general performance degradation while preserving target-domain performance. TALR consistently outperforms other methods in balancing domain-specific gains and general capabilities, though no method completely eliminates the trade-off.

Conclusion: Two practical guidelines: (1) use small learning rates for favorable trade-off, (2) adopt TALR when stronger balance is needed. The paper provides both empirical evidence and theoretical understanding of the SFT trade-off.

Abstract: Supervised Fine-Tuning (SFT) on domain-specific datasets is a common approach to adapt Large Language Models (LLMs) to specialized tasks but is often believed to degrade their general capabilities. In this work, we revisit this trade-off and present both empirical and theoretical insights. First, we show that SFT does not always hurt: using a smaller learning rate can substantially mitigate general performance degradation while preserving comparable target-domain performance. We then provide a theoretical analysis that explains these phenomena and further motivates a new method, Token-Adaptive Loss Reweighting (TALR). Building on this, and recognizing that smaller learning rates alone do not fully eliminate general-performance degradation in all cases, we evaluate a range of strategies for reducing general capability loss, including L2 regularization, LoRA, model averaging, FLOW, and our proposed TALR. Experimental results demonstrate that while no method completely eliminates the trade-off, TALR consistently outperforms these baselines in balancing domain-specific gains and general capabilities. Finally, we distill our findings into practical guidelines for adapting LLMs to new domains: (i) using a small learning rate to achieve a favorable trade-off, and (ii) when a stronger balance is further desired, adopt TALR as an effective strategy.

Method: Meta-learning framework that generates adapter parameters from context with compositional structure, allowing algebraic merging of adapters from different context chunks. Features reversible encoding for safety and context recovery.

Result: Outperforms ICL and prior generator-based methods on diverse multiple-choice and extractive QA tasks, especially when scaling to more inputs. Provides lower inference cost, improved stability with long contexts, and principled solution for inputs exceeding context window.

Conclusion: Composable adapter generation offers a practical and efficient alternative for scaling LLM deployment, bridging the gap between ICL and SFT with benefits in efficiency, stability, and safety.

Abstract: Large language models (LLMs) often seamlessly adapt to new tasks through in-context learning (ICL) or supervised fine-tuning (SFT). However, ICL is inefficient when handling many demonstrations, and SFT incurs training overhead while sacrificing flexibility. Mapping instructions or demonstrations from context directly into adapter parameters offers an appealing alternative. While prior work explored generating adapters based on a single input context, it has overlooked the need to integrate multiple chunks of information. To address this gap, we introduce CompAs, a meta-learning framework that translates context into adapter parameters with a compositional structure that allows them to be merged algebraically. This approach yields three benefits: lower inference cost, improved stability under long contexts, and establishes a principled solution when input exceeds the model’s context window. Furthermore, CompAs reversibly encodes information into adapter parameters, enabling recovery of the original input context and facilitating safety. Empirical results on diverse multiple-choice and extractive question answering tasks show that CompAs outperforms ICL and prior generator-based methods, especially when scaling to more inputs. Our work establishes composable adapter generation as a practical and efficient alternative for scaling LLM deployment.

Method: Introduces a simple, training-free method to identify and manipulate these cross-lingual dimensions using only 50 sentences of either parallel or monolingual data. The method identifies sparse dimensions that control language switching in intermediate-to-final layers.

Result: Experiments on multilingual generation control show the dimensions are interpretable - interventions can switch output language while preserving semantic content. The method surpasses prior neuron-based approaches at substantially lower cost.

Conclusion: Cross-lingual transitions in LLMs are governed by sparse, interpretable dimensions that can be manipulated for controlled language switching, offering a low-cost alternative to prior methods.

Abstract: Large language models exhibit strong multilingual capabilities despite limited exposure to non-English data. Prior studies show that English-centric large language models map multilingual content into English-aligned representations at intermediate layers and then project them back into target-language token spaces in the final layer. From this observation, we hypothesize that this cross-lingual transition is governed by a small and sparse set of dimensions, which occur at consistent indices across the intermediate to final layers. Building on this insight, we introduce a simple, training-free method to identify and manipulate these dimensions, requiring only as few as 50 sentences of either parallel or monolingual data. Experiments on a multilingual generation control task reveal the interpretability of these dimensions, demonstrating that the interventions in these dimensions can switch the output language while preserving semantic content, and that it surpasses the performance of prior neuron-based approaches at a substantially lower cost.

Method: The authors develop RefDiv (reference-guided diversity reduction protocol) as a diagnostic attack to stress test TTS pipelines. They conduct extensive experiments across open-source models (Qwen3, Mistral, Llama3.1, Gemma3) and TTS strategies (Monte Carlo Tree Search and Best-of-N), and also test closed-source models (OpenAI o3-mini and Gemini-2.5-Pro). They evaluate safety guardrail classifiers like Llama-Guard against adversarial prompts generated by RefDiv.

Result: Constraining diversity consistently increases unsafe output rates in TTS, often more effectively than direct adversarial prompts. The vulnerability transfers across TTS strategies and model types. Existing safety classifiers fail to detect RefDiv-generated adversarial inputs, showing limited protection against this diversity-driven failure mode.

Conclusion: TTS has a fundamental vulnerability where reduced candidate diversity leads to increased unsafe outputs, which current safety measures cannot adequately address. This represents a general property of TTS systems rather than a model-specific artifact.

Abstract: Test-Time Scaling (TTS) improves LLM reasoning by exploring multiple candidate responses and then operating over this set to find the best output. A tacit premise behind TTS is that sufficiently diverse candidate pools enhance reliability. In this work, we show that this assumption in TTS introduces a previously unrecognized failure mode. When candidate diversity is curtailed, even by a modest amount, TTS becomes much more likely to produce unsafe outputs. We present a reference-guided diversity reduction protocol (RefDiv) that serves as a diagnostic attack to stress test TTS pipelines. Through extensive experiments across open-source models (e.g. Qwen3, Mistral, Llama3.1, Gemma3) and two widely used TTS strategies (Monte Carlo Tree Search and Best-of-N), constraining diversity consistently signifies the rate at which TTS produces unsafe results. The effect is often stronger than that produced by prompts directly with high adversarial intent scores. This observed phenomenon also transfers across TTS strategies and to closed-source models (e.g. OpenAI o3-mini and Gemini-2.5-Pro), thus indicating that this is a general and extant property of TTS rather than a model-specific artifact. Additionally, we find that numerous widely used safety guardrail classifiers (e.g. Llama-Guard), are unable to flag the adversarial input prompts generated by RefDiv, demonstrating that existing defenses offer limited protection against this diversity-driven failure mode.

Method: Uses a hierarchically modular architecture with independent MCP servers for components like skeleton initialization, digest construction, and skeleton refinement. A planner agent dynamically orchestrates workflow based on MCP tool descriptions and execution history.

Result: Human evaluations show the system surpasses representative baselines in both content depth and length, demonstrating the strength of MCP-based modular planning.

Conclusion: The modular MCP-based approach enables effective long-form survey generation with human-in-the-loop control and customization capabilities.

Abstract: We introduce LLM x MapReduce-V3, a hierarchically modular agent system designed for long-form survey generation. Building on the prior work, LLM x MapReduce-V2, this version incorporates a multi-agent architecture where individual functional components, such as skeleton initialization, digest construction, and skeleton refinement, are implemented as independent model-context-protocol (MCP) servers. These atomic servers can be aggregated into higher-level servers, creating a hierarchically structured system. A high-level planner agent dynamically orchestrates the workflow by selecting appropriate modules based on their MCP tool descriptions and the execution history. This modular decomposition facilitates human-in-the-loop intervention, affording users greater control and customization over the research process. Through a multi-turn interaction, the system precisely captures the intended research perspectives to generate a comprehensive skeleton, which is then developed into an in-depth survey. Human evaluations demonstrate that our system surpasses representative baselines in both content depth and length, highlighting the strength of MCP-based modular planning.

Method: NOSA is a trainable sparse attention mechanism that explicitly constrains CPU-GPU KV transfer volume. It’s designed natively for KV cache offloading, enabling low communication overhead. The authors also build NOSI, a KV cache offloading inference system to fully utilize NOSA’s efficiency.

Result: NOSA outperforms KV cache offloading baselines on general, long-input, and long-generation tasks. It boosts decoding throughput by up to 5.04x over FullAttn, 1.92x over InfLLMv2, and 1.83x over ShadowKV on 1-8B LLMs.

Conclusion: NOSA provides an effective solution for KV cache offloading that maintains generation quality while significantly improving decoding throughput through constrained sparse attention patterns optimized for CPU-GPU transfers.

Abstract: Decoding throughput improvements from larger inference batches are limited by GPU memory, which is largely consumed by the key-value (KV) cache. Prior training-free KV cache offloading alleviates this by keeping redundant context on the CPU and fetching only a sparse subset for attention, but it often degrades long-generation quality due to training-inference mismatch on sparse patterns. Meanwhile, trainable sparse attention is incompatible with efficient offloading, as unconstrained KV accesses may force large CPU-to-GPU transfers and erase throughput gains. To this end, we propose NOSA, a trainable sparse attention mechanism natively designed for KV cache offloading. NOSA explicitly constrains the volume of CPU-GPU KV transfers, thereby achieving low communication overhead and high decoding throughput. We further build NOSI, a KV cache offloading inference system that fully unlocks NOSA’s efficiency. Empirical results on 1,3,8B LLMs demonstrate that NOSA outperforms KV cache offloading baselines on general, long-input, and long-generation tasks, while boosting decoding throughput by up to 5.04x, 1.92x, and 1.83x over FullAttn, InfLLMv2, and ShadowKV, respectively. We release our code at https://github.com/thunlp/NOSA.

Method: LiRA combines Arca (Anchored Representation Composition Architecture) for aligning low-resource inputs to English semantic space through anchor-based alignment and collaborative encoding, with LaSR (Language-coupled Semantic Reasoner) for consistency regularization and unified cross-lingual understanding.

Result: The framework demonstrates consistent improvements across diverse low-resource benchmarks for retrieval, ranking, QA, and reasoning tasks, with theoretical guarantees on representation stability.

Conclusion: LiRA provides an effective plug-and-play solution for enhancing LLM performance on low-resource languages with theoretical guarantees and practical improvements.

Abstract: Large language models (LLMs) continue to struggle with low-resource languages, primarily due to limited training data, translation noise, and unstable cross-lingual alignment. To address these challenges, we propose LiRA (Linguistic Robust Anchoring for LLMs)-a plug-and-play framework that requires only lightweight fine-tuning on top of existing pretrained backbones. LiRA jointly optimizes representation stability and cross-lingual semantic consistency by combining two key components: Arca (Anchored Representation Composition Architecture), which aligns low-resource inputs to a shared English semantic space through anchor-based alignment and collaborative encoding; and LaSR (Language-coupled Semantic Reasoner), a lightweight, language-aware head that enforces consistency regularization for unified cross-lingual understanding, retrieval, and reasoning. We theoretically show that under controlled anchoring error and translation-induced bias, LiRA guarantees bounded representation deviation and stable downstream performance under local Lipschitz continuity. To facilitate research, we release a new multilingual product retrieval dataset covering five Southeast Asian and two South Asian languages. Extensive experiments across diverse low-resource benchmarks demonstrate consistent improvements in retrieval, ranking, question answering, and reasoning tasks. Code will be publicly available on GitHub, and the dataset will be hosted on Hugging Face.

Method: MOSAIC uses a multi-stage framework with joint domain-specific masked supervision, combining masked language modeling (MLM) and contrastive objectives within a unified training pipeline for effective domain adaptation.

Result: The method achieves improvements up to 13.4% in NDCG@10 over strong general-domain baselines, validated on both high-resource and low-resource domains through comprehensive ablation studies.

Conclusion: The framework effectively enables domain adaptation of text embedding models through balanced joint supervision and staged adaptation, demonstrating the importance of combining MLM and contrastive objectives.

Abstract: We introduce MOSAIC (Masked Objective with Selective Adaptation for In-domain Contrastive learning), a multi-stage framework for domain adaptation of text embedding models that incorporates joint domain-specific masked supervision. Our approach addresses the challenges of adapting large-scale general-domain text embedding models to specialized domains. By jointly optimizing masked language modeling (MLM) and contrastive objectives within a unified training pipeline, our method enables effective learning of domain-relevant representations while preserving the robust semantic discrimination properties of the original model. We empirically validate our approach on both high-resource and low-resource domains, achieving improvements up to 13.4% in NDCG@10 (Normalized Discounted Cumulative Gain) over strong general-domain baselines. Comprehensive ablation studies further demonstrate the effectiveness of each component, highlighting the importance of balanced joint supervision and staged adaptation.

Method: Conducted systematic study of top-performing regression-based and LLM-as-a-Judge QE metrics across 10 diverse language pairs, identified two critical length biases, investigated root causes through analysis of supervision distributions, and applied length normalization during training as a diagnostic intervention.

Result: Found that QE metrics consistently over-predict errors with increasing translation length (even for error-free texts) and exhibit preference for shorter translations when multiple candidates are available. Length normalization during training effectively decouples error probability from length and counteracts data skew.

Conclusion: QE metrics have inherent length biases that risk unfairly penalizing longer correct translations and can lead to suboptimal decision-making in applications like QE reranking and reinforcement learning. Simple length normalization during training can mitigate these biases and yield more reliable QE signals.

Abstract: Quality Estimation (QE) metrics are vital in machine translation for reference-free evaluation and as a reward signal in tasks like reinforcement learning. However, the prevalence and impact of length bias in QE have been underexplored. Through a systematic study of top-performing regression-based and LLM-as-a-Judge QE metrics across 10 diverse language pairs, we reveal two critical length biases: First, QE metrics consistently over-predict errors with increasing translation length, even for high-quality, error-free texts. Second, they exhibit a preference for shorter translations when multiple candidates are available for the same source text. These inherent length biases risk unfairly penalizing longer, correct translations and can lead to sub-optimal decision-making in applications such as QE reranking and QE guided reinforcement learning. We further investigate the root cause, presenting evidence that QE models conflate quality with sequence length due to skewed supervision distributions. As a diagnostic intervention, we apply length normalization during training. We show that this simple intervention is sufficient to decouple error probability from length, effectively counteracting the data skew and yielding more reliable QE signals.

Method: BR-RM is a two-turn reward model: Turn 1 performs adaptive branching to select instance-critical dimensions (e.g., factuality, safety) and sketches evidence-seeking hypotheses. Turn 2 executes branch-conditioned rethinking, a targeted reread that tests those hypotheses and scrutinizes only the most important aspects. Training uses GRPO-style reinforcement learning over structured two-turn traces with binary outcome rewards and strict format checks.

Result: The model achieves state-of-the-art performance on three challenging reward modeling benchmarks across diverse domains, demonstrating reduced judgment diffusion and improved sensitivity to subtle yet consequential errors.

Conclusion: By converting all-at-once scoring into focused, second-look reasoning, BR-RM reduces judgment diffusion and improves reward modeling performance while remaining practical and scalable for standard RLHF pipelines.

Abstract: Large language models (LLMs) increasingly rely on thinking models that externalize intermediate steps and allocate extra test-time compute, with think-twice strategies showing that a deliberate second pass can elicit stronger reasoning. In contrast, most reward models (RMs) still compress many quality dimensions into a single scalar in one shot, a design that induces judgment diffusion: attention spreads across evaluation criteria, yielding diluted focus and shallow analysis. We introduce branch-and-rethink (BR-RM), a two-turn RM that transfers the think-twice principle to reward modeling. Turn 1 performs adaptive branching, selecting a small set of instance-critical dimensions (such as factuality and safety) and sketching concise, evidence-seeking hypotheses. Turn 2 executes branch-conditioned rethinking, a targeted reread that tests those hypotheses and scrutinizes only what matters most. We train with GRPO-style reinforcement learning over structured two-turn traces using a simple binary outcome reward with strict format checks, making the approach compatible with standard RLHF pipelines. By converting all-at-once scoring into focused, second-look reasoning, BR-RM reduces judgment diffusion and improves sensitivity to subtle yet consequential errors while remaining practical and scalable. Experimental results demonstrate that our model achieves state-of-the-art performance on three challenging reward modeling benchmarks across diverse domains.

Method: The authors analyze the sample complexity of language generation in the limit for several canonical classes of formal languages, including context-free languages, regular languages, locally threshold testable languages, and non-erasing pattern languages. They examine computational feasibility through theoretical analysis of these language classes.

Result: Results show infeasibility already appears for context-free and regular languages, and persists even for strict subclasses like locally threshold testable languages, as well as for incomparable classes like non-erasing pattern languages. This establishes a clear gap between theoretical possibility and computational feasibility.

Conclusion: There is a significant gap between the theoretical possibility of language generation in the limit (established by Kleinberg and Mullainathan) and its computational feasibility, with infeasibility appearing even for relatively simple language classes.

Abstract: Kleinberg and Mullainathan showed that language generation in the limit is always possible at the level of computability: given enough positive examples, a learner can eventually generate data indistinguishable from a target language. However, such existence results do not address feasibility. We study the sample complexity of language generation in the limit for several canonical classes of formal languages. Our results show that infeasibility already appears for context-free and regular languages, and persists even for strict subclasses such as locally threshold testable languages, as well as for incomparable classes such as non-erasing pattern languages, a well-studied class in the theory of language identification. Overall, our results establish a clear gap between the theoretical possibility of language generation in the limit and its computational feasibility.

Method: TS-PEFT uses proximal optimization as a dynamic probe to identify token-level redundancy during fine-tuning, allowing selective updates of only important tokens while discarding redundant ones.

Result: Extensive experiments show that discarding 30-70% of token updates consistently matches or exceeds performance of dense baselines like LoRA and DoRA, while learned token-level sparsity serves as a superior indicator of module importance.

Conclusion: Indiscriminately updating all tokens is computationally superfluous and introduces optimization noise; token-level sparsity provides a novel data-driven perspective on LM adaptation mechanisms.

Abstract: Current Parameter-Efficient Fine-Tuning (PEFT) methods typically operate under an implicit assumption: Once a target module is selected, every token passing through it contributes equally to the downstream task and requires a parameter update. In this paper, we challenge this convention by revealing a pervasive token-level redundancy in the fine-tuning of large models (LMs). We propose TS-PEFT, a theoretical framework utilizing proximal optimization that acts as a dynamic probe to identify token-level redundancy during the fine-tuning process. Extensive experiments demonstrate that indiscriminately updating all tokens is not only computationally superfluous but often introduces optimization noise. Surprisingly, by discarding 30%-70% of token updates, TS-PEFT consistently matches or exceeds the performance of dense baselines such as LoRA, DoRA. Our in-depth analysis shows that the learned token-level sparsity is a superior indicator of module importance compared to traditional weight criteria, providing a novel data-driven perspective on the intrinsic adaptation mechanism of LMs.

Method: A multi-stage pipeline that: 1) extracts candidate triplets with qualifiers from text, 2) enforces Wikidata-based type and relation constraints to ensure ontology consistency, and 3) normalizes entities to reduce duplication. The approach focuses on creating compact, well-connected KGs with minimal token usage.

Result: Achieves 96% coverage of correct answer entities in generated triplets on MuSiQue. On HotpotQA: 76.0 F1 (triplets-only), and on MuSiQue: 59.8 F1, matching or surpassing retrieval-augmented generation baselines. State-of-the-art 86% information-retention on MINE-1 benchmark. Efficient with <1,000 output tokens (3× fewer than AriGraph, <1/20 of GraphRAG).

Conclusion: Wikontic provides a scalable solution for constructing high-quality knowledge graphs from text, enhancing KG quality and offering practical benefits for leveraging structured knowledge in LLMs while maintaining efficiency.

Abstract: Knowledge graphs (KGs) provide structured, verifiable grounding for large language models (LLMs), but current LLM-based systems commonly use KGs as auxiliary structures for text retrieval, leaving their intrinsic quality underexplored. In this work, we propose Wikontic, a multi-stage pipeline that constructs KGs from open-domain text by extracting candidate triplets with qualifiers, enforcing Wikidata-based type and relation constraints, and normalizing entities to reduce duplication. The resulting KGs are compact, ontology-consistent, and well-connected; on MuSiQue, the correct answer entity appears in 96% of generated triplets. On HotpotQA, our triplets-only setup achieves 76.0 F1, and on MuSiQue 59.8 F1, matching or surpassing several retrieval-augmented generation baselines that still require textual context. In addition, Wikontic attains state-of-the-art information-retention performance on the MINE-1 benchmark (86%), outperforming prior KG construction methods. Wikontic is also efficient at build time: KG construction uses less than 1,000 output tokens, about 3$\times$ fewer than AriGraph and $<$1/20 of GraphRAG. The proposed pipeline enhances the quality of the generated KG and offers a scalable solution for leveraging structured knowledge in LLMs.

Method: Proposes LogicReward, a reward system that guides model training by enforcing step-level logical correctness using a theorem prover. Introduces Autoformalization with Soft Unification to reduce natural language ambiguity and improve formalization quality for effective theorem prover use.

Result: An 8B model trained with LogicReward surpasses GPT-4o and o4-mini by 11.6% and 2% on natural language inference and logical reasoning tasks. Enhances reasoning faithfulness, improves generalizability to unseen tasks (math and commonsense reasoning), and provides reliable reward signals without ground-truth labels.

Conclusion: LogicReward effectively improves logical reasoning in LLMs through theorem-prover-based step-level supervision, enhancing reasoning quality and generalizability across diverse reasoning tasks.

Abstract: Although LLMs exhibit strong reasoning capabilities, existing training methods largely depend on outcome-based feedback, which can produce correct answers with flawed reasoning. Prior work introduces supervision on intermediate steps but still lacks guarantees of logical soundness, which is crucial in high-stakes scenarios where logical consistency is paramount. To address this, we propose LogicReward, a novel reward system that guides model training by enforcing step-level logical correctness with a theorem prover. We further introduce Autoformalization with Soft Unification, which reduces natural language ambiguity and improves formalization quality, enabling more effective use of the theorem prover. An 8B model trained on data constructed with LogicReward surpasses GPT-4o and o4-mini by 11.6% and 2% on natural language inference and logical reasoning tasks with simple training procedures. Further analysis shows that LogicReward enhances reasoning faithfulness, improves generalizability to unseen tasks such as math and commonsense reasoning, and provides a reliable reward signal even without ground-truth labels. We will release all data and code at https://llm-symbol.github.io/LogicReward.

Method: Combines an autonomous agent and an LLM-based detection model where the agent acts as a reliable decision-maker for early time point determination, while the LLM serves as a powerful rumor detector. Only the lightweight agent needs training, keeping the LLM training-free.

Result: Extensive experiments on four real-world datasets show the approach boosts performance across LLMs and surpasses existing EARD methods in both accuracy and earliness.

Conclusion: The proposed framework offers the first solution for few-shot Early Rumor Detection, requiring only lightweight agent training while leveraging LLMs’ powerful detection capabilities without additional training.

Abstract: Early Rumor Detection (EARD) aims to identify the earliest point at which a claim can be accurately classified based on a sequence of social media posts. This is especially challenging in data-scarce settings. While Large Language Models (LLMs) perform well in few-shot NLP tasks, they are not well-suited for time-series data and are computationally expensive for both training and inference. In this work, we propose a novel EARD framework that combines an autonomous agent and an LLM-based detection model, where the agent acts as a reliable decision-maker for \textit{early time point determination}, while the LLM serves as a powerful \textit{rumor detector}. This approach offers the first solution for few-shot EARD, necessitating only the training of a lightweight agent and allowing the LLM to remain training-free. Extensive experiments on four real-world datasets show our approach boosts performance across LLMs and surpasses existing EARD methods in accuracy and earliness.

Method: Proposes MixMinMatch: combines multiple existing web corpora, performs cross-dataset MinHash deduplication, and identifies documents independently recovered by multiple sources. Cross-source agreement serves as a model-free quality filter.

Result: Applied to Arabic, Turkish, and Hindi, producing corpora that match or exceed best single-source baselines while providing up to 4× more unique tokens. Arabic subset shows 4.5% improvement over ArabicWeb24, Turkish shows 5.5% improvement over FineWeb-2.

Conclusion: Cross-source agreement is an effective, free quality signal for multilingual pretraining data. MixMinMatch enables creation of higher-quality datasets from existing redundant web corpora without additional computational cost.

Abstract: Multilingual data from the web is essential for LLM pretraining. Yet, scraping it is expensive, and research groups repeatedly crawl the same content. For example, we found that over 40% of tokens across major Arabic web corpora are duplicated between sources. In this work, we propose to use this wasteful redundancy as a quality signal to create high-quality pretraining datasets. Our key insight is that cross-source agreement functions as a free, model-free quality filter: content retained by multiple independent pipelines is more likely to represent high-quality text. Crucially, this signal requires no additional computation beyond standard deduplication, which is already performed at scale when pretraining language models. So, we propose MixMinMatch, a method that combines multiple existing web corpora, performs cross-dataset MinHash deduplication, and identifies documents independently recovered by multiple sources. We apply MixMinMatch to Arabic, Turkish, and Hindi, producing corpora that match or exceed the quality of the best single-source baselines, while providing up to 4$\times$ more unique tokens. On Arabic, our matched subset achieves a 4.5% relative improvement over ArabicWeb24, while on Turkish, we improve over FineWeb-2 by 5.5%. We release the datasets at: https://huggingface.co/collections/AdaMLLab/mixminmatch

Method: Operationalizes hierarchical density modeling on large language model embeddings by progressively relaxing local density constraints instead of enforcing fixed taxonomies or single clustering resolutions.

Result: Semantic alignment peaks at intermediate density levels, with abrupt transitions corresponding to meaningful changes in semantic resolution. Applied to large institutional and scientific corpora, it exposes dominant fields, cross-disciplinary proximities, and emerging thematic clusters.

Conclusion: Framing hierarchical structure as an emergent property of density in embedding spaces provides interpretable, multi-scale representation of semantic structure suitable for large, evolving text collections.

Abstract: Recent advances in large language models enable documents to be represented as dense semantic embeddings, supporting similarity-based operations over large text collections. However, many web-scale systems still rely on flat clustering or predefined taxonomies, limiting insight into hierarchical topic relationships. In this paper we operationalize hierarchical density modeling on large language model embeddings in a way not previously explored. Instead of enforcing a fixed taxonomy or single clustering resolution, the method progressively relaxes local density constraints, revealing how compact semantic groups merge into broader thematic regions. The resulting tree encodes multi-scale semantic organization directly from data, making structural relationships between topics explicit. We evaluate the hierarchies on standard text benchmarks, showing that semantic alignment peaks at intermediate density levels and that abrupt transitions correspond to meaningful changes in semantic resolution. Beyond benchmarks, the approach is applied to large institutional and scientific corpora, exposing dominant fields, cross-disciplinary proximities, and emerging thematic clusters. By framing hierarchical structure as an emergent property of density in embedding spaces, this method provides an interpretable, multi-scale representation of semantic structure suitable for large, evolving text collections.

Method: Analysis of digital resources and datasets for ancient scripts, examination of methodological pipeline from visual processing to advanced reasoning, study of technological shift from classical computer vision to deep learning including transformers and multimodal models.

Result: Mapping of the field’s landscape, identification of core challenges (data scarcity, AI-humanistic disconnect), and synthesis of future research agenda focused on multimodal, few-shot, and human-centric systems.

Conclusion: Computational Chinese paleography is evolving toward integrated digital ecosystems, requiring multimodal AI systems that can handle few-shot learning and augment rather than replace scholarly expertise.

Abstract: Chinese paleography, the study of ancient Chinese writing, is undergoing a computational turn powered by artificial intelligence. This position paper charts the trajectory of this emerging field, arguing that it is evolving from automating isolated visual tasks to creating integrated digital ecosystems for scholarly research. We first map the landscape of digital resources, analyzing critical datasets for oracle bone, bronze, and bamboo slip scripts. The core of our analysis follows the field’s methodological pipeline: from foundational visual processing (image restoration, character recognition), through contextual analysis (artifact rejoining, dating), to the advanced reasoning required for automated decipherment and human-AI collaboration. We examine the technological shift from classical computer vision to modern deep learning paradigms, including transformers and large multimodal models. Finally, we synthesize the field’s core challenges – notably data scarcity and a disconnect between current AI capabilities and the holistic nature of humanistic inquiry – and advocate for a future research agenda focused on creating multimodal, few-shot, and human-centric systems to augment scholarly expertise.

Method: Proposes Plan-Verify-Fill (PVF): 1) Actively constructs hierarchical skeleton by prioritizing high-leverage semantic anchors, 2) Employs verification protocol for pragmatic structural stopping, 3) Training-free paradigm that grounds planning via quantitative validation.

Result: PVF reduces Number of Function Evaluations (NFE) by up to 65% compared to confidence-based parallel decoding across benchmark datasets on LLaDA-8B-Instruct and Dream-7B-Instruct, achieving superior efficiency without compromising accuracy.

Conclusion: PVF demonstrates that active planning with verification protocols can significantly improve decoding efficiency for Diffusion Language Models, offering a promising direction for non-sequential text generation.

Abstract: Diffusion Language Models (DLMs) present a promising non-sequential paradigm for text generation, distinct from standard autoregressive (AR) approaches. However, current decoding strategies often adopt a reactive stance, underutilizing the global bidirectional context to dictate global trajectories. To address this, we propose Plan-Verify-Fill (PVF), a training-free paradigm that grounds planning via quantitative validation. PVF actively constructs a hierarchical skeleton by prioritizing high-leverage semantic anchors and employs a verification protocol to operationalize pragmatic structural stopping where further deliberation yields diminishing returns. Extensive evaluations on LLaDA-8B-Instruct and Dream-7B-Instruct demonstrate that PVF reduces the Number of Function Evaluations (NFE) by up to 65% compared to confidence-based parallel decoding across benchmark datasets, unlocking superior efficiency without compromising accuracy.

Method: 1) Computational analysis of linguistic “dialectness” and audio quality across DA corpora; 2) Development of Arab Voices framework that unifies 31 datasets with harmonized metadata; 3) Benchmarking of recent ASR systems on this standardized framework.

Result: Found substantial heterogeneity in acoustic conditions and dialectal signals across datasets; Created Arab Voices framework providing unified access to 31 datasets across 14 dialects; Established strong baselines for modern DA ASR.

Conclusion: Standardized characterization beyond coarse labels is needed for DA speech data; Arab Voices framework reduces fragmentation and supports reproducible evaluation for Dialectal Arabic ASR research.

Abstract: Dialectal Arabic (DA) speech data vary widely in domain coverage, dialect labeling practices, and recording conditions, complicating cross-dataset comparison and model evaluation. To characterize this landscape, we conduct a computational analysis of linguistic ``dialectness’’ alongside objective proxies of audio quality on the training splits of widely used DA corpora. We find substantial heterogeneity both in acoustic conditions and in the strength and consistency of dialectal signals across datasets, underscoring the need for standardized characterization beyond coarse labels. To reduce fragmentation and support reproducible evaluation, we introduce Arab Voices, a standardized framework for DA ASR. Arab Voices provides unified access to 31 datasets spanning 14 dialects, with harmonized metadata and evaluation utilities. We further benchmark a range of recent ASR systems, establishing strong baselines for modern DA ASR.

Method: Proposes AfroScope framework with AfroScope-Data (dataset covering 713 African languages) and AfroScope-Models (LID models). Introduces hierarchical classification using Mirror-Serengeti embedding model for 29 closely related languages to improve discrimination of confusable varieties.

Result: Hierarchical approach improves macro F1 by 4.55 on confusable language subset compared to best base model. Framework enables large-scale measurement of Africa’s linguistic landscape in digital text.

Conclusion: AfroScope provides comprehensive African LID capability with improved handling of confusable languages, positioning it as enabling technology for African NLP applications and linguistic landscape analysis.

Abstract: Language Identification (LID) is the task of determining the language of a given text and is a fundamental preprocessing step that affects the reliability of downstream NLP applications. While recent work has expanded LID coverage for African languages, existing approaches remain limited in (i) the number of supported languages and (ii) their ability to make fine-grained distinctions among closely related varieties. We introduce AfroScope, a unified framework for African LID that includes AfroScope-Data, a dataset covering 713 African languages, and AfroScope-Models, a suite of strong LID models with broad language coverage. To better distinguish highly confusable languages, we propose a hierarchical classification approach that leverages Mirror-Serengeti, a specialized embedding model targeting 29 closely related or geographically proximate languages. This approach improves macro F1 by 4.55 on this confusable subset compared to our best base model. Finally, we analyze cross linguistic transfer and domain effects, offering guidance for building robust African LID systems. We position African LID as an enabling technology for large scale measurement of Africas linguistic landscape in digital text and release AfroScope-Data and AfroScope-Models publicly.

Method: Proposes a lightweight probe that reads confidence signals directly from hidden states of intermediate LLM layers. The probe adds less than 0.1% computational overhead and runs fully in parallel with generation, enabling hallucination detection before answers are produced. This is used to build an LLM router that answers confident queries immediately while delegating uncertain ones to stronger models.

Result: Achieves state-of-the-art AUROC on 10 out of 12 settings across four QA benchmarks and three LLM families, with gains of up to 13 points over prior methods. The method generalizes across dataset shifts without retraining.

Conclusion: Intermediate LLM layers encode useful confidence signals that can be efficiently extracted for real-time hallucination detection, enabling practical deployment of LLM routing systems with minimal computational overhead.

Abstract: LLMs often produce fluent but incorrect answers, yet detecting such hallucinations typically requires multiple sampling passes or post-hoc verification, adding significant latency and cost. We hypothesize that intermediate layers encode confidence signals that are lost in the final output layer, and propose a lightweight probe to read these signals directly from hidden states. The probe adds less than 0.1% computational overhead and can run fully in parallel with generation, enabling hallucination detection before the answer is produced. Building on this, we develop an LLM router that answers confident queries immediately while delegating uncertain ones to stronger models. Despite its simplicity, our method achieves SOTA AUROC on 10 out of 12 settings across four QA benchmarks and three LLM families, with gains of up to 13 points over prior methods, and generalizes across dataset shifts without retraining.

Method: The paper investigates two approaches: selecting small, architecturally informed subsets of model components vs. projecting full gradients into lower-dimensional space. Uses a novel benchmark to compare greedy component selection against full gradients and random projections for influence estimation in retrieval tasks.

Result: Greedily selected component subsets capture training data influence information more effectively than full gradients or random projections for retrieval tasks. This approach is also more computationally efficient than random projection methods.

Conclusion: Targeted component selection based on architectural insights provides a practical strategy for making instance-based explanations of large models computationally feasible, outperforming both full gradient analysis and dimensionality reduction techniques.

Abstract: Gradient-based methods for instance-based explanation for large language models (LLMs) are hindered by the immense dimensionality of model gradients. In practice, influence estimation is restricted to a subset of model parameters to make computation tractable, but this subset is often chosen ad hoc and rarely justified by systematic evaluation. This paper investigates if it is better to create low-dimensional representations by selecting a small, architecturally informed subset of model components or by projecting the full gradients into a lower-dimensional space. Using a novel benchmark, we show that a greedily selected subset of components captures the information about training data influence needed for a retrieval task more effectively than either the full gradient or random projection. We further find that this approach is more computationally efficient than random projection, demonstrating that targeted component selection is a practical strategy for making instance-based explanations of large models more computationally feasible.

Method: Used controlled distribution-sampling simulations to analyze token probability behavior, evaluated models on downstream tasks (code generation, recommendation), and analyzed internal properties to understand underlying mechanisms.

Result: Discovered dichotomy: D-models show large step-to-step variability in P_token with poor P_task alignment; E-models show stable P_token with better P_task alignment. Found systematic trade-offs between diversity and stability affecting task outcomes.

Conclusion: Provides foundational insights into LLM probabilistic sampling behavior and practical guidance for model selection: D-models for diversity, E-models for stability, helping balance diversity with reliability in web-scale applications.

Abstract: The predictive probability of the next token (P_token) in large language models (LLMs) is inextricably linked to the probability of relevance for the next piece of information, the purchase probability of the next product, and the execution probability of the next action-all of which fall under the scope of the task-level target distribution (P_task). While LLMs are known to generate samples that approximate real-world distributions, whether their fine-grained sampling probabilities faithfully align with task requirements remains an open question. Through controlled distribution-sampling simulations, we uncover a striking dichotomy in LLM behavior, distinguishing two model types: D-models (e.g. Qwen-2.5), whose P_token exhibits large step-to-step variability and poor alignment with P_task; and E-models (e.g. Mistral-Small), whose P_token is more stable and better aligned with P_task. We further evaluate these two model types in downstream tasks such as code generation and recommendation, revealing systematic trade-offs between diversity and stability that shape task outcomes. Finally, we analyze the internal properties of both model families to probe their underlying mechanisms. These findings offer foundational insights into the probabilistic sampling behavior of LLMs and provide practical guidance on when to favor D- versus E-models. For web-scale applications, including recommendation, search, and conversational agents, our results inform model selection and configuration to balance diversity with reliability under real-world uncertainty, providing a better level of interpretation.

Method: Evaluated LLMs on diverse C programs from the Termination category of SV-Comp 2025. Compared performance of models like GPT-5, Claude Sonnet-4.5, and Code World Model against specialized verification tools using test-time-scaling.

Result: LLMs perform remarkably well at predicting program termination, with GPT-5 and Claude Sonnet-4.5 ranking just behind the top-ranked tool, and CWM placing behind the second-ranked tool. However, LLMs often fail to provide valid proof witnesses, and performance degrades with increasing program length.

Conclusion: LLMs show promise for program termination prediction despite the undecidable nature of the problem, but have limitations in proof generation and scalability. The findings motivate further research into LLMs for reasoning about undecidable problems.

Abstract: Determining whether a program terminates is a central problem in computer science. Turing’s foundational result established the Halting Problem as undecidable, showing that no algorithm can universally determine termination for all programs and inputs. Consequently, automatic verification tools approximate termination, sometimes failing to prove or disprove; these tools rely on problem-specific architectures and abstractions, and are usually tied to particular programming languages. Recent success and progress in large language models (LLMs) raises the following question: can LLMs reliably predict program termination? In this work, we evaluate LLMs on a diverse set of C programs from the Termination category of the International Competition on Software Verification (SV-Comp) 2025. Our results suggest that LLMs perform remarkably well at predicting program termination, where GPT-5 and Claude Sonnet-4.5 would rank just behind the top-ranked tool (using test-time-scaling), and Code World Model (CWM) would place just behind the second-ranked tool. While LLMs are effective at predicting program termination, they often fail to provide a valid witness as a proof. Moreover, LLMs performance drops as program length increases. We hope these insights motivate further research into program termination and the broader potential of LLMs for reasoning about undecidable problems.

Method: Introduce a simple extra sink token via modified attention mask: a special token constrained to attend solely to itself while remaining globally visible to all other tokens, creating a dedicated structural sink

Result: Adding a single extra token stabilizes attention sinks and substantially improves model performance; effectiveness is independent of token position and has negligible semantic content, validating its role as a robust structural sink

Conclusion: The moving sink phenomenon in DLMs can be effectively addressed by introducing a dedicated sink token with self-only attention, providing a simple but effective solution to improve inference stability and performance

Abstract: Diffusion Language Models (DLMs) have emerged as a compelling alternative to autoregressive approaches, enabling parallel text generation with competitive performance. Despite these advantages, there is a critical instability in DLMs: the moving sink phenomenon. Our analysis indicates that sink tokens exhibit low-norm representations in the Transformer’s value space, and that the moving sink phenomenon serves as a protective mechanism in DLMs to prevent excessive information mixing. However, their unpredictable positions across diffusion steps undermine inference robustness. To resolve this, we propose a simple but effective extra sink token implemented via a modified attention mask. Specifically, we introduce a special token constrained to attend solely to itself, while remaining globally visible to all other tokens. Experimental results demonstrate that introducing a single extra token stabilizes attention sinks, substantially improving model performance. Crucially, further analysis confirms that the effectiveness of this token is independent of its position and characterized by negligible semantic content, validating its role as a robust and dedicated structural sink.

Method: Decomposes complex dialogue into minimal conversational units, processes each independently, and predicts transitions using a semi-cascaded full-duplex system built around a multimodal LLM with auxiliary modules (VAD, TTS).

Result: System achieved second place on the test set of the Human-like Spoken Dialogue Systems Challenge (Track 2: Full-Duplex Interaction) on the HumDial dataset.

Conclusion: The framework enables train-free, plug-and-play full-duplex voice interaction and demonstrates effectiveness through competitive performance on benchmark datasets.

Abstract: Full-duplex voice interaction is crucial for natural human computer interaction. We present a framework that decomposes complex dialogue into minimal conversational units, enabling the system to process each unit independently and predict when to transit to the next. This framework is instantiated as a semi-cascaded full-duplex dialogue system built around a multimodal large language model, supported by auxiliary modules such as voice activity detection (VAD) and text-to-speech (TTS) synthesis. The resulting system operates in a train-free, plug-and-play manner. Experiments on the HumDial dataset demonstrate the effectiveness of our framework, which ranks second among all teams on the test set of the Human-like Spoken Dialogue Systems Challenge (Track 2: Full-Duplex Interaction). Code is available at the GitHub repository https://github.com/yu-haoyuan/fd-badcat.

Method: Proposes MobileBench-OL with 1080 tasks from 80 Chinese apps, organized into 5 subsets to measure task execution, complex reasoning, and noise robustness. Includes an auto-eval framework with reset mechanism for stable, repeatable real-world benchmarking.

Result: Evaluation of 12 leading GUI agents shows significant room for improvement to meet real-world requirements. Human evaluation confirms MobileBench-OL reliably measures agent performance in real environments.

Conclusion: MobileBench-OL addresses limitations of existing benchmarks by providing comprehensive evaluation of GUI agents’ task execution, reasoning, and noise robustness in realistic mobile environments.

Abstract: Recent advances in mobile Graphical User Interface (GUI) agents highlight the growing need for comprehensive evaluation benchmarks. While new online benchmarks offer more realistic testing than offline ones, they tend to focus on the agents’ task instruction-following ability while neglecting their reasoning and exploration ability. Moreover, these benchmarks do not consider the random noise in real-world mobile environments. This leads to a gap between benchmarks and real-world environments. To addressing these limitations, we propose MobileBench-OL, an online benchmark with 1080 tasks from 80 Chinese apps. It measures task execution, complex reasoning, and noise robustness of agents by including 5 subsets, which set multiple evaluation dimensions. We also provide an auto-eval framework with a reset mechanism, enabling stable and repeatable real-world benchmarking. Evaluating 12 leading GUI agents on MobileBench-OL shows significant room for improvement to meet real-world requirements. Human evaluation further confirms that MobileBench-OL can reliably measure the performance of leading GUI agents in real environments. Our data and code will be released upon acceptance.

Method: Introduces AgentLongBench which evaluates agents through simulated environment rollouts based on Lateral Thinking Puzzles, generating interaction trajectories across knowledge-intensive and knowledge-free scenarios. Tests state-of-the-art models and memory systems with context lengths from 32K to 4M tokens.

Result: Agents struggle with dynamic information synthesis essential for workflows despite being adept at static retrieval. Performance degradation is driven by the minimum number of tokens required to resolve a query, explaining why high information density in massive tool responses is more challenging than memory fragmentation in long-turn dialogues.

Conclusion: Current LLM agents have critical weaknesses in handling dynamic, interactive environments requiring non-linear reasoning and iterative feedback, highlighting the need for improved agent architectures that can better synthesize information across complex workflows.

Abstract: The evolution of Large Language Models (LLMs) into autonomous agents necessitates the management of extensive, dynamic contexts. Current benchmarks, however, remain largely static, relying on passive retrieval tasks that fail to simulate the complexities of agent-environment interaction, such as non-linear reasoning and iterative feedback. To address this, we introduce \textbf{AgentLongBench}, which evaluates agents through simulated environment rollouts based on Lateral Thinking Puzzles. This framework generates rigorous interaction trajectories across knowledge-intensive and knowledge-free scenarios. Experiments with state-of-the-art models and memory systems (32K to 4M tokens) expose a critical weakness: while adept at static retrieval, agents struggle with the dynamic information synthesis essential for workflows. Our analysis indicates that this degradation is driven by the minimum number of tokens required to resolve a query. This factor explains why the high information density inherent in massive tool responses poses a significantly greater challenge than the memory fragmentation typical of long-turn dialogues.

Method: Proposes MA-LipNet with three sequential attention modules: 1) Channel Attention (CA) for channel-wise feature recalibration, 2) Joint Spatial-Temporal Attention (JSTA) for coarse-grained spatio-temporal filtering, and 3) Separate Spatial-Temporal Attention (SSTA) for fine-grained refinement by separately modeling temporal and spatial attention.

Result: Extensive experiments on CMLR and GRID datasets show MA-LipNet significantly reduces Character Error Rate (CER) and Word Error Rate (WER), outperforming several state-of-the-art methods.

Conclusion: Multi-dimensional feature refinement through attention mechanisms is crucial for robust visual speech recognition, and MA-LipNet demonstrates effectiveness in improving lipreading performance.

Abstract: Lipreading, the technology of decoding spoken content from silent videos of lip movements, holds significant application value in fields such as public security. However, due to the subtle nature of articulatory gestures, existing lipreading methods often suffer from limited feature discriminability and poor generalization capabilities. To address these challenges, this paper delves into the purification of visual features from temporal, spatial, and channel dimensions. We propose a novel method named Multi-Attention Lipreading Network(MA-LipNet). The core of MA-LipNet lies in its sequential application of three dedicated attention modules. Firstly, a \textit{Channel Attention (CA)} module is employed to adaptively recalibrate channel-wise features, thereby mitigating interference from less informative channels. Subsequently, two spatio-temporal attention modules with distinct granularities-\textit{Joint Spatial-Temporal Attention (JSTA)} and \textit{Separate Spatial-Temporal Attention (SSTA)}-are leveraged to suppress the influence of irrelevant pixels and video frames. The JSTA module performs a coarse-grained filtering by computing a unified weight map across the spatio-temporal dimensions, while the SSTA module conducts a more fine-grained refinement by separately modeling temporal and spatial attentions. Extensive experiments conducted on the CMLR and GRID datasets demonstrate that MA-LipNet significantly reduces the Character Error Rate (CER) and Word Error Rate (WER), validating its effectiveness and superiority over several state-of-the-art methods. Our work highlights the importance of multi-dimensional feature refinement for robust visual speech recognition.

Method: Three key components: 1) Non-Markov multi-round data construction with rollback editing and name-based personalization, 2) History-conditioned training with token-level caching to prevent identity drift, 3) Reconstruction-based DiT detokenizer and multi-stage fine-tuning curriculum for high-fidelity image generation.

Result: The framework yields substantial improvements in multi-round consistency and instruction compliance while maintaining strong single-round editing and personalization capabilities.

Conclusion: Explicitly training for non-Markov interactions is crucial for true conversational image generation, enabling models to maintain consistency across multiple rounds of interaction by properly referencing and preserving earlier visual states.

Abstract: Conversational image generation requires a model to follow user instructions across multiple rounds of interaction, grounded in interleaved text and images that accumulate as chat history. While recent multimodal large language models (MLLMs) can generate and edit images, most existing multi-turn benchmarks and training recipes are effectively Markov: the next output depends primarily on the most recent image, enabling shortcut solutions that ignore long-range history. In this work we formalize and target the more challenging non-Markov setting, where a user may refer back to earlier states, undo changes, or reference entities introduced several rounds ago. We present (i) non-Markov multi-round data construction strategies, including rollback-style editing that forces retrieval of earlier visual states and name-based multi-round personalization that binds names to appearances across rounds; (ii) a history-conditioned training and inference framework with token-level caching to prevent multi-round identity drift; and (iii) enabling improvements for high-fidelity image reconstruction and editable personalization, including a reconstruction-based DiT detokenizer and a multi-stage fine-tuning curriculum. We demonstrate that explicitly training for non-Markov interactions yields substantial improvements in multi-round consistency and instruction compliance, while maintaining strong single-round editing and personalization.

Method: Proposes a novel text-guided denoising method using features from a pretrained CLIP model combined with a U-Net based denoising architecture to enhance PET images across various count levels within a single model.

Result: Experimental results show significant improvements in both qualitative and quantitative assessments, demonstrating the model’s effectiveness in PET image denoising.

Conclusion: The proposed text-guided denoising model offers flexibility for handling complex denoising demands and potentially reducing acquisition time in PET imaging.

Abstract: Positron Emission Tomography (PET) imaging is a vital tool in medical diagnostics, offering detailed insights into molecular processes within the human body. However, PET images often suffer from complicated noise, which can obscure critical diagnostic information. The quality of the PET image is impacted by various factors including scanner hardware, image reconstruction, tracer properties, dose/count level, and acquisition time. In this study, we propose a novel text-guided denoising method capable of enhancing PET images across a wide range of count levels within a single model. The model utilized the features from a pretrained CLIP model with a U-Net based denoising model. Experimental results demonstrate that the proposed model leads significant improvements in both qualitative and quantitative assessments. The flexibility of the model shows the potential for helping more complicated denoising demands or reducing the acquisition time.

Method: Proposes an unrolled dual-domain method based on synthetic data. Leverages intrinsic correlations between sinogram and image domains through synthetic data generated from natural images, enabling artifact correction without real clinical data.

Result: Outperformed state-of-the-art approaches by large margin in experiments simulating 1-2% detectors defect near isocenter. Can correct LPP artifacts without cost of data collection for training.

Conclusion: Solution corrects LPP artifacts without requiring real-world clinical data, adaptable to different scanner settings for software-based applications.

Abstract: Low performance pixels (LPP) in Computed Tomography (CT) detectors would lead to ring and streak artifacts in the reconstructed images, making them clinically unusable. In recent years, several solutions have been proposed to correct LPP artifacts, either in the image domain or in the sinogram domain using supervised deep learning methods. However, these methods require dedicated datasets for training, which are expensive to collect. Moreover, existing approaches focus solely either on image-space or sinogram-space correction, ignoring the intrinsic correlations from the forward operation of the CT geometry. In this work, we propose an unrolled dual-domain method based on synthetic data to correct LPP artifacts. Specifically, the intrinsic correlations of LPP between the sinogram and image domains are leveraged through synthetic data generated from natural images, enabling the trained model to correct artifacts without requiring any real-world clinical data. In experiments simulating 1-2% detectors defect near the isocenter, the proposed method outperformed the state-of-the-art approaches by a large margin. The results indicate that our solution can correct LPP artifacts without the cost of data collection for model training, and it is adaptable to different scanner settings for software-based applications.

Method: Three-stage framework: (1) pinpoint sensitive onset layer via attention divergence analysis, (2) use sparse autoencoders and differential activation analysis to disentangle malicious features, (3) map features to downstream causal pathways via feature influence scores from zero-out interventions, then selectively suppress these causal chains.

Result: Extensive experiments show TraceRouter significantly outperforms state-of-the-art baselines, achieving superior trade-off between adversarial robustness and general utility.

Conclusion: TraceRouter provides an effective path-level defense framework that physically severs harmful information flow while preserving orthogonal computation routes, addressing limitations of localized interventions against distributed adversarial circuits.

Abstract: Despite their capabilities, large foundation models (LFMs) remain susceptible to adversarial manipulation. Current defenses predominantly rely on the “locality hypothesis”, suppressing isolated neurons or features. However, harmful semantics act as distributed, cross-layer circuits, rendering such localized interventions brittle and detrimental to utility. To bridge this gap, we propose \textbf{TraceRouter}, a path-level framework that traces and disconnects the causal propagation circuits of illicit semantics. TraceRouter operates in three stages: (1) it pinpoints a sensitive onset layer by analyzing attention divergence; (2) it leverages sparse autoencoders (SAEs) and differential activation analysis to disentangle and isolate malicious features; and (3) it maps these features to downstream causal pathways via feature influence scores (FIS) derived from zero-out interventions. By selectively suppressing these causal chains, TraceRouter physically severs the flow of harmful information while leaving orthogonal computation routes intact. Extensive experiments demonstrate that TraceRouter significantly outperforms state-of-the-art baselines, achieving a superior trade-off between adversarial robustness and general utility. Our code will be publicly released. WARNING: This paper contains unsafe model responses.

Method: Trained AI model on diagnostic prostate biopsy slides from STHLM3 cohort using foundation models and attention-based multiple instance learning, validated across three external RP cohorts (LEOPARD, CHIMERA, TCGA-PRAD).

Result: Achieved 5-year time-dependent AUCs of 0.64, 0.70, and 0.70 across external cohorts; integration with clinical variables improved risk stratification; AI incrementally improved prognostication over guideline-based CAPRA-S.

Conclusion: Biopsy-trained histopathology AI can generalize across specimen types for preoperative/postoperative decision making, but added value of multimodal AI approaches over simpler models needs further scrutiny.

Abstract: Biochemical recurrence (BCR) after radical prostatectomy (RP) is a surrogate marker for aggressive prostate cancer with adverse outcomes, yet current prognostic tools remain imprecise. We trained an AI-based model on diagnostic prostate biopsy slides from the STHLM3 cohort (n = 676) to predict patient-specific risk of BCR, using foundation models and attention-based multiple instance learning. Generalizability was assessed across three external RP cohorts: LEOPARD (n = 508), CHIMERA (n = 95), and TCGA-PRAD (n = 379). The image-based approach achieved 5-year time-dependent AUCs of 0.64, 0.70, and 0.70, respectively. Integrating clinical variables added complementary prognostic value and enabled statistically significant risk stratification. Compared with guideline-based CAPRA-S, AI incrementally improved postoperative prognostication. These findings suggest biopsy-trained histopathology AI can generalize across specimen types to support preoperative and postoperative decision making, but the added value of AI-based multimodal approaches over simpler predictive models should be critically scrutinized in further studies.

Method: Introduces BadDet+, a penalty-based framework using log-barrier penalty to suppress true-class predictions for triggered inputs, achieving position/scale invariance and enhanced physical robustness for both region misclassification and object disappearance attacks.

Result: Achieves superior synthetic-to-physical transfer compared to existing baselines while preserving clean performance, with theoretical analysis confirming the penalty operates within trigger-specific feature subspace.

Conclusion: Highlights significant vulnerabilities in object detection and the necessity for specialized defenses against backdoor attacks.

Abstract: Backdoor attacks pose a severe threat to deep learning, yet their impact on object detection remains poorly understood compared to image classification. While attacks have been proposed, we identify critical weaknesses in existing detection-based methods, specifically their reliance on unrealistic assumptions and a lack of physical validation. To bridge this gap, we introduce BadDet+, a penalty-based framework that unifies Region Misclassification Attacks (RMA) and Object Disappearance Attacks (ODA). The core mechanism utilizes a log-barrier penalty to suppress true-class predictions for triggered inputs, resulting in (i) position and scale invariance, and (ii) enhanced physical robustness. On real-world benchmarks, BadDet+ achieves superior synthetic-to-physical transfer compared to existing RMA and ODA baselines while preserving clean performance. Theoretical analysis confirms the proposed penalty acts within a trigger-specific feature subspace, reliably inducing attacks without degrading standard inference. These results highlight significant vulnerabilities in object detection and the necessity for specialized defenses.

Method: A DiT-based framework that builds on a general-purpose video diffusion model, augmenting its video-to-video capabilities with audio conditioning and region-aware, edit-focused training extensions. Uses spatiotemporal inpainting for temporally coherent restructuring of performances.

Result: Enables precise lip synchronization and temporally coherent restructuring of existing performances, including synthesis of realistic human motion in newly added segments while maintaining visual fidelity and identity consistency over long durations.

Conclusion: Represents a foundational step toward generative video models as practical tools for professional video post-production, addressing the critical gap in editing existing videos with audio-driven modifications.

Abstract: Current generative video models excel at producing novel content from text and image prompts, but leave a critical gap in editing existing pre-recorded videos, where minor alterations to the spoken script require preserving motion, temporal coherence, speaker identity, and accurate lip synchronization. We introduce EditYourself, a DiT-based framework for audio-driven video-to-video (V2V) editing that enables transcript-based modification of talking head videos, including the seamless addition, removal, and retiming of visually spoken content. Building on a general-purpose video diffusion model, EditYourself augments its V2V capabilities with audio conditioning and region-aware, edit-focused training extensions. This enables precise lip synchronization and temporally coherent restructuring of existing performances via spatiotemporal inpainting, including the synthesis of realistic human motion in newly added segments, while maintaining visual fidelity and identity consistency over long durations. This work represents a foundational step toward generative video models as practical tools for professional video post-production.

Method: Proposes ActionVLM with two key components: (1) a debiasing reweighting module that estimates the language advantage (incremental benefit of language over vision-only predictions) and dynamically reweights language modality accordingly, and (2) a residual aggregation strategy that treats language as complementary refinement rather than primary driver.

Result: Experiments on THUMOS14 show the model outperforms state-of-the-art by up to 3.2% mAP, demonstrating effective mitigation of modality bias and improved temporal reasoning.

Conclusion: ActionVLM successfully addresses modality bias in vision-language models for temporal action localization by systematically preserving vision as the dominant signal while adaptively exploiting language only when beneficial.

Abstract: Temporal Action Localization (TAL) requires identifying both the boundaries and categories of actions in untrimmed videos. While vision-language models (VLMs) offer rich semantics to complement visual evidence, existing approaches tend to overemphasize linguistic priors at the expense of visual performance, leading to a pronounced modality bias. We propose ActionVLM, a vision-language aggregation framework that systematically mitigates modality bias in TAL. Our key insight is to preserve vision as the dominant signal while adaptively exploiting language only when beneficial. To this end, we introduce (i) a debiasing reweighting module that estimates the language advantage-the incremental benefit of language over vision-only predictions-and dynamically reweights language modality accordingly, and (ii) a residual aggregation strategy that treats language as a complementary refinement rather than the primary driver. This combination alleviates modality bias, reduces overconfidence from linguistic priors, and strengthens temporal reasoning. Experiments on THUMOS14 show that our model outperforms state-of-the-art by up to 3.2% mAP.

Method: SoT trains a unified multimodal autoregressive model to generate interleaved textual plans and rendered intermediate 2D states, enabling progressive shape assembly without external engines. The approach uses a large-scale dataset (SoT-26K) of grounded assembly traces from CAD hierarchies and introduces T2S-CompBench for evaluation.

Result: Fine-tuning on SoT-26K achieves 88.4% on component numeracy and 84.8% on structural topology, outperforming text-only baselines by around 20%. The method establishes a new paradigm for transparent, process-supervised compositional generation.

Conclusion: SoT provides an effective visual chain-of-thought framework that improves compositional reasoning in text-to-image generation through progressive shape assembly and intermediate state supervision, addressing key limitations in current multimodal models.

Abstract: Multimodal models for text-to-image generation have achieved strong visual fidelity, yet they remain brittle under compositional structural constraints-notably generative numeracy, attribute binding, and part-level relations. To address these challenges, we propose Shape-of-Thought (SoT), a visual CoT framework that enables progressive shape assembly via coherent 2D projections without external engines at inference time. SoT trains a unified multimodal autoregressive model to generate interleaved textual plans and rendered intermediate states, helping the model capture shape-assembly logic without producing explicit geometric representations. To support this paradigm, we introduce SoT-26K, a large-scale dataset of grounded assembly traces derived from part-based CAD hierarchies, and T2S-CompBench, a benchmark for evaluating structural integrity and trace faithfulness. Fine-tuning on SoT-26K achieves 88.4% on component numeracy and 84.8% on structural topology, outperforming text-only baselines by around 20%. SoT establishes a new paradigm for transparent, process-supervised compositional generation. The code is available at https://anonymous.4open.science/r/16FE/. The SoT-26K dataset will be released upon acceptance.

Method: Four-component AI framework: (1) YOLO-based instrument detection, (2) DeepSORT-based instrument tracking, (3) shape descriptor-based instrument tip localization, (4) supervised classification trained on expert-labeled data for proficiency evaluation.

Result: Achieved 97% instrument detection precision with 96% mean Average Precision (mAP50-95) measured by Intersection over Union thresholds from 50% to 95%.

Conclusion: Proposed AI framework effectively automates assessment of microanastomosis instrument handling skills, addressing limitations of traditional subjective evaluation methods.

Abstract: Proficiency in microanastomosis is a fundamental competency across multiple microsurgical disciplines. These procedures demand exceptional precision and refined technical skills, making effective, standardized assessment methods essential. Traditionally, the evaluation of microsurgical techniques has relied heavily on the subjective judgment of expert raters. They are inherently constrained by limitations such as inter-rater variability, lack of standardized evaluation criteria, susceptibility to cognitive bias, and the time-intensive nature of manual review. These shortcomings underscore the urgent need for an objective, reliable, and automated system capable of assessing microsurgical performance with consistency and scalability. To bridge this gap, we propose a novel AI framework for the automated assessment of microanastomosis instrument handling skills. The system integrates four core components: (1) an instrument detection module based on the You Only Look Once (YOLO) architecture; (2) an instrument tracking module developed from Deep Simple Online and Realtime Tracking (DeepSORT); (3) an instrument tip localization module employing shape descriptors; and (4) a supervised classification module trained on expert-labeled data to evaluate instrument handling proficiency. Experimental results demonstrate the effectiveness of the framework, achieving an instrument detection precision of 97%, with a mean Average Precision (mAP) of 96%, measured by Intersection over Union (IoU) thresholds ranging from 50% to 95% (mAP50-95).

Method: Proposes SDCI with three key components: 1) Cross-model attention fusion (CAF) module that injects self-attention maps between CLIP and vision foundation models, 2) Bidirectional cross-graph diffusion refinement (BCDR) module for iterative score enhancement, and 3) Convex-optimization-based superpixel collaborative prediction (CSCP) for boundary refinement.

Result: Experiments on multiple remote sensing semantic segmentation benchmarks show SDCI outperforms existing approaches. Ablation studies confirm traditional superpixel-based methods remain effective within deep learning frameworks.

Conclusion: SDCI effectively addresses the challenges of high-resolution remote sensing imagery through collaborative inference and boundary refinement, demonstrating the continued relevance of traditional object-based methods in modern deep learning frameworks.

Abstract: High-resolution remote sensing imagery is characterized by densely distributed land-cover objects and complex boundaries, which places higher demands on both geometric localization and semantic prediction. Existing training-free open-vocabulary semantic segmentation (OVSS) methods typically fuse CLIP and vision foundation models (VFMs) using “one-way injection” and “shallow post-processing” strategies, making it difficult to satisfy these requirements. To address this issue, we propose a spatial-regularization-aware dual-branch collaborative inference framework for training-free OVSS, termed SDCI. First, during feature encoding, SDCI introduces a cross-model attention fusion (CAF) module, which guides collaborative inference by injecting self-attention maps into each other. Second, we propose a bidirectional cross-graph diffusion refinement (BCDR) module that enhances the reliability of dual-branch segmentation scores through iterative random-walk diffusion. Finally, we incorporate low-level superpixel structures and develop a convex-optimization-based superpixel collaborative prediction (CSCP) mechanism to further refine object boundaries. Experiments on multiple remote sensing semantic segmentation benchmarks demonstrate that our method achieves better performance than existing approaches. Moreover, ablation studies further confirm that traditional object-based remote sensing image analysis methods leveraging superpixel structures remain effective within deep learning frameworks. Code: https://github.com/yu-ni1989/SDCI.

Method: The proposed GeoDiff-LF framework builds upon SD-Turbo and includes three key adaptations: (1) a modified U-Net architecture with convolutional and attention adapters to model geometric cues from light field data, (2) a geometry-guided loss function using tensor decomposition and progressive weighting to regularize global structure, and (3) an optimized sampling strategy with noise prediction to improve efficiency.

Result: Extensive experiments demonstrate that GeoDiff-LF outperforms existing methods in both visual fidelity and quantitative performance metrics, effectively mitigating color distortion in underwater scenes and advancing the state-of-the-art in underwater imaging enhancement.

Conclusion: GeoDiff-LF successfully integrates diffusion priors with light field geometry to enhance underwater 4-D light field imaging, providing a novel framework that addresses color distortion challenges and improves image quality beyond existing methods.

Abstract: This work studies the challenging problem of acquiring high-quality underwater images via 4-D light field (LF) imaging. To this end, we propose GeoDiff-LF, a novel diffusion-based framework built upon SD-Turbo to enhance underwater 4-D LF imaging by leveraging its spatial-angular structure. GeoDiff-LF consists of three key adaptations: (1) a modified U-Net architecture with convolutional and attention adapters to model geometric cues, (2) a geometry-guided loss function using tensor decomposition and progressive weighting to regularize global structure, and (3) an optimized sampling strategy with noise prediction to improve efficiency. By integrating diffusion priors and LF geometry, GeoDiff-LF effectively mitigates color distortion in underwater scenes. Extensive experiments demonstrate that our framework outperforms existing methods across both visual fidelity and quantitative performance, advancing the state-of-the-art in enhancing underwater imaging. The code will be publicly available at https://github.com/linlos1234/GeoDiff-LF.

Method: FRISM uses subspace-level model merging via Singular Value Decomposition (SVD) to decompose LRM task vectors, then adaptively tunes scaling coefficients for each subspace. It employs label-free self-distillation learning with dual-objective optimization using common vision-language perception datasets.

Result: Extensive experiments show FRISM effectively improves reasoning capabilities without compromising original visual capabilities, achieving state-of-the-art performance across diverse visual reasoning benchmarks.

Conclusion: FRISM demonstrates that fine-grained subspace-level merging is an effective approach for enhancing VLMs with reasoning capabilities while preserving their visual perception abilities, outperforming coarse-grained layer-level methods.

Abstract: Efficiently enhancing the reasoning capabilities of Vision-Language Models (VLMs) by merging them with Large Reasoning Models (LRMs) has emerged as a promising direction. However, existing methods typically operate at a coarse-grained layer level, which often leads to a trade-off between injecting reasoning capabilities and preserving visual capabilities. To address this limitation, we propose {FRISM} (Fine-grained Reasoning Injection via Subspace-level model Merging), a fine-grained reasoning injection framework based on subspace-level model merging. Observing that reasoning capabilities are encoded in distinct subspaces, FRISM decomposes LRM task vectors via Singular Value Decomposition (SVD) and adaptively tunes the scaling coefficients of each subspace through learning to realize fine-grained reasoning injection. Furthermore, we introduce a label-free self-distillation learning strategy with a dual-objective optimization using common vision-language perception datasets. Extensive experiments demonstrate that FRISM effectively improves reasoning capabilities without compromising the model’s original visual capabilities by consistently achieving state-of-the-art performance across diverse visual reasoning benchmarks.

Method: Proposes GRDR with: 1) query-guided multi-view tokenizer assigning multiple semantic IDs per video for diverse semantic access, 2) joint training of tokenizer and generative retriever via shared codebook to align text and video semantics, and 3) trie-constrained decoding for compact candidate generation followed by dense reranking.

Result: GRDR matches strong dense retrievers in accuracy while reducing index storage by an order of magnitude and accelerating full-corpus retrieval up to 300× on TVR benchmarks.

Conclusion: GRDR effectively addresses limitations of generative retrieval for recall in two-stage TVR, achieving high accuracy with dramatically improved efficiency through multi-view semantic IDs and joint cross-modal training.

Abstract: Text-to-Video Retrieval (TVR) is essential in video platforms. Dense retrieval with dual-modality encoders leads in accuracy, but its computation and storage scale poorly with corpus size. Thus, real-time large-scale applications adopt two-stage retrieval, where a fast recall model gathers a small candidate pool, which is reranked by an advanced dense retriever. Due to hugely reduced candidates, the reranking model can use any off-the-shelf dense retriever without hurting efficiency, meaning the recall model bounds two-stage TVR performance. Recently, generative retrieval (GR) replaces dense video embeddings with discrete semantic IDs and retrieves by decoding text queries into ID tokens. GR offers near-constant inference and storage complexity, and its semantic IDs capture high-level video features via quantization, making it ideal for quickly eliminating irrelevant candidates during recall. However, as a recall model in two-stage TVR, GR suffers from (i) semantic ambiguity, where each video satisfies diverse queries but is forced into one semantic ID; and (ii) cross-modal misalignment, as semantic IDs are solely derived from visual features without text supervision. We propose Generative Recall and Dense Reranking (GRDR), designing a novel GR method to uplift recalled candidate quality. GRDR assigns multiple semantic IDs to each video using a query-guided multi-view tokenizer exposing diverse semantic access paths, and jointly trains the tokenizer and generative retriever via a shared codebook to cast semantic IDs as the semantic bridge between texts and videos. At inference, trie-constrained decoding generates a compact candidate set reranked by a dense model for fine-grained matching. Experiments on TVR benchmarks show GRDR matches strong dense retrievers in accuracy while reducing index storage by an order of magnitude and accelerating up to 300$\times$ in full-corpus retrieval.

Method: 1) Construct large-scale dataset for robotic perception and reasoning including ego-view videos, visual grounding, spatial understanding, and chain-of-thought data. 2) Introduce simple yet effective approach that jointly incorporates key frames and full video sequences as inputs to enhance video comprehension.

Result: Achieves state-of-the-art results on two of the most commonly used benchmark datasets in the field of task planning.

Conclusion: Thinker effectively addresses fundamental challenges in applying vision-language models to robotics through specialized dataset construction and improved video comprehension techniques.

Abstract: When large vision-language models are applied to the field of robotics, they encounter problems that are simple for humans yet error-prone for models. Such issues include confusion between third-person and first-person perspectives and a tendency to overlook information in video endings during temporal reasoning. To address these challenges, we propose Thinker, a large vision-language foundation model designed for embodied intelligence. We tackle the aforementioned issues from two perspectives. Firstly, we construct a large-scale dataset tailored for robotic perception and reasoning, encompassing ego-view videos, visual grounding, spatial understanding, and chain-of-thought data. Secondly, we introduce a simple yet effective approach that substantially enhances the model’s capacity for video comprehension by jointly incorporating key frames and full video sequences as inputs. Our model achieves state-of-the-art results on two of the most commonly used benchmark datasets in the field of task planning.

Method: LAMP uses attention-based constraints to prevent cross-image information aggregation, cross-image contagious constraints to spread adversarial effects, and index-attention suppression for position-invariant attacks.

Result: LAMP outperforms state-of-the-art baselines and achieves highest attack success rates across multiple vision-language tasks and models.

Conclusion: LAMP demonstrates effective black-box adversarial attacks on multi-image MLLMs, revealing security vulnerabilities in multimodal systems.

Abstract: Multimodal Large Language Models (MLLMs) have achieved remarkable performance across vision-language tasks. Recent advancements allow these models to process multiple images as inputs. However, the vulnerabilities of multi-image MLLMs remain unexplored. Existing adversarial attacks focus on single-image settings and often assume a white-box threat model, which is impractical in many real-world scenarios. This paper introduces LAMP, a black-box method for learning Universal Adversarial Perturbations (UAPs) targeting multi-image MLLMs. LAMP applies an attention-based constraint that prevents the model from effectively aggregating information across images. LAMP also introduces a novel cross-image contagious constraint that forces perturbed tokens to influence clean tokens, spreading adversarial effects without requiring all inputs to be modified. Additionally, an index-attention suppression loss enables a robust position-invariant attack. Experimental results show that LAMP outperforms SOTA baselines and achieves the highest attack success rates across multiple vision-language tasks and models.

Method: Proposes PTQ4ARVG with three components: (1) Gain-Projected Scaling (GPS) to mitigate channel-wise outliers by expanding quantization loss via Taylor series and deriving optimal scaling factors; (2) Static Token-Wise Quantization (STWQ) that leverages ARVG’s fixed token length and position-invariant distributions; (3) Distribution-Guided Calibration (DGC) that selects samples contributing most to distributional entropy.

Result: Extensive experiments show PTQ4ARVG can effectively quantize ARVG family models to 8-bit and 6-bit while maintaining competitive performance.

Conclusion: PTQ4ARVG successfully addresses the unique quantization challenges of ARVG models through a training-free framework that handles channel-wise outliers, token-wise variance, and sample-wise distribution mismatches.

Abstract: AutoRegressive Visual Generation (ARVG) models retain an architecture compatible with language models, while achieving performance comparable to diffusion-based models. Quantization is commonly employed in neural networks to reduce model size and computational latency. However, applying quantization to ARVG remains largely underexplored, and existing quantization methods fail to generalize effectively to ARVG models. In this paper, we explore this issue and identify three key challenges: (1) severe outliers at channel-wise level, (2) highly dynamic activations at token-wise level, and (3) mismatched distribution information at sample-wise level. To these ends, we propose PTQ4ARVG, a training-free post-training quantization (PTQ) framework consisting of: (1) Gain-Projected Scaling (GPS) mitigates the channel-wise outliers, which expands the quantization loss via a Taylor series to quantify the gain of scaling for activation-weight quantization, and derives the optimal scaling factor through differentiation.(2) Static Token-Wise Quantization (STWQ) leverages the inherent properties of ARVG, fixed token length and position-invariant distribution across samples, to address token-wise variance without incurring dynamic calibration overhead.(3) Distribution-Guided Calibration (DGC) selects samples that contribute most to distributional entropy, eliminating the sample-wise distribution mismatch. Extensive experiments show that PTQ4ARVG can effectively quantize the ARVG family models to 8-bit and 6-bit while maintaining competitive performance. Code is available at http://github.com/BienLuky/PTQ4ARVG .

Method: 1) Learn compact video latent space via spatiotemporal VAE with high compression ratio; 2) Introduce ID-Context Cache mechanism (ID-Sink + Context-Cache) for semi-autoregressive streaming with identity consistency and temporal coherence; 3) Propose Asynchronous Streaming Distillation strategy using non-streaming teacher to supervise streaming student and mitigate error accumulation.

Result: REST outperforms state-of-the-art methods in both generation speed and overall performance, achieving real-time streaming audio-driven talking head generation.

Conclusion: REST bridges autoregressive and diffusion-based approaches, achieving breakthrough efficiency for real-time talking head generation applications.

Abstract: Diffusion models have significantly advanced the field of talking head generation (THG). However, slow inference speeds and prevalent non-autoregressive paradigms severely constrain the application of diffusion-based THG models. In this study, we propose REST, a pioneering diffusion-based, real-time, end-to-end streaming audio-driven talking head generation framework. To support real-time end-to-end generation, a compact video latent space is first learned through a spatiotemporal variational autoencoder with a high compression ratio. Additionally, to enable semi-autoregressive streaming within the compact video latent space, we introduce an ID-Context Cache mechanism, which integrates ID-Sink and Context-Cache principles into key-value caching for maintaining identity consistency and temporal coherence during long-term streaming generation. Furthermore, an Asynchronous Streaming Distillation (ASD) strategy is proposed to mitigate error accumulation and enhance temporal consistency in streaming generation, leveraging a non-streaming teacher with an asynchronous noise schedule to supervise the streaming student. REST bridges the gap between autoregressive and diffusion-based approaches, achieving a breakthrough in efficiency for applications requiring real-time THG. Experimental results demonstrate that REST outperforms state-of-the-art methods in both generation speed and overall performance.

Method: Proposes Noise Frequency-Controlled Diffusion Sampling (NFCDS) with a spectral modulation mechanism. Key insight: fidelity-perception conflict can be understood through noise frequency - low-frequency noise causes blur/fidelity loss, high-frequency drives detail generation. Uses Fourier-domain filter to progressively suppress low-frequency noise while preserving high-frequency content.

Result: Enables fast convergence to results that are both high-fidelity and perceptually convincing. As a PnP module, it integrates into existing diffusion-based restoration frameworks and improves fidelity-perception balance across diverse zero-shot tasks.

Conclusion: NFCDS provides a training-free solution to the fidelity-perception trade-off in diffusion sampling by controlling noise frequency, making it a versatile plug-and-play module for diffusion-based image restoration.

Abstract: Diffusion sampling-based Plug-and-Play (PnP) methods produce images with high perceptual quality but often suffer from reduced data fidelity, primarily due to the noise introduced during reverse diffusion. To address this trade-off, we propose Noise Frequency-Controlled Diffusion Sampling (NFCDS), a spectral modulation mechanism for reverse diffusion noise. We show that the fidelity-perception conflict can be fundamentally understood through noise frequency: low-frequency components induce blur and degrade fidelity, while high-frequency components drive detail generation. Based on this insight, we design a Fourier-domain filter that progressively suppresses low-frequency noise and preserves high-frequency content. This controlled refinement injects a data-consistency prior directly into sampling, enabling fast convergence to results that are both high-fidelity and perceptually convincing–without additional training. As a PnP module, NFCDS seamlessly integrates into existing diffusion-based restoration frameworks and improves the fidelity-perception balance across diverse zero-shot tasks.

Method: Hypersolid treats representation learning as a discrete packing problem, using short-range hard-ball repulsion to prevent local collisions between representations. This maintains injectivity by ensuring high separation in the geometric space.

Result: The method preserves augmentation diversity and excels on fine-grained and low-resolution classification tasks by creating a high-separation geometric regime that prevents representation collapse.

Conclusion: Local geometric constraints via hard-ball repulsion provide an effective alternative to global regularization for preventing representation collapse in self-supervised learning, particularly beneficial for fine-grained and low-resolution tasks.

Abstract: A recurring challenge in self-supervised learning is preventing representation collapse. Existing solutions typically rely on global regularization, such as maximizing distances, decorrelating dimensions or enforcing certain distributions. We instead reinterpret representation learning as a discrete packing problem, where preserving information simplifies to maintaining injectivity. We operationalize this in Hypersolid, a method using short-range hard-ball repulsion to prevent local collisions. This constraint results in a high-separation geometric regime that preserves augmentation diversity, excelling on fine-grained and low-resolution classification tasks.

Method: Integrates FLAME-based parametric modeling with 3DGS neural rendering. Transmits only essential facial metadata in real time, uses Gaussian-based head model reconstruction, and introduces compact representation with Gaussian attribute compression and MLP optimization.

Result: Achieves superior rate-distortion performance, delivering high-quality facial rendering at extremely low bitrates, suitable for real-time 3D video conferencing applications.

Conclusion: Proposed framework successfully addresses the challenge of high-fidelity 3D talking face compression at low bitrates, making real-time immersive communication more feasible.

Abstract: The demand for immersive and interactive communication has driven advancements in 3D video conferencing, yet achieving high-fidelity 3D talking face representation at low bitrates remains a challenge. Traditional 2D video compression techniques fail to preserve fine-grained geometric and appearance details, while implicit neural rendering methods like NeRF suffer from prohibitive computational costs. To address these challenges, we propose a lightweight, high-fidelity, low-bitrate 3D talking face compression framework that integrates FLAME-based parametric modeling with 3DGS neural rendering. Our approach transmits only essential facial metadata in real time, enabling efficient reconstruction with a Gaussian-based head model. Additionally, we introduce a compact representation and compression scheme, including Gaussian attribute compression and MLP optimization, to enhance transmission efficiency. Experimental results demonstrate that our method achieves superior rate-distortion performance, delivering high-quality facial rendering at extremely low bitrates, making it well-suited for real-time 3D video conferencing applications.

Method: Created first benchmark for geolocation reasoning chains using GeoGuessr game with Google Street View. Collaborated with expert players to produce 800 ground truth reasoning chains for 500 scenes. Evaluated LLM-as-a-judge and VLM-as-a-judge strategies for scoring VLM-generated reasoning chains.

Result: Qwen 3 LLM-as-a-judge correlates best with human scoring. Large closed-source VLMs (Gemini, GPT 5) rival human experts at location prediction but lag in producing auditable reasoning chains. Open-weight VLMs (Llama, Qwen) catastrophically fail, performing only slightly better than hallucinated reasoning chains with oracle location knowledge.

Conclusion: Gap between human experts and VLMs points to VLM limitations in extracting fine-grained visual attributes from high-resolution images. Benchmark reveals critical need for improved visual reasoning capabilities in multimodal models.

Abstract: Vision Language Models (VLMs) are good at recognizing the global location of a photograph – their geolocation prediction accuracy rivals the best human experts. But many VLMs are startlingly bad at explaining which image evidence led to their prediction, even when their location prediction is correct. The reasoning chains produced by VLMs frequently hallucinate scene attributes to support their location prediction (e.g. phantom writing, imagined infrastructure, misidentified flora). In this paper, we introduce the first benchmark for geolocation reasoning chains. We focus on the global location prediction task in the popular GeoGuessr game which draws from Google Street View spanning more than 100 countries. We collaborate with expert GeoGuessr players, including the reigning world champion, to produce 800 ground truth reasoning chains for 500 query scenes. These expert reasoning chains address hundreds of different discriminative visual attributes such as license plate shape, architecture, and soil properties to name just a few. We evaluate LLM-as-a-judge and VLM-as-a-judge strategies for scoring VLM-generated reasoning chains against our expert reasoning chains and find that Qwen 3 LLM-as-a-judge correlates best with human scoring. Our benchmark reveals that while large, closed-source VLMs such as Gemini and GPT 5 rival human experts at prediction locations, they still lag behind human experts when it comes to producing auditable reasoning chains. Open weights VLMs such as Llama and Qwen catastrophically fail on our benchmark – they perform only slightly better than a baseline in which an LLM hallucinates a reasoning chain with oracle knowledge of the photo location but no visual information at all. We believe the gap between human experts and VLMs on this task points to VLM limitations at extracting fine-grained visual attributes from high resolution images.

Method: Proposes a multimodal classification and matching approach fusing video footage, antenna geometric features, and PCI signals. Introduces a dedicated training framework with Token Entropy Regularization module during pretraining to address representation alignment challenges between antenna images and PCI signals.

Result: Experiments show TER accelerates convergence and yields significant performance gains. Analysis reveals that the entropy of the first token is modality-dependent, validating the approach’s effectiveness.

Conclusion: The proposed multimodal framework with TER successfully addresses antenna affiliation identification, offering an automated solution that outperforms manual methods and existing pretrained transformers for this specific communication domain task.

Abstract: Accurate antenna affiliation identification is crucial for optimizing and maintaining communication networks. Current practice, however, relies on the cumbersome and error-prone process of manual tower inspections. We propose a novel paradigm shift that fuses video footage of base stations, antenna geometric features, and Physical Cell Identity (PCI) signals, transforming antenna affiliation identification into multi-modal classification and matching tasks. Publicly available pretrained transformers struggle with this unique task due to a lack of analogous data in the communications domain, which hampers cross-modal alignment. To address this, we introduce a dedicated training framework that aligns antenna images with corresponding PCI signals. To tackle the representation alignment challenge, we propose a novel Token Entropy Regularization module in the pretraining stage. Our experiments demonstrate that TER accelerates convergence and yields significant performance gains. Further analysis reveals that the entropy of the first token is modality-dependent. Code will be made available upon publication.

Method: Introduces WorldBench with two-level evaluation: 1) intuitive physical understanding (object permanence, scale/perspective) and 2) low-level physical constants (friction coefficients, fluid viscosity). Uses concept-specific, disentangled evaluation to isolate and assess single physical concepts at a time.

Result: When evaluating SOTA video-based world models on WorldBench, specific patterns of failure in particular physics concepts were identified. All tested models lacked the physical consistency required for reliable real-world interactions.

Conclusion: WorldBench provides a more nuanced and scalable framework for evaluating physical reasoning in video generation and world models, enabling better assessment of models for robust world-model-driven learning applications.

Abstract: Recent advances in generative foundational models, often termed “world models,” have propelled interest in applying them to critical tasks like robotic planning and autonomous system training. For reliable deployment, these models must exhibit high physical fidelity, accurately simulating real-world dynamics. Existing physics-based video benchmarks, however, suffer from entanglement, where a single test simultaneously evaluates multiple physical laws and concepts, fundamentally limiting their diagnostic capability. We introduce WorldBench, a novel video-based benchmark specifically designed for concept-specific, disentangled evaluation, allowing us to rigorously isolate and assess understanding of a single physical concept or law at a time. To make WorldBench comprehensive, we design benchmarks at two different levels: 1) an evaluation of intuitive physical understanding with concepts such as object permanence or scale/perspective, and 2) an evaluation of low-level physical constants and material properties such as friction coefficients or fluid viscosity. When SOTA video-based world models are evaluated on WorldBench, we find specific patterns of failure in particular physics concepts, with all tested models lacking the physical consistency required to generate reliable real-world interactions. Through its concept-specific evaluation, WorldBench offers a more nuanced and scalable framework for rigorously evaluating the physical reasoning capabilities of video generation and world models, paving the way for more robust and generalizable world-model-driven learning.

Method: Proposes Gaussian Belief Propagation Network (GBPN) that integrates deep learning with probabilistic graphical models. Uses Graphical Model Construction Network (GMCN) to dynamically construct scene-specific Markov Random Fields with adaptive non-local edges, then performs inference via Gaussian Belief Propagation with serial & parallel message passing.

Result: Achieves state-of-the-art performance on NYUv2 and KITTI benchmarks. Shows superior performance, robustness, and generalizable capability across varying sparsity levels, patterns, and datasets.

Conclusion: GBPN effectively addresses the challenge of sparse and irregular depth data in depth completion by synergistically combining deep learning with probabilistic graphical models, demonstrating strong performance and robustness.

Abstract: Depth completion aims to predict a dense depth map from a color image with sparse depth measurements. Although deep learning methods have achieved state-of-the-art (SOTA), effectively handling the sparse and irregular nature of input depth data in deep networks remains a significant challenge, often limiting performance, especially under high sparsity. To overcome this limitation, we introduce the Gaussian Belief Propagation Network (GBPN), a novel hybrid framework synergistically integrating deep learning with probabilistic graphical models for end-to-end depth completion. Specifically, a scene-specific Markov Random Field (MRF) is dynamically constructed by the Graphical Model Construction Network (GMCN), and then inferred via Gaussian Belief Propagation (GBP) to yield the dense depth distribution. Crucially, the GMCN learns to construct not only the data-dependent potentials of MRF but also its structure by predicting adaptive non-local edges, enabling the capture of complex, long-range spatial dependencies. Furthermore, we enhance GBP with a serial & parallel message passing scheme, designed for effective information propagation, particularly from sparse measurements. Extensive experiments demonstrate that GBPN achieves SOTA performance on the NYUv2 and KITTI benchmarks. Evaluations across varying sparsity levels, sparsity patterns, and datasets highlight GBPN’s superior performance, notable robustness, and generalizable capability.

Method: Proposes Mam-App, a parameter-efficient Mamba-based model for feature extraction and leaf disease classification, designed to be extremely lightweight while maintaining competitive performance.

Result: Achieves 99.58% accuracy, 99.30% precision, 99.14% recall, and 99.22% F1-score on Apple Leaf Disease dataset with only 0.051M parameters. Also shows strong performance on Corn (99.48% accuracy) and Potato (98.46% accuracy) leaf disease datasets.

Conclusion: The proposed Mam-App model successfully balances efficiency and performance, making it suitable for deployment on drones, mobile devices, and other low-resource platforms for agricultural disease detection.

Abstract: The rapid growth of the global population, alongside exponential technological advancement, has intensified the demand for food production. Meeting this demand depends not only on increasing agricultural yield but also on minimizing food loss caused by crop diseases. Diseases account for a substantial portion of apple production losses, despite apples being among the most widely produced and nutritionally valuable fruits worldwide. Previous studies have employed machine learning techniques for feature extraction and early diagnosis of apple leaf diseases, and more recently, deep learning-based models have shown remarkable performance in disease recognition. However, most state-of-the-art deep learning models are highly parameter-intensive, resulting in increased training and inference time. Although lightweight models are more suitable for user-friendly and resource-constrained applications, they often suffer from performance degradation. To address the trade-off between efficiency and performance, we propose Mam-App, a parameter-efficient Mamba-based model for feature extraction and leaf disease classification. The proposed approach achieves competitive state-of-the-art performance on the PlantVillage Apple Leaf Disease dataset, attaining 99.58% accuracy, 99.30% precision, 99.14% recall, and a 99.22% F1-score, while using only 0.051M parameters. This extremely low parameter count makes the model suitable for deployment on drones, mobile devices, and other low-resource platforms. To demonstrate the robustness and generalizability of the proposed model, we further evaluate it on the PlantVillage Corn Leaf Disease and Potato Leaf Disease datasets. The model achieves 99.48%, 99.20%, 99.34%, and 99.27% accuracy, precision, recall, and F1-score on the corn dataset and 98.46%, 98.91%, 95.39%, and 97.01% on the potato dataset, respectively.

Method: Proposes Latent Autoregressive Network (LANE) with compact autoregressive dependencies and Adaptive Computation Graph Reconfiguration (AdaGraph) strategy for spatiotemporal decoupling to accelerate inference.

Result: Achieves 6× improvement in maximum generatable sequence length compared to existing methods, with superior performance in generation speed, structural detail, and geometric consistency.

Conclusion: LANE provides an effective solution for high-quality 3D mesh generation with improved efficiency and detail expression capabilities.

Abstract: High-fidelity 3D meshes can be tokenized into one-dimension (1D) sequences and directly modeled using autoregressive approaches for faces and vertices. However, existing methods suffer from insufficient resource utilization, resulting in slow inference and the ability to handle only small-scale sequences, which severely constrains the expressible structural details. We introduce the Latent Autoregressive Network (LANE), which incorporates compact autoregressive dependencies in the generation process, achieving a $6\times$ improvement in maximum generatable sequence length compared to existing methods. To further accelerate inference, we propose the Adaptive Computation Graph Reconfiguration (AdaGraph) strategy, which effectively overcomes the efficiency bottleneck of traditional serial inference through spatiotemporal decoupling in the generation process. Experimental validation demonstrates that LANE achieves superior performance across generation speed, structural detail, and geometric consistency, providing an effective solution for high-quality 3D mesh generation.

Method: Formulate an optimal transport problem between a continuous base distribution and latent embeddings of training data, identify resulting singular boundaries, sample near these boundaries to construct geometrically grounded OOD samples (OTIS), and apply confidence suppression loss during training.

Result: Extensive experiments show the method significantly alleviates OOD overconfidence and outperforms state-of-the-art methods.

Conclusion: The proposed framework effectively leverages optimal transport geometry to address OOD overconfidence by targeting structurally uncertain regions for improved model calibration.

Abstract: Deep neural networks (DNNs) often produce overconfident predictions on out-of-distribution (OOD) inputs, undermining their reliability in open-world environments. Singularities in semi-discrete optimal transport (OT) mark regions of semantic ambiguity, where classifiers are particularly prone to unwarranted high-confidence predictions. Motivated by this observation, we propose a principled framework to mitigate OOD overconfidence by leveraging the geometry of OT-induced singular boundaries. Specifically, we formulate an OT problem between a continuous base distribution and the latent embeddings of training data, and identify the resulting singular boundaries. By sampling near these boundaries, we construct a class of OOD inputs, termed optimal transport-induced OOD samples (OTIS), which are geometrically grounded and inherently semantically ambiguous. During training, a confidence suppression loss is applied to OTIS to guide the model toward more calibrated predictions in structurally uncertain regions. Extensive experiments show that our method significantly alleviates OOD overconfidence and outperforms state-of-the-art methods.

Method: Used diffusion pseudotime (from single-cell transcriptomics) to probe whether foundation models organize disease states along coherent progression trajectories in representation space across four cancer progressions and six models.

Result: All pathology-specific models recovered trajectory orderings significantly exceeding null baselines, with vision-only models achieving highest fidelities (τ>0.78 on CRC-Serrated). Trajectory fidelity strongly predicted few-shot classification performance on held-out diseases (ρ=0.92).

Conclusion: Vision foundation models can implicitly learn continuous processes from static observations, and trajectory fidelity provides complementary measure of representation quality beyond downstream performance, applicable to domains beyond pathology.

Abstract: Vision foundation models trained on discretely sampled images achieve strong performance on classification benchmarks, yet whether their representations encode the continuous processes underlying their training data remains unclear. This question is especially pertinent in computational pathology, where we posit that models whose latent representations implicitly capture continuous disease progression may better reflect underlying biology, support more robust generalization, and enable quantitative analyses of features associated with disease transitions. Using diffusion pseudotime, a method developed to infer developmental trajectories from single-cell transcriptomics, we probe whether foundation models organize disease states along coherent progression directions in representation space. Across four cancer progressions and six models, we find that all pathology-specific models recover trajectory orderings significantly exceeding null baselines, with vision-only models achieving the highest fidelities $(τ> 0.78$ on CRC-Serrated). Model rankings by trajectory fidelity on reference diseases strongly predict few-shot classification performance on held-out diseases ($ρ= 0.92$), and exploratory analysis shows cell-type composition varies smoothly along inferred trajectories in patterns consistent with known stromal remodeling. Together, these results demonstrate that vision foundation models can implicitly learn to represent continuous processes from independent static observations, and that trajectory fidelity provides a complementary measure of representation quality beyond downstream performance. While demonstrated in pathology, this framework could be applied to other domains where continuous processes are observed through static snapshots.

Method: Proposes SR²-Net with enhance-then-rectify pipeline: (1) Hierarchical Spectral-Spatial Synergy Attention (H-S³A) for cross-band interactions, (2) Manifold Consistency Rectification (MCR) to constrain spectra to physically plausible manifold, and (3) degradation-consistency loss for data fidelity.

Result: Extensive experiments on multiple benchmarks with diverse backbones show consistent improvements in spectral fidelity and overall reconstruction quality with negligible computational overhead.

Conclusion: SR²-Net provides a lightweight, plug-and-play solution to improve spectral consistency in HSI-SR without modifying existing model architectures, addressing physical plausibility while maintaining flexibility.

Abstract: HSI-SR aims to enhance spatial resolution while preserving spectrally faithful and physically plausible characteristics. Recent methods have achieved great progress by leveraging spatial correlations to enhance spatial resolution. However, these methods often neglect spectral consistency across bands, leading to spurious oscillations and physically implausible artifacts. While spectral consistency can be addressed by designing the network architecture, it results in a loss of generality and flexibility. To address this issue, we propose a lightweight plug-and-play rectifier, physically priors Spectral Rectification Super-Resolution Network (SR$^{2}$-Net), which can be attached to a wide range of HSI-SR models without modifying their architectures. SR$^{2}$-Net follows an enhance-then-rectify pipeline consisting of (i) Hierarchical Spectral-Spatial Synergy Attention (H-S$^{3}$A) to reinforce cross-band interactions and (ii) Manifold Consistency Rectification (MCR) to constrain the reconstructed spectra to a compact, physically plausible spectral manifold. In addition, we introduce a degradation-consistency loss to enforce data fidelity by encouraging the degraded SR output to match the observed low resolution input. Extensive experiments on multiple benchmarks and diverse backbones demonstrate consistent improvements in spectral fidelity and overall reconstruction quality with negligible computational overhead. Our code will be released upon publication.

Method: Proposes Dynamical Adapter Fusion (DAF) grounded in PAC-Bayes theorem. Derives fusion mechanism integrating three components: optimized task-specific adapter parameters, previous global adapter parameters, and initialization parameters. Uses Taylor expansion of loss function to derive optimal fusion coefficients for balancing stability and plasticity. Includes Robust Initialization strategy to capture global knowledge patterns.

Result: Experiments on multiple CIL benchmarks demonstrate that DAF achieves state-of-the-art (SOTA) performance.

Conclusion: DAF successfully addresses challenges in class-incremental learning by creating a single robust global adapter through dynamic fusion, achieving optimal balance between stability and plasticity while avoiding catastrophic forgetting.

Abstract: Class-Incremental Learning (CIL) requires models to continuously acquire new classes without forgetting previously learned ones. A dominant paradigm involves freezing a pre-trained model and training lightweight, task-specific adapters. However, maintaining task-specific parameters hinders knowledge transfer and incurs high retrieval costs, while naive parameter fusion often leads to destructive interference and catastrophic forgetting. To address these challenges, we propose Dynamical Adapter Fusion (DAF) to construct a single robust global adapter. Grounded in the PAC-Bayes theorem, we derive a fusion mechanism that explicitly integrates three components: the optimized task-specific adapter parameters, the previous global adapter parameters, and the initialization parameters. We utilize the Taylor expansion of the loss function to derive the optimal fusion coefficients, dynamically achieving the best balance between stability and plasticity. Furthermore, we propose a Robust Initialization strategy to effectively capture global knowledge patterns. Experiments on multiple CIL benchmarks demonstrate that DAF achieves state-of-the-art (SOTA) performance.

Method: SGDS proactively guides activation space by governing orientation and rank of subspaces through targeted sparsification. It promotes knowledge transfer by encouraging similar classes to share compact activation subspaces, while assigning non-overlapping subspaces to dissimilar classes to prevent interference.

Result: Extensive experiments on various benchmark datasets demonstrate state-of-the-art performance, showing SGDS effectively mitigates interference without imposing rigid constraints on parameter space.

Conclusion: SGDS provides an effective approach to class-incremental learning by sculpting class-specific sparse subspaces in activation space, balancing plasticity and stability better than parameter-constraining methods.

Abstract: Class-Incremental Learning (CIL) requires a model to continually learn new classes without forgetting old ones. A common and efficient solution freezes a pre-trained model and employs lightweight adapters, whose parameters are often forced to be orthogonal to prevent inter-task interference. However, we argue that this parameter-constraining method is detrimental to plasticity. To this end, we propose Semantic-Guided Dynamic Sparsification (SGDS), a novel method that proactively guides the activation space by governing the orientation and rank of its subspaces through targeted sparsification. Specifically, SGDS promotes knowledge transfer by encouraging similar classes to share a compact activation subspace, while simultaneously preventing interference by assigning non-overlapping activation subspaces to dissimilar classes. By sculpting class-specific sparse subspaces in the activation space, SGDS effectively mitigates interference without imposing rigid constraints on the parameter space. Extensive experiments on various benchmark datasets demonstrate the state-of-the-art performance of SGDS.

Method: Two core components: 1) Geometry-Aware Lifting Module with dual-scan Mamba architecture for robust 2D-to-3D pose lifting using geometric cues, and 2) Motion-guided Reconstruction Network that processes kinematic patterns over time for temporal awareness.

Result: Sets new state-of-the-art on 3DPW, MPI-INF-3DHP, and Human3.6M benchmarks, outperforming existing methods in reconstruction accuracy and temporal consistency with superior computational efficiency.

Conclusion: HMRMamba introduces a new paradigm using SSMs for 3D human mesh recovery, effectively addressing architectural limitations of previous methods and achieving better physical plausibility and temporal coherence.

Abstract: Existing video-based 3D Human Mesh Recovery (HMR) methods often produce physically implausible results, stemming from their reliance on flawed intermediate 3D pose anchors and their inability to effectively model complex spatiotemporal dynamics. To overcome these deep-rooted architectural problems, we introduce HMRMamba, a new paradigm for HMR that pioneers the use of Structured State Space Models (SSMs) for their efficiency and long-range modeling prowess. Our framework is distinguished by two core contributions. First, the Geometry-Aware Lifting Module, featuring a novel dual-scan Mamba architecture, creates a robust foundation for reconstruction. It directly grounds the 2D-to-3D pose lifting process with geometric cues from image features, producing a highly reliable 3D pose sequence that serves as a stable anchor. Second, the Motion-guided Reconstruction Network leverages this anchor to explicitly process kinematic patterns over time. By injecting this crucial temporal awareness, it significantly enhances the final mesh’s coherence and robustness, particularly under occlusion and motion blur. Comprehensive evaluations on 3DPW, MPI-INF-3DHP, and Human3.6M benchmarks confirm that HMRMamba sets a new state-of-the-art, outperforming existing methods in both reconstruction accuracy and temporal consistency while offering superior computational efficiency.

Method: Proposes Geometry-Induced Query-Key Transformation (GIQT), a lightweight low-rank module that explicitly rectifies the similarity space by conditioning query-key interactions on camera geometry. Also includes a geometry-conditioned prompt generation mechanism for global, view-adaptive representation priors.

Result: Experiments on four aerial-ground person re-identification benchmarks show consistent improvement in robustness under extreme and previously unseen geometric conditions with minimal computational overhead compared to state-of-the-art methods.

Conclusion: Explicitly addressing geometry-induced distortions in the similarity space through GIQT significantly improves aerial-ground person re-identification performance under extreme viewpoint variations.

Abstract: Aerial-ground person re-identification (AG-ReID) is fundamentally challenged by extreme viewpoint and distance discrepancies between aerial and ground cameras, which induce severe geometric distortions and invalidate the assumption of a shared similarity space across views. Existing methods primarily rely on geometry-aware feature learning or appearance-conditioned prompting, while implicitly assuming that the geometry-invariant dot-product similarity used in attention mechanisms remains reliable under large viewpoint and scale variations. We argue that this assumption does not hold. Extreme camera geometry systematically distorts the query-key similarity space and degrades attention-based matching, even when feature representations are partially aligned. To address this issue, we introduce Geometry-Induced Query-Key Transformation (GIQT), a lightweight low-rank module that explicitly rectifies the similarity space by conditioning query-key interactions on camera geometry. Rather than modifying feature representations or the attention formulation itself, GIQT adapts the similarity computation to compensate for dominant geometry-induced anisotropic distortions. Building on this local similarity rectification, we further incorporate a geometry-conditioned prompt generation mechanism that provides global, view-adaptive representation priors derived directly from camera geometry. Experiments on four aerial-ground person re-identification benchmarks demonstrate that the proposed framework consistently improves robustness under extreme and previously unseen geometric conditions, while introducing minimal computational overhead compared to state-of-the-art methods.

Method: UniMRG is an architecture-agnostic post-training method that trains UMMs to generate multiple intrinsic representations of input images: pixel reconstruction (appearance), depth (geometry), and segmentation (structure), alongside standard visual understanding objectives.

Result: Extensive experiments show UniMRG notably enhances fine-grained perception, reduces hallucinations, improves spatial understanding, and simultaneously boosts generation capabilities across diverse UMM architectures.

Conclusion: By synthesizing diverse representations through auxiliary generation tasks, UMMs develop deeper and more comprehensive understanding of visual inputs, demonstrating that generation can effectively enhance understanding in multimodal models.

Abstract: Unified Multimodal Models (UMMs) integrate both visual understanding and generation within a single framework. Their ultimate aspiration is to create a cycle where understanding and generation mutually reinforce each other. While recent post-training methods have successfully leveraged understanding to enhance generation, the reverse direction of utilizing generation to improve understanding remains largely unexplored. In this work, we propose UniMRG (Unified Multi-Representation Generation), a simple yet effective architecture-agnostic post-training method. UniMRG enhances the understanding capabilities of UMMs by incorporating auxiliary generation tasks. Specifically, we train UMMs to generate multiple intrinsic representations of input images, namely pixel (reconstruction), depth (geometry), and segmentation (structure), alongside standard visual understanding objectives. By synthesizing these diverse representations, UMMs capture complementary information regarding appearance, spatial relations, and structural layout. Consequently, UMMs develop a deeper and more comprehensive understanding of visual inputs. Extensive experiments across diverse UMM architectures demonstrate that our method notably enhances fine-grained perception, reduces hallucinations, and improves spatial understanding, while simultaneously boosting generation capabilities.

Method: Proposes a hierarchical dual-path framework: 1) Static Manifold Deviation Branch uses Vision Foundation Models to detect spatial anomalies or physical violations deviating from natural real-world manifolds; 2) Micro-Temporal Fluctuation Branch analyzes structured “Manifold Projection Fluctuations” (MPF) - subtle computational fingerprints in pixel composition logic across consecutive frames.

Result: The framework successfully detects AI-generated videos by exposing forgeries through either global real-world manifold deviations or subtle computational fingerprints, even in visually perfect sequences that evade spatial detection.

Conclusion: AI-generated videos exhibit detectable structured patterns (MPF) due to their manifold-fitting nature, enabling hierarchical detection that combines spatial anomaly detection with fine-grained temporal fluctuation analysis.

Abstract: With the rapid advancement of video generation models such as Veo and Wan, the visual quality of synthetic content has reached a level where macro-level semantic errors and temporal inconsistencies are no longer prominent. However, this does not imply that the distinction between real and cutting-edge high-fidelity fake is untraceable. We argue that AI-generated videos are essentially products of a manifold-fitting process rather than a physical recording. Consequently, the pixel composition logic of consecutive adjacent frames residual in AI videos exhibits a structured and homogenous characteristic. We term this phenomenon `Manifold Projection Fluctuations’ (MPF). Driven by this insight, we propose a hierarchical dual-path framework that operates as a sequential filtering process. The first, the Static Manifold Deviation Branch, leverages the refined perceptual boundaries of Large-Scale Vision Foundation Models (VFMs) to capture residual spatial anomalies or physical violations that deviate from the natural real-world manifold (off-manifold). For the remaining high-fidelity videos that successfully reside on-manifold and evade spatial detection, we introduce the Micro-Temporal Fluctuation Branch as a secondary, fine-grained filter. By analyzing the structured MPF that persists even in visually perfect sequences, our framework ensures that forgeries are exposed regardless of whether they manifest as global real-world manifold deviations or subtle computational fingerprints.

Method: Identifies Interior Geometric Degradation (IGD) failure mode in implicit density fields. Proposes explicit geometric pipeline using Sparse Voxel Rasterization (SVRaster) initialized from SfM feature geometry. Projects 2D instance masks onto explicit voxel grid with recursive splitting for geometric separation.

Result: State-of-the-art mask-supervised NeRFs saturate at ~89% instance recovery in dense scenes. SVRaster achieves 95.8% recovery rate in dense clusters. Explicit SfM-based geometry recovers 43% more instances than implicit baselines under degraded segmentation masks.

Conclusion: Explicit geometric priors are essential for reliable quantitative analysis in highly self-occluding 3D scenes. Implicit methods like NeRFs suffer from fundamental limitations under heavy occlusion that explicit approaches can overcome.

Abstract: Neural Radiance Fields (NeRFs) have emerged as a powerful paradigm for multi-view reconstruction, complementing classical photogrammetric pipelines based on Structure-from-Motion (SfM) and Multi-View Stereo (MVS). However, their reliability for quantitative 3D analysis in dense, self-occluding scenes remains poorly understood. In this study, we identify a fundamental failure mode of implicit density fields under heavy occlusion, which we term Interior Geometric Degradation (IGD). We show that transmittance-based volumetric optimization satisfies photometric supervision by reconstructing hollow or fragmented structures rather than solid interiors, leading to systematic instance undercounting. Through controlled experiments on synthetic datasets with increasing occlusion, we demonstrate that state-of-the-art mask-supervised NeRFs saturate at approximately 89% instance recovery in dense scenes, despite improved surface coherence and mask quality. To overcome this limitation, we introduce an explicit geometric pipeline based on Sparse Voxel Rasterization (SVRaster), initialized from SfM feature geometry. By projecting 2D instance masks onto an explicit voxel grid and enforcing geometric separation via recursive splitting, our approach preserves physical solidity and achieves a 95.8% recovery rate in dense clusters. A sensitivity analysis using degraded segmentation masks further shows that explicit SfM-based geometry is substantially more robust to supervision failure, recovering 43% more instances than implicit baselines. These results demonstrate that explicit geometric priors are a prerequisite for reliable quantitative analysis in highly self-occluding 3D scenes.

Method: Uses MLLMs to generate synthetic image captions for unimodal datasets, employs carefully designed prompts with class labels and domain context, introduces supervised contrastive loss for clustering same-class representations, and uses class-averaged text embeddings from multiple synthetic captions per image for inference.

Result: Extensive experiments across 13 image classification benchmarks show the approach outperforms baseline methods, with particularly significant improvements in few-shot learning scenarios.

Conclusion: Establishes a new paradigm for dataset enhancement that effectively bridges the gap between multimodal pre-training and fine-tuning.

Abstract: In this paper, we address a fundamental gap between pre-training and fine-tuning of deep neural networks: while pre-training has shifted from unimodal to multimodal learning with enhanced visual understanding, fine-tuning predominantly remains unimodal, limiting the benefits of rich pre-trained representations. To bridge this gap, we propose a novel approach that transforms unimodal datasets into multimodal ones using Multimodal Large Language Models (MLLMs) to generate synthetic image captions for fine-tuning models with a multimodal objective. Our method employs carefully designed prompts incorporating class labels and domain context to produce high-quality captions tailored for classification tasks. Furthermore, we introduce a supervised contrastive loss function that explicitly encourages clustering of same-class representations during fine-tuning, along with a new inference technique that leverages class-averaged text embeddings from multiple synthetic captions per image. Extensive experiments across 13 image classification benchmarks demonstrate that our approach outperforms baseline methods, with particularly significant improvements in few-shot learning scenarios. Our work establishes a new paradigm for dataset enhancement that effectively bridges the gap between multimodal pre-training and fine-tuning. Our code is available at https://github.com/s-enmt/MMFT.

Method: Spava uses a sequence-parallel framework with optimized attention that distributes approximate attention computation across multiple GPUs. It reduces computation and increases parallelism, enabling efficient processing of more visual embeddings without compression. System-level optimizations include load balancing and fused forward passes.

Result: Spava achieves speedups of 12.72x over FlashAttn, 1.70x over ZigZagRing, and 1.18x over APB, without notable performance loss. The framework enables efficient processing of longer videos while maintaining model performance.

Conclusion: Spava effectively addresses the efficiency bottleneck in long-video inference for LMMs by leveraging multi-GPU parallelization and optimized attention mechanisms, enabling scalable processing of complex video content without compromising accuracy.

Abstract: The efficiency of long-video inference remains a critical bottleneck, mainly due to the dense computation in the prefill stage of Large Multimodal Models (LMMs). Existing methods either compress visual embeddings or apply sparse attention on a single GPU, yielding limited acceleration or degraded performance and restricting LMMs from handling longer, more complex videos. To overcome these issues, we propose Spava, a sequence-parallel framework with optimized attention that accelerates long-video inference across multiple GPUs. By distributing approximate attention, Spava reduces computation and increases parallelism, enabling efficient processing of more visual embeddings without compression and thereby improving task performance. System-level optimizations, such as load balancing and fused forward passes, further unleash the potential of Spava, delivering speedups of 12.72x, 1.70x, and 1.18x over FlashAttn, ZigZagRing, and APB, without notable performance loss. Code available at https://github.com/thunlp/APB

Method: Introduces VARIANCE (intra-/inter-class variance) and GREEDINESS (active ratio and gradient norms) diagnostic framework. Compares seven representative losses (Contrastive, Triplet, N-pair, InfoNCE, ArcFace, SCL, CCL) across five image-retrieval datasets.

Result: Triplet and SCL preserve higher within-class variance and clearer inter-class margins, leading to stronger top-1 retrieval in fine-grained settings. Contrastive and InfoNCE achieve compact embeddings quickly through many small updates but may oversimplify class structures. N-pair achieves large mean separation but with uneven spacing.

Conclusion: Reveals an efficiency-granularity trade-off: prefer Triplet/SCL for diversity preservation and hard-sample discrimination, and Contrastive/InfoNCE for faster embedding compaction. Provides practical guidance for loss selection in retrieval tasks.

Abstract: Metric learning is central to retrieval, yet its effects on embedding geometry and optimization dynamics are not well understood. We introduce a diagnostic framework, VARIANCE (intra-/inter-class variance) and GREEDINESS (active ratio and gradient norms), to compare seven representative losses, i.e., Contrastive, Triplet, N-pair, InfoNCE, ArcFace, SCL, and CCL, across five image-retrieval datasets. Our analysis reveals that Triplet and SCL preserve higher within-class variance and clearer inter-class margins, leading to stronger top-1 retrieval in fine-grained settings. In contrast, Contrastive and InfoNCE compact embeddings are achieved quickly through many small updates, accelerating convergence but potentially oversimplifying class structures. N-pair achieves a large mean separation but with uneven spacing. These insights reveal a form of efficiency-granularity trade-off and provide practical guidance: prefer Triplet/SCL when diversity preservation and hard-sample discrimination are critical, and Contrastive/InfoNCE when faster embedding compaction is desired.

Method: Uses Masked Autoencoder (MAE) trained exclusively on authentic data to learn intrinsic spatiotemporal patterns, then detects forgeries via reconstruction discrepancies. Introduces Asymmetric Intra-video Contrastive Loss (AICL) to focus on compactness of authentic features guided by reconstruction cues for stable decision boundaries.

Result: Extensive experiments on large-scale datasets including LAV-DF demonstrate state-of-the-art performance in weakly-supervised temporal forgery localization.

Conclusion: RT-DeepLoc effectively addresses the challenge of fine-grained temporal deepfake localization with only video-level supervision, achieving superior performance through reconstruction-based detection and contrastive learning.

Abstract: Modern deepfakes have evolved into localized and intermittent manipulations that require fine-grained temporal localization. The prohibitive cost of frame-level annotation makes weakly supervised methods a practical necessity, which rely only on video-level labels. To this end, we propose Reconstruction-based Temporal Deepfake Localization (RT-DeepLoc), a weakly supervised temporal forgery localization framework that identifies forgeries via reconstruction errors. Our framework uses a Masked Autoencoder (MAE) trained exclusively on authentic data to learn its intrinsic spatiotemporal patterns; this allows the model to produce significant reconstruction discrepancies for forged segments, effectively providing the missing fine-grained cues for localization. To robustly leverage these indicators, we introduce a novel Asymmetric Intra-video Contrastive Loss (AICL). By focusing on the compactness of authentic features guided by these reconstruction cues, AICL establishes a stable decision boundary that enhances local discrimination while preserving generalization to unseen forgeries. Extensive experiments on large-scale datasets, including LAV-DF, demonstrate that RT-DeepLoc achieves state-of-the-art performance in weakly-supervised temporal forgery localization.

Method: Formulates the task as a Multiple-instance Learning (MIL) problem and proposes HyperAdAgFormer - a hypernetwork-based adaptive aggregation transformer that modifies feature aggregation strategies for each patient based on tabular data through a hypernetwork.

Result: Experiments using clinical datasets demonstrated the effectiveness of HyperAdAgFormer for this medical imaging task.

Conclusion: The proposed HyperAdAgFormer successfully addresses the challenge of adaptive feature aggregation in medical imaging tasks where physician decisions depend on patient-specific tabular data.

Abstract: In this paper, we present the first attempt to estimate the necessity of debulking coronary artery calcifications from computed tomography (CT) images. We formulate this task as a Multiple-instance Learning (MIL) problem. The difficulty of this task lies in that physicians adjust their focus and decision criteria for device usage according to tabular data representing each patient’s condition. To address this issue, we propose a hypernetwork-based adaptive aggregation transformer (HyperAdAgFormer), which adaptively modifies the feature aggregation strategy for each patient based on tabular data through a hypernetwork. The experiments using the clinical dataset demonstrated the effectiveness of HyperAdAgFormer. The code is publicly available at https://github.com/Shiku-Kaito/HyperAdAgFormer.

Method: Introduces SimGraph, a unified framework that integrates scene graph-based image generation and editing. Combines token-based generation and diffusion-based editing within a single scene graph-driven model to ensure precise control over object interactions, layouts, and spatial coherence.

Result: Through extensive experiments, the approach empirically demonstrates that it outperforms existing state-of-the-art methods in both image generation and editing tasks.

Conclusion: SimGraph provides a unified solution for scene graph-based image generation and editing, offering greater control over composition and interactions while maintaining spatial consistency and semantic coherence.

Abstract: Recent advancements in Generative Artificial Intelligence (GenAI) have significantly enhanced the capabilities of both image generation and editing. However, current approaches often treat these tasks separately, leading to inefficiencies and challenges in maintaining spatial consistency and semantic coherence between generated content and edits. Moreover, a major obstacle is the lack of structured control over object relationships and spatial arrangements. Scene graph-based methods, which represent objects and their interrelationships in a structured format, offer a solution by providing greater control over composition and interactions in both image generation and editing. To address this, we introduce SimGraph, a unified framework that integrates scene graph-based image generation and editing, enabling precise control over object interactions, layouts, and spatial coherence. In particular, our framework integrates token-based generation and diffusion-based editing within a single scene graph-driven model, ensuring high-quality and consistent results. Through extensive experiments, we empirically demonstrate that our approach outperforms existing state-of-the-art methods.

Method: Fine-tunes base diffusion models via domain-specific expert adaptation using self-supervised image-text pairs generated by LLMs and T2I pipelines, creating separate experts for each damage category integrated into unified multi-damage model.

Result: Consistent improvements across four diffusion backbones: +5.5% in text faithfulness and +2.3% in human preference ratings compared to baselines.

Conclusion: HERS demonstrates both opportunities and risks of domain-specific diffusion, highlighting importance of trustworthy generation in safety-critical applications like auto insurance.

Abstract: Recent advances in text-to-image (T2I) diffusion models have enabled increasingly realistic synthesis of vehicle damage, raising concerns about their reliability in automated insurance workflows. The ability to generate crash-like imagery challenges the boundary between authentic and synthetic data, introducing new risks of misuse in fraud or claim manipulation. To address these issues, we propose HERS (Hidden-Pattern Expert Learning for Risk-Specific Damage Adaptation), a framework designed to improve fidelity, controllability, and domain alignment of diffusion-generated damage images. HERS fine-tunes a base diffusion model via domain-specific expert adaptation without requiring manual annotation. Using self-supervised image-text pairs automatically generated by a large language model and T2I pipeline, HERS models each damage category, such as dents, scratches, broken lights, or cracked paint, as a separate expert. These experts are later integrated into a unified multi-damage model that balances specialization with generalization. We evaluate HERS across four diffusion backbones and observe consistent improvements: plus 5.5 percent in text faithfulness and plus 2.3 percent in human preference ratings compared to baselines. Beyond image fidelity, we discuss implications for fraud detection, auditability, and safe deployment of generative models in high-stakes domains. Our findings highlight both the opportunities and risks of domain-specific diffusion, underscoring the importance of trustworthy generation in safety-critical applications such as auto insurance.

Method: Introduces Vision KAN (ViK) with MultiPatch-RBFKAN token mixer combining: (1) patch-wise nonlinear transform using Radial Basis Function-based KANs, (2) axis-wise separable mixing for local propagation, and (3) low-rank global mapping for long-range interaction. Uses patch-wise grouping strategy to handle high-resolution features efficiently.

Result: ViK achieves competitive accuracy on ImageNet-1K classification while maintaining linear complexity, demonstrating KAN-based token mixing as an efficient alternative to attention mechanisms.

Conclusion: KAN-based token mixing offers a theoretically grounded, efficient alternative to attention mechanisms in vision backbones, with linear complexity and competitive performance.

Abstract: Attention mechanisms have become a key module in modern vision backbones due to their ability to model long-range dependencies. However, their quadratic complexity in sequence length and the difficulty of interpreting attention weights limit both scalability and clarity. Recent attention-free architectures demonstrate that strong performance can be achieved without pairwise attention, motivating the search for alternatives. In this work, we introduce Vision KAN (ViK), an attention-free backbone inspired by the Kolmogorov-Arnold Networks. At its core lies MultiPatch-RBFKAN, a unified token mixer that combines (a) patch-wise nonlinear transform with Radial Basis Function-based KANs, (b) axis-wise separable mixing for efficient local propagation, and (c) low-rank global mapping for long-range interaction. Employing as a drop-in replacement for attention modules, this formulation tackles the prohibitive cost of full KANs on high-resolution features by adopting a patch-wise grouping strategy with lightweight operators to restore cross-patch dependencies. Experiments on ImageNet-1K show that ViK achieves competitive accuracy with linear complexity, demonstrating the potential of KAN-based token mixing as an efficient and theoretically grounded alternative to attention.

Method: Proposes Bi-Anchor Interpolation Solver (BA-solver) with two components: 1) Bidirectional Temporal Perception where a lightweight SideNet (1-2% of backbone size) learns to approximate future and historical velocities without retraining the backbone, and 2) Bi-Anchor Velocity Integration that uses the SideNet with two anchor velocities to efficiently approximate intermediate velocities for batched high-order integration.

Result: On ImageNet-256^2, BA-solver achieves generation quality comparable to 100+ NFE Euler solver in just 10 NFEs and maintains high fidelity in as few as 5 NFEs, with negligible training costs. It also enables seamless integration with existing generative pipelines for downstream tasks like image editing.

Conclusion: BA-solver bridges the gap between training-free and training-based acceleration methods for Flow Matching models, offering significant speedup with minimal training overhead while maintaining versatility and high-quality generation.

Abstract: Flow Matching (FM) models have emerged as a leading paradigm for high-fidelity synthesis. However, their reliance on iterative Ordinary Differential Equation (ODE) solving creates a significant latency bottleneck. Existing solutions face a dichotomy: training-free solvers suffer from significant performance degradation at low Neural Function Evaluations (NFEs), while training-based one- or few-steps generation methods incur prohibitive training costs and lack plug-and-play versatility. To bridge this gap, we propose the Bi-Anchor Interpolation Solver (BA-solver). BA-solver retains the versatility of standard training-free solvers while achieving significant acceleration by introducing a lightweight SideNet (1-2% backbone size) alongside the frozen backbone. Specifically, our method is founded on two synergistic components: \textbf{1) Bidirectional Temporal Perception}, where the SideNet learns to approximate both future and historical velocities without retraining the heavy backbone; and 2) Bi-Anchor Velocity Integration, which utilizes the SideNet with two anchor velocities to efficiently approximate intermediate velocities for batched high-order integration. By utilizing the backbone to establish high-precision ``anchors’’ and the SideNet to densify the trajectory, BA-solver enables large interval sizes with minimized error. Empirical results on ImageNet-256^2 demonstrate that BA-solver achieves generation quality comparable to 100+ NFEs Euler solver in just 10 NFEs and maintains high fidelity in as few as 5 NFEs, incurring negligible training costs. Furthermore, BA-solver ensures seamless integration with existing generative pipelines, facilitating downstream tasks such as image editing.

Method: Proposes Uncertainty-Aware Diffusion Bridge Model (UDBM) that reformulates AiOIR as stochastic transport steered by pixel-wise uncertainty. Uses relaxed diffusion bridge formulation to replace strict terminal constraints, models degradation uncertainty, and resolves drift singularity. Implements dual modulation strategy: noise schedule aligns degradations to shared high-entropy latent space, and path schedule adaptively regulates transport trajectory based on viscous dynamics of entropy regularization.

Result: Achieves state-of-the-art performance across diverse restoration tasks within a single inference step by effectively rectifying transport geometry and dynamics.

Conclusion: UDBM successfully addresses the fundamental challenge of conflicting optimization objectives in AiOIR through uncertainty-aware stochastic transport, providing superior adaptation across heterogeneous degradations.

Abstract: All-in-One Image Restoration (AiOIR) faces the fundamental challenge in reconciling conflicting optimization objectives across heterogeneous degradations. Existing methods are often constrained by coarse-grained control mechanisms or fixed mapping schedules, yielding suboptimal adaptation. To address this, we propose an Uncertainty-Aware Diffusion Bridge Model (UDBM), which innovatively reformulates AiOIR as a stochastic transport problem steered by pixel-wise uncertainty. By introducing a relaxed diffusion bridge formulation which replaces the strict terminal constraint with a relaxed constraint, we model the uncertainty of degradations while theoretically resolving the drift singularity inherent in standard diffusion bridges. Furthermore, we devise a dual modulation strategy: the noise schedule aligns diverse degradations into a shared high-entropy latent space, while the path schedule adaptively regulates the transport trajectory motivated by the viscous dynamics of entropy regularization. By effectively rectifying the transport geometry and dynamics, UDBM achieves state-of-the-art performance across diverse restoration tasks within a single inference step.

Method: Dual-microcontroller architecture using Arduino Uno for precision analog measurements with five-point calibration algorithms and ESP32 for wireless connectivity, edge processing, and cloud integration. Implements advanced signal processing including median filtering for TDS, temperature compensation algorithms, and robust error handling.

Result: Exceptional performance over 90 days: pH accuracy ±0.08 units, DO stability ±0.2 mg/L, TDS accuracy ±1.9% across 0-1000 ppm, 99.8% cloud data transmission reliability. Total cost 32,983 BDT (~300 USD), achieving 85% cost reduction compared to commercial systems.

Conclusion: HydroSense establishes a new paradigm for accessible environmental monitoring, demonstrating professional-grade water quality assessment can be achieved through intelligent system architecture and cost-effective component selection.

Abstract: The global water crisis necessitates affordable, accurate, and real-time water quality monitoring solutions. Traditional approaches relying on manual sampling or expensive commercial systems fail to address accessibility challenges in resource-constrained environments. This paper presents HydroSense, an innovative Internet of Things framework that integrates six critical water quality parameters including pH, dissolved oxygen (DO), temperature, total dissolved solids (TDS), estimated nitrogen, and water level into a unified monitoring system. HydroSense employs a novel dual-microcontroller architecture, utilizing Arduino Uno for precision analog measurements with five-point calibration algorithms and ESP32 for wireless connectivity, edge processing, and cloud integration. The system implements advanced signal processing techniques including median filtering for TDS measurement, temperature compensation algorithms, and robust error handling. Experimental validation over 90 days demonstrates exceptional performance metrics: pH accuracy of plus or minus 0.08 units across the 0 to 14 range, DO measurement stability within plus or minus 0.2 mg/L, TDS accuracy of plus or minus 1.9 percent across 0 to 1000 ppm, and 99.8 percent cloud data transmission reliability. With a total implementation cost of 32,983 BDT (approximately 300 USD), HydroSense achieves an 85 percent cost reduction compared to commercial systems while providing enhanced connectivity through the Firebase real-time database. This research establishes a new paradigm for accessible environmental monitoring, demonstrating that professional-grade water quality assessment can be achieved through intelligent system architecture and cost-effective component selection.

Method: Proposes WMVLM, a vision-language model-based framework that redefines quality and security metrics for each watermark type: residual watermarks evaluated by artifact strength and erasure resistance, semantic watermarks assessed through latent distribution shifts. Uses a three-stage training strategy for progressive learning of classification, scoring, and interpretable text generation.

Result: WMVLM outperforms state-of-the-art VLMs with strong generalization across datasets, diffusion models, and watermarking methods, demonstrating effective unified evaluation capabilities.

Conclusion: WMVLM provides the first unified and interpretable evaluation framework for diffusion model image watermarking, addressing critical gaps in existing evaluation methodologies through vision-language model capabilities.

Abstract: Digital watermarking is essential for securing generated images from diffusion models. Accurate watermark evaluation is critical for algorithm development, yet existing methods have significant limitations: they lack a unified framework for both residual and semantic watermarks, provide results without interpretability, neglect comprehensive security considerations, and often use inappropriate metrics for semantic watermarks. To address these gaps, we propose WMVLM, the first unified and interpretable evaluation framework for diffusion model image watermarking via vision-language models (VLMs). We redefine quality and security metrics for each watermark type: residual watermarks are evaluated by artifact strength and erasure resistance, while semantic watermarks are assessed through latent distribution shifts. Moreover, we introduce a three-stage training strategy to progressively enable the model to achieve classification, scoring, and interpretable text generation. Experiments show WMVLM outperforms state-of-the-art VLMs with strong generalization across datasets, diffusion models, and watermarking methods.

Method: Created PathReasoner dataset using medical knowledge graphs to align pathological findings with diagnoses. Developed PathReasoner-R1 model using trajectory-masked supervised fine-tuning with reasoning-oriented reinforcement learning, incorporating knowledge-aware multi-granular reward functions with Entity Reward mechanism.

Result: Achieves state-of-the-art performance on both PathReasoner dataset and public benchmarks across various image scales, providing transparent, clinically grounded reasoning capabilities.

Conclusion: PathReasoner-R1 successfully equips pathology models with verifiable evidence-linked reasoning, enhancing clinical trust through structured chain-of-thought capabilities aligned with medical knowledge graphs.

Abstract: Vision-Language Models (VLMs) are advancing computational pathology with superior visual understanding capabilities. However, current systems often reduce diagnosis to directly output conclusions without verifiable evidence-linked reasoning, which severely limits clinical trust and hinders expert error rectification. To address these barriers, we construct PathReasoner, the first large-scale dataset of whole-slide image (WSI) reasoning. Unlike previous work reliant on unverified distillation, we develop a rigorous knowledge-guided generation pipeline. By leveraging medical knowledge graphs, we explicitly align structured pathological findings and clinical reasoning with diagnoses, generating over 20K high-quality instructional samples. Based on the database, we propose PathReasoner-R1, which synergizes trajectory-masked supervised fine-tuning with reasoning-oriented reinforcement learning to instill structured chain-of-thought capabilities. To ensure medical rigor, we engineer a knowledge-aware multi-granular reward function incorporating an Entity Reward mechanism strictly aligned with knowledge graphs. This effectively guides the model to optimize for logical consistency rather than mere outcome matching, thereby enhancing robustness. Extensive experiments demonstrate that PathReasoner-R1 achieves state-of-the-art performance on both PathReasoner and public benchmarks across various image scales, equipping pathology models with transparent, clinically grounded reasoning capabilities. Dataset and code are available at https://github.com/cyclexfy/PathReasoner-R1.

Method: Quantifies distances between different model representations at various processing stages, tracks evolution of distances throughout processing, identifies most divergent processing steps, and compares CNN vs transformer behaviors and classifier-specific patterns.

Result: Found that while layers at similar positions have most similar representations, strong differences remain; classifiers discard low-level image statistics in final layers; transformers apply smoother representation changes between layers than CNNs.

Conclusion: The study clarifies the level and nature of convergence between model representations, enabling more qualitative understanding of underlying processes in image models beyond just final representation similarity.

Abstract: Recent literature suggests that the bigger the model, the more likely it is to converge to similar, ``universal’’ representations, despite different training objectives, datasets, or modalities. While this literature shows that there is an area where model representations are similar, we study here how vision models might get to those representations – in particular, do they also converge to the same intermediate steps and operations? We therefore study the processes that lead to convergent representations in different models. First, we quantify distance between different model representations at different stages. We follow the evolution of distances between models throughout processing, identifying the processing steps which are most different between models. We find that while layers at similar positions in different models have the most similar representations, strong differences remain. Classifier models, unlike the others, will discard information about low-level image statistics in their final layers. CNN- and transformer-based models also behave differently, with transformer models applying smoother changes to representations from one layer to the next. These distinctions clarify the level and nature of convergence between model representations, and enables a more qualitative account of the underlying processes in image models.

Method: The authors theoretically analyze why gFID-dominant preference appears unproblematic for ImageNet generation but becomes risky for controllable diffusion, then empirically validate using multi-dimensional condition-drift evaluation protocol and ControlNet experiments.

Result: gFID is only weakly predictive of condition preservation, while reconstruction-oriented metrics are substantially more aligned with controllability. ControlNet experiments confirm controllability tracks condition preservation rather than gFID.

Conclusion: There’s a gap between ImageNet-centric AE evaluation and requirements of scalable controllable diffusion, offering practical guidance for more reliable benchmarking and model selection in multimodal generation systems.

Abstract: In latent diffusion models, the autoencoder (AE) is typically expected to balance two capabilities: faithful reconstruction and a generation-friendly latent space (e.g., low gFID). In recent ImageNet-scale AE studies, we observe a systematic bias toward generative metrics in handling this trade-off: reconstruction metrics are increasingly under-reported, and ablation-based AE selection often favors the best-gFID configuration even when reconstruction fidelity degrades. We theoretically analyze why this gFID-dominant preference can appear unproblematic for ImageNet generation, yet becomes risky when scaling to controllable diffusion: AEs can induce condition drift, which limits achievable condition alignment. Meanwhile, we find that reconstruction fidelity, especially instance-level measures, better indicates controllability. We empirically validate the impact of tilted autoencoder evaluation on controllability by studying several recent ImageNet AEs. Using a multi-dimensional condition-drift evaluation protocol reflecting controllable generation tasks, we find that gFID is only weakly predictive of condition preservation, whereas reconstruction-oriented metrics are substantially more aligned. ControlNet experiments further confirm that controllability tracks condition preservation rather than gFID. Overall, our results expose a gap between ImageNet-centric AE evaluation and the requirements of scalable controllable diffusion, offering practical guidance for more reliable benchmarking and model selection.

Method: Proposes RSGround-R1 with three key components: 1) Chain-of-Thought Supervised Fine-Tuning using synthetic RSVG reasoning data for explicit position awareness, 2) Reinforcement Fine-Tuning with novel positional reward for continuous distance-aware guidance, and 3) Spatial consistency guided optimization to ensure stable convergence by adjusting policy updates based on spatial coherence.

Result: Extensive experiments on RSVG benchmarks demonstrate superior performance and generalization of the proposed model compared to existing approaches.

Conclusion: The RSGround-R1 framework effectively enhances spatial understanding in MLLMs for remote sensing applications through reasoning-guided, position-aware post-training, addressing the unique challenges of RSVG tasks.

Abstract: Remote Sensing Visual Grounding (RSVG) aims to localize target objects in large-scale aerial imagery based on natural language descriptions. Owing to the vast spatial scale and high semantic ambiguity of remote sensing scenes, these descriptions often rely heavily on positional cues, posing unique challenges for Multimodal Large Language Models (MLLMs) in spatial reasoning. To leverage this unique feature, we propose a reasoning-guided, position-aware post-training framework, dubbed \textbf{RSGround-R1}, to progressively enhance spatial understanding. Specifically, we first introduce Chain-of-Thought Supervised Fine-Tuning (CoT-SFT) using synthetically generated RSVG reasoning data to establish explicit position awareness. Reinforcement Fine-Tuning (RFT) is then applied, augmented by our newly designed positional reward that provides continuous and distance-aware guidance toward accurate localization. Moreover, to mitigate incoherent localization behaviors across rollouts, we introduce a spatial consistency guided optimization scheme that dynamically adjusts policy updates based on their spatial coherence, ensuring stable and robust convergence. Extensive experiments on RSVG benchmarks demonstrate superior performance and generalization of our model.

Method: OCRVerse uses comprehensive data engineering covering both text-centric documents and vision-centric rendered composites. It employs a two-stage training approach: 1) Supervised Fine-Tuning (SFT) with mixed cross-domain data to establish initial domain knowledge, and 2) Reinforcement Learning (RL) with personalized reward strategies customized for each domain’s specific output formats and requirements.

Result: OCRVerse achieves competitive results across both text-centric and vision-centric data types, performing comparably to large-scale open-source and closed-source models.

Conclusion: OCRVerse represents the first holistic OCR method that unifies text-centric and vision-centric OCR in an end-to-end manner, addressing the gap in existing OCR technology for visually information-dense images.

Abstract: The development of large vision language models drives the demand for managing, and applying massive amounts of multimodal data, making OCR technology, which extracts information from visual images, increasingly popular. However, existing OCR methods primarily focus on recognizing text elements from images or scanned documents (\textbf{Text-centric OCR}), neglecting the identification of visual elements from visually information-dense image sources (\textbf{Vision-centric OCR}), such as charts, web pages and science plots. In reality, these visually information-dense images are widespread on the internet and have significant real-world application value, such as data visualization and web page analysis. In this technical report, we propose \textbf{OCRVerse}, the first holistic OCR method in end-to-end manner that enables unified text-centric OCR and vision-centric OCR. To this end, we constructe comprehensive data engineering to cover a wide range of text-centric documents, such as newspapers, magazines and books, as well as vision-centric rendered composites, including charts, web pages and scientific plots. Moreover, we propose a two-stage SFT-RL multi-domain training method for OCRVerse. SFT directly mixes cross-domain data to train and establish initial domain knowledge, while RL focuses on designing personalized reward strategies for the characteristics of each domain. Specifically, since different domains require various output formats and expected outputs, we provide sufficient flexibility in the RL stage to customize flexible reward signals for each domain, thereby improving cross-domain fusion and avoiding data conflicts. Experimental results demonstrate the effectiveness of OCRVerse, achieving competitive results across text-centric and vision-centric data types, even comparable to large-scale open-source and closed-source models.

Method: Proposes CAF-Mamba, a novel framework using Mamba architecture with cross-modal adaptive attention fusion that captures both explicit and implicit cross-modal interactions and dynamically adjusts modality contributions through modality-wise attention mechanisms.

Result: Experiments on two in-the-wild benchmark datasets (LMVD and D-Vlog) show CAF-Mamba consistently outperforms existing methods and achieves state-of-the-art performance for depression detection.

Conclusion: CAF-Mamba effectively addresses limitations of existing multimodal fusion approaches for depression detection by enabling more sophisticated cross-modal interaction modeling and dynamic modality weighting.

Abstract: Depression is a prevalent mental health disorder that severely impairs daily functioning and quality of life. While recent deep learning approaches for depression detection have shown promise, most rely on limited feature types, overlook explicit cross-modal interactions, and employ simple concatenation or static weighting for fusion. To overcome these limitations, we propose CAF-Mamba, a novel Mamba-based cross-modal adaptive attention fusion framework. CAF-Mamba not only captures cross-modal interactions explicitly and implicitly, but also dynamically adjusts modality contributions through a modality-wise attention mechanism, enabling more effective multimodal fusion. Experiments on two in-the-wild benchmark datasets, LMVD and D-Vlog, demonstrate that CAF-Mamba consistently outperforms existing methods and achieves state-of-the-art performance.

Method: Few-shot domain adaptation strategy incorporating spatial static prior knowledge and including summer reference images in input time series, without any architectural modifications to the original model.

Result: Delineation error reduced dramatically from 1131.6 meters to 68.7 meters when applying the domain adaptation approach to novel study sites.

Conclusion: The methodological advancements establish a framework for applying deep learning-based calving front segmentation to novel study sites, enabling global-scale calving front monitoring.

Abstract: During benchmarking, the state-of-the-art model for glacier calving front delineation achieves near-human performance. However, when applied in a real-world setting at a novel study site, its delineation accuracy is insufficient for calving front products intended for further scientific analyses. This site represents an out-of-distribution domain for a model trained solely on the benchmark dataset. By employing a few-shot domain adaptation strategy, incorporating spatial static prior knowledge, and including summer reference images in the input time series, the delineation error is reduced from 1131.6 m to 68.7 m without any architectural modifications. These methodological advancements establish a framework for applying deep learning-based calving front segmentation to novel study sites, enabling calving front monitoring on a global scale.

Method: Proposes \regName, a lightweight geometry-aware regularization framework with two complementary constraints: intra-modal dispersive regularization to promote representation diversity, and inter-modal anchoring regularization to bound sample-level cross-modal drift without rigid alignment.

Result: Extensive experiments across multiple multimodal benchmarks demonstrate consistent improvements in both multimodal and unimodal performance, showing effective mitigation of modality trade-offs.

Conclusion: Explicitly regulating representation geometry is an effective approach to address geometric pathologies in multimodal learning, and the proposed plug-and-play regularizer works with various training paradigms without architectural modifications.

Abstract: Multimodal learning aims to integrate complementary information from heterogeneous modalities, yet strong optimization alone does not guaranty well-structured representations. Even under carefully balanced training schemes, multimodal models often exhibit geometric pathologies, including intra-modal representation collapse and sample-level cross-modal inconsistency, which degrade both unimodal robustness and multimodal fusion. We identify representation geometry as a missing control axis in multimodal learning and propose \regName, a lightweight geometry-aware regularization framework. \regName enforces two complementary constraints on intermediate embeddings: an intra-modal dispersive regularization that promotes representation diversity, and an inter-modal anchoring regularization that bounds sample-level cross-modal drift without rigid alignment. The proposed regularizer is plug-and-play, requires no architectural modifications, and is compatible with various training paradigms. Extensive experiments across multiple multimodal benchmarks demonstrate consistent improvements in both multimodal and unimodal performance, showing that explicitly regulating representation geometry effectively mitigates modality trade-offs.

Method: Proposes Multimodal Visual Surrogate Compression (MVSC) with two components: 1) Volume Context Encoder captures global cross-slice context under textual guidance, 2) Adaptive Slice Fusion module aggregates slice-level information in a text-enhanced, patch-wise manner. Learns to compress 3D sMRI volumes into compact 2D features (visual surrogates) aligned with frozen 2D foundation models.

Result: Extensive experiments on three large-scale Alzheimer’s disease benchmarks show MVSC performs favorably on both binary and multi-class classification tasks compared to state-of-the-art methods.

Conclusion: MVSC effectively addresses limitations of existing sMRI representation learning methods by compressing 3D volumes into 2D features that leverage powerful 2D foundation models while preserving important 3D structural information through multimodal guidance.

Abstract: High-dimensional structural MRI (sMRI) images are widely used for Alzheimer’s Disease (AD) diagnosis. Most existing methods for sMRI representation learning rely on 3D architectures (e.g., 3D CNNs), slice-wise feature extraction with late aggregation, or apply training-free feature extractions using 2D foundation models (e.g., DINO). However, these three paradigms suffer from high computational cost, loss of cross-slice relations, and limited ability to extract discriminative features, respectively. To address these challenges, we propose Multimodal Visual Surrogate Compression (MVSC). It learns to compress and adapt large 3D sMRI volumes into compact 2D features, termed as visual surrogates, which are better aligned with frozen 2D foundation models to extract powerful representations for final AD classification. MVSC has two key components: a Volume Context Encoder that captures global cross-slice context under textual guidance, and an Adaptive Slice Fusion module that aggregates slice-level information in a text-enhanced, patch-wise manner. Extensive experiments on three large-scale Alzheimer’s disease benchmarks demonstrate our MVSC performs favourably on both binary and multi-class classification tasks compared against state-of-the-art methods.

Method: Introduces ChartE³ benchmark with over 1,200 high-quality samples constructed via a data pipeline with human curation. Each sample includes chart image, underlying code, and multimodal editing instruction. Focuses on two editing dimensions: local editing (fine-grained appearance changes) and global editing (holistic data-centric transformations).

Result: Extensive benchmarking reveals substantial performance gaps in state-of-the-art multimodal large language models, particularly on global editing tasks, highlighting critical limitations in current end-to-end chart editing capabilities.

Conclusion: ChartE³ provides a comprehensive benchmark for evaluating end-to-end chart editing capabilities, revealing significant challenges in current multimodal models, especially for complex global editing tasks that require structural understanding.

Abstract: Charts are a fundamental visualization format for structured data analysis. Enabling end-to-end chart editing according to user intent is of great practical value, yet remains challenging due to the need for both fine-grained control and global structural consistency. Most existing approaches adopt pipeline-based designs, where natural language or code serves as an intermediate representation, limiting their ability to faithfully execute complex edits. We introduce ChartE$^{3}$, an End-to-End Chart Editing benchmark that directly evaluates models without relying on intermediate natural language programs or code-level supervision. ChartE$^{3}$ focuses on two complementary editing dimensions: local editing, which involves fine-grained appearance changes such as font or color adjustments, and global editing, which requires holistic, data-centric transformations including data filtering and trend line addition. ChartE$^{3}$ contains over 1,200 high-quality samples constructed via a well-designed data pipeline with human curation. Each sample is provided as a triplet of a chart image, its underlying code, and a multimodal editing instruction, enabling evaluation from both objective and subjective perspectives. Extensive benchmarking of state-of-the-art multimodal large language models reveals substantial performance gaps, particularly on global editing tasks, highlighting critical limitations in current end-to-end chart editing capabilities.

Method: Two-stage approach: (1) Fuses reference appearance and motion cues into a unified latent space to jointly reason about spatial identity and temporal dynamics using foundational models’ generative priors. (2) Introduces self-bootstrapped data synthesis pipeline that creates pseudo cross-identity training pairs to transition from pose-dependent control to direct RGB-driven animation.

Result: DreamActor-M2 achieves state-of-the-art performance with superior visual fidelity and robust cross-domain generalization. The paper also introduces AW Bench, a comprehensive benchmark for evaluating character animation across diverse character types and motion scenarios.

Conclusion: DreamActor-M2 presents a universal animation framework that addresses fundamental limitations in existing methods by treating motion conditioning as in-context learning, enabling better identity preservation, motion consistency, and generalization across diverse characters without relying on explicit pose priors.

Abstract: Character image animation aims to synthesize high-fidelity videos by transferring motion from a driving sequence to a static reference image. Despite recent advancements, existing methods suffer from two fundamental challenges: (1) suboptimal motion injection strategies that lead to a trade-off between identity preservation and motion consistency, manifesting as a “see-saw”, and (2) an over-reliance on explicit pose priors (e.g., skeletons), which inadequately capture intricate dynamics and hinder generalization to arbitrary, non-humanoid characters. To address these challenges, we present DreamActor-M2, a universal animation framework that reimagines motion conditioning as an in-context learning problem. Our approach follows a two-stage paradigm. First, we bridge the input modality gap by fusing reference appearance and motion cues into a unified latent space, enabling the model to jointly reason about spatial identity and temporal dynamics by leveraging the generative prior of foundational models. Second, we introduce a self-bootstrapped data synthesis pipeline that curates pseudo cross-identity training pairs, facilitating a seamless transition from pose-dependent control to direct, end-to-end RGB-driven animation. This strategy significantly enhances generalization across diverse characters and motion scenarios. To facilitate comprehensive evaluation, we further introduce AW Bench, a versatile benchmark encompassing a wide spectrum of characters types and motion scenarios. Extensive experiments demonstrate that DreamActor-M2 achieves state-of-the-art performance, delivering superior visual fidelity and robust cross-domain generalization. Project Page: https://grisoon.github.io/DreamActor-M2/

Method: GMC includes: (1) Granularity Modulator using Gaussian-weighted correlations conditioned on MOS values and pairwise MOS differences, and (2) Distribution Regulator to mitigate biases from non-uniform quality distributions, producing a 3D correlation surface.

Result: Experiments on standard benchmarks show GMC reveals performance characteristics invisible to scalar metrics, offering more informative and reliable analysis of IQA models.

Conclusion: GMC provides a structured, fine-grained paradigm for evaluating IQA models that overcomes limitations of traditional global correlation metrics.

Abstract: Evaluation of Image Quality Assessment (IQA) models has long been dominated by global correlation metrics, such as Pearson Linear Correlation Coefficient (PLCC) and Spearman Rank-Order Correlation Coefficient (SRCC). While widely adopted, these metrics reduce performance to a single scalar, failing to capture how ranking consistency varies across the local quality spectrum. For example, two IQA models may achieve identical SRCC values, yet one ranks high-quality images (related to high Mean Opinion Score, MOS) more reliably, while the other better discriminates image pairs with small quality/MOS differences (related to $|Δ$MOS$|$). Such complementary behaviors are invisible under global metrics. Moreover, SRCC and PLCC are sensitive to test-sample quality distributions, yielding unstable comparisons across test sets. To address these limitations, we propose \textbf{Granularity-Modulated Correlation (GMC)}, which provides a structured, fine-grained analysis of IQA performance. GMC includes: (1) a \textbf{Granularity Modulator} that applies Gaussian-weighted correlations conditioned on absolute MOS values and pairwise MOS differences ($|Δ$MOS$|$) to examine local performance variations, and (2) a \textbf{Distribution Regulator} that regularizes correlations to mitigate biases from non-uniform quality distributions. The resulting \textbf{correlation surface} maps correlation values as a joint function of MOS and $|Δ$MOS$|$, providing a 3D representation of IQA performance. Experiments on standard benchmarks show that GMC reveals performance characteristics invisible to scalar metrics, offering a more informative and reliable paradigm for analyzing, comparing, and deploying IQA models. Codes are available at https://github.com/Dniaaa/GMC.

Method: DGNav introduces two core innovations: 1) Scene-Aware Adaptive Strategy that dynamically modulates graph construction thresholds based on predicted waypoint dispersion, enabling “densification on demand” in challenging environments; 2) Dynamic Graph Transformer that reconstructs graph connectivity by fusing visual, linguistic, and geometric cues into dynamic edge weights to filter topological noise and enhance instruction adherence.

Result: Extensive experiments on R2R-CE and RxR-CE benchmarks demonstrate superior navigation performance and strong generalization capabilities. Ablation studies confirm the framework achieves optimal trade-off between navigation efficiency and safe exploration.

Conclusion: DGNav effectively addresses the Granularity Rigidity problem in vision-language navigation by introducing dynamic topological planning that adapts to environmental complexity, improving both navigation precision and computational efficiency.

Abstract: Vision-Language Navigation in Continuous Environments (VLN-CE) presents a core challenge: grounding high-level linguistic instructions into precise, safe, and long-horizon spatial actions. Explicit topological maps have proven to be a vital solution for providing robust spatial memory in such tasks. However, existing topological planning methods suffer from a “Granularity Rigidity” problem. Specifically, these methods typically rely on fixed geometric thresholds to sample nodes, which fails to adapt to varying environmental complexities. This rigidity leads to a critical mismatch: the model tends to over-sample in simple areas, causing computational redundancy, while under-sampling in high-uncertainty regions, increasing collision risks and compromising precision. To address this, we propose DGNav, a framework for Dynamic Topological Navigation, introducing a context-aware mechanism to modulate map density and connectivity on-the-fly. Our approach comprises two core innovations: (1) A Scene-Aware Adaptive Strategy that dynamically modulates graph construction thresholds based on the dispersion of predicted waypoints, enabling “densification on demand” in challenging environments; (2) A Dynamic Graph Transformer that reconstructs graph connectivity by fusing visual, linguistic, and geometric cues into dynamic edge weights, enabling the agent to filter out topological noise and enhancing instruction adherence. Extensive experiments on the R2R-CE and RxR-CE benchmarks demonstrate DGNav exhibits superior navigation performance and strong generalization capabilities. Furthermore, ablation studies confirm that our framework achieves an optimal trade-off between navigation efficiency and safe exploration. The code is available at https://github.com/shannanshouyin/DGNav.

Method: Uses Splatter Image network (3D Gaussian representation) fine-tuned on synthetic ShapeNet vessels and custom ship dataset. Integrates YOLOv8 segmentation, preprocessing, postprocessing for real-world scaling/alignment, and georeferencing with AIS metadata.

Result: Strong reconstruction fidelity on synthetic validation data, successful transfer to real maritime images from ShipSG dataset, enabling interactive 3D inspection without real-world 3D annotations.

Conclusion: Provides efficient, scalable solution for maritime monitoring with potential for real-time 3D ship visualization in practical applications.

Abstract: Three-dimensional (3D) reconstruction of ships is an important part of maritime monitoring, allowing improved visualization, inspection, and decision-making in real-world monitoring environments. However, most state-ofthe-art 3D reconstruction methods require multi-view supervision, annotated 3D ground truth, or are computationally intensive, making them impractical for real-time maritime deployment. In this work, we present an efficient pipeline for single-view 3D reconstruction of real ships by training entirely on synthetic data and requiring only a single view at inference. Our approach uses the Splatter Image network, which represents objects as sparse sets of 3D Gaussians for rapid and accurate reconstruction from single images. The model is first fine-tuned on synthetic ShapeNet vessels and further refined with a diverse custom dataset of 3D ships, bridging the domain gap between synthetic and real-world imagery. We integrate a state-of-the-art segmentation module based on YOLOv8 and custom preprocessing to ensure compatibility with the reconstruction network. Postprocessing steps include real-world scaling, centering, and orientation alignment, followed by georeferenced placement on an interactive web map using AIS metadata and homography-based mapping. Quantitative evaluation on synthetic validation data demonstrates strong reconstruction fidelity, while qualitative results on real maritime images from the ShipSG dataset confirm the potential for transfer to operational maritime settings. The final system provides interactive 3D inspection of real ships without requiring real-world 3D annotations. This pipeline provides an efficient, scalable solution for maritime monitoring and highlights a path toward real-time 3D ship visualization in practical applications. Interactive demo: https://dlr-mi.github.io/ship3d-demo/.

Method: CG-MLLM uses a Mixture-of-Transformer architecture with two components: Token-level Autoregressive (TokenAR) Transformer for token-level content and Block-level Autoregressive (BlockAR) Transformer for block-level content. It integrates a pre-trained vision-language backbone with a specialized 3D VAE latent space to enable long-context interactions between standard tokens and spatial blocks.

Result: CG-MLLM significantly outperforms existing MLLMs in generating high-fidelity 3D objects, effectively bringing high-resolution 3D content creation into the mainstream LLM paradigm.

Conclusion: The proposed CG-MLLM framework successfully addresses the limitations of existing methods by enabling both 3D captioning and high-resolution 3D generation within a single multimodal LLM architecture.

Abstract: Large Language Models(LLMs) have revolutionized text generation and multimodal perception, but their capabilities in 3D content generation remain underexplored. Existing methods compromise by producing either low-resolution meshes or coarse structural proxies, failing to capture fine-grained geometry natively. In this paper, we propose CG-MLLM, a novel Multi-modal Large Language Model (MLLM) capable of 3D captioning and high-resolution 3D generation in a single framework. Leveraging the Mixture-of-Transformer architecture, CG-MLLM decouples disparate modeling needs, where the Token-level Autoregressive (TokenAR) Transformer handles token-level content, and the Block-level Autoregressive (BlockAR) Transformer handles block-level content. By integrating a pre-trained vision-language backbone with a specialized 3D VAE latent space, CG-MLLM facilitates long-context interactions between standard tokens and spatial blocks within a single integrated architecture. Experimental results show that CG-MLLM significantly outperforms existing MLLMs in generating high-fidelity 3D objects, effectively bringing high-resolution 3D content creation into the mainstream LLM paradigm.

Method: Three-stage pipeline: (1) large-scale data collection and standardization, (2) CoT rationale generation using Qwen3-VL-235B-A22B-Thinking, (3) comprehensive selection based on reasoning quality and difficulty awareness. Fine-tuned Qwen3-VL-Instruct on MMFineReason to create MMFineReason-2B/4B/8B models.

Result: MMFineReason models establish new SOTA for their size class. MMFineReason-4B surpasses Qwen3-VL-8B-Thinking, and MMFineReason-8B outperforms Qwen3-VL-30B-A3B-Thinking while approaching Qwen3-VL-32B-Thinking. Found “less is more” phenomenon: 7% subset (123K samples) achieves comparable performance to full dataset.

Conclusion: MMFineReason dataset effectively bridges the reasoning data gap for VLMs, enabling parameter-efficient models that outperform larger counterparts. The difficulty-aware filtering strategy reveals data efficiency insights, and reasoning-oriented data composition boosts general capabilities synergistically.

Abstract: Recent advances in Vision Language Models (VLMs) have driven significant progress in visual reasoning. However, open-source VLMs still lag behind proprietary systems, largely due to the lack of high-quality reasoning data. Existing datasets offer limited coverage of challenging domains such as STEM diagrams and visual puzzles, and lack consistent, long-form Chain-of-Thought (CoT) annotations essential for eliciting strong reasoning capabilities. To bridge this gap, we introduce MMFineReason, a large-scale multimodal reasoning dataset comprising 1.8M samples and 5.1B solution tokens, featuring high-quality reasoning annotations distilled from Qwen3-VL-235B-A22B-Thinking. The dataset is established via a systematic three-stage pipeline: (1) large-scale data collection and standardization, (2) CoT rationale generation, and (3) comprehensive selection based on reasoning quality and difficulty awareness. The resulting dataset spans STEM problems, visual puzzles, games, and complex diagrams, with each sample annotated with visually grounded reasoning traces. We fine-tune Qwen3-VL-Instruct on MMFineReason to develop MMFineReason-2B/4B/8B versions. Our models establish new state-of-the-art results for their size class. Notably, MMFineReason-4B succesfully surpasses Qwen3-VL-8B-Thinking, and MMFineReason-8B even outperforms Qwen3-VL-30B-A3B-Thinking while approaching Qwen3-VL-32B-Thinking, demonstrating remarkable parameter efficiency. Crucially, we uncover a “less is more” phenomenon via our difficulty-aware filtering strategy: a subset of just 7% (123K samples) achieves performance comparable to the full dataset. Notably, we reveal a synergistic effect where reasoning-oriented data composition simultaneously boosts general capabilities.

Method: Reinterprets diffusion as evolution of stochastic trajectories in structured latent space. Shapes initial noise and geometric alignment to avoid foreground regions naturally. Decouples style control from text conditioning using cached style directions as persistent vectors in latent space to constrain diffusion to shared stylistic subspace.

Result: Produces visually coherent, foreground-preserving results across complex documents without explicit constraints. Maintains stylistic consistency across pages and editing iterations without repeated prompt-based style specification.

Conclusion: By reframing diffusion as trajectory design in latent space, the method offers a principled, training-free approach to consistent and structured generative modeling for document backgrounds, compatible with existing diffusion backbones.

Abstract: We present a diffusion-based framework for document-centric background generation that achieves foreground preservation and multi-page stylistic consistency through latent-space design rather than explicit constraints. Instead of suppressing diffusion updates or applying masking heuristics, our approach reinterprets diffusion as the evolution of stochastic trajectories through a structured latent space. By shaping the initial noise and its geometric alignment, background generation naturally avoids designated foreground regions, allowing readable content to remain intact without auxiliary mechanisms. To address the long-standing issue of stylistic drift across pages, we decouple style control from text conditioning and introduce cached style directions as persistent vectors in latent space. Once selected, these directions constrain diffusion trajectories to a shared stylistic subspace, ensuring consistent appearance across pages and editing iterations. This formulation eliminates the need for repeated prompt-based style specification and provides a more stable foundation for multi-page generation. Our framework admits a geometric and physical interpretation, where diffusion paths evolve on a latent manifold shaped by preferred directions, and foreground regions are rarely traversed as a consequence of trajectory initialization rather than explicit exclusion. The proposed method is training-free, compatible with existing diffusion backbones, and produces visually coherent, foreground-preserving results across complex documents. By reframing diffusion as trajectory design in latent space, we offer a principled approach to consistent and structured generative modeling.

Method: 1) Interpret CFG through optimization lens, showing velocity field corresponds to gradient of smoothed distance functions; 2) Reformulate CFG sampling as homotopy optimization with manifold constraint; 3) Implement manifold projection via incremental gradient descent; 4) Enhance with Anderson Acceleration for efficiency without extra model evaluations.

Result: Proposed methods are training-free and consistently improve generation fidelity, prompt alignment, and robustness to guidance scale. Validated across diverse benchmarks including large-scale models like DiT-XL-2-256, Flux, and Stable Diffusion 3.5.

Conclusion: The optimization perspective provides theoretical foundation for CFG, and the proposed manifold-constrained homotopy optimization with Anderson Acceleration offers practical improvements in diffusion model sampling without requiring retraining.

Abstract: Classifier-free guidance (CFG) is a widely used technique for controllable generation in diffusion and flow-based models. Despite its empirical success, CFG relies on a heuristic linear extrapolation that is often sensitive to the guidance scale. In this work, we provide a principled interpretation of CFG through the lens of optimization. We demonstrate that the velocity field in flow matching corresponds to the gradient of a sequence of smoothed distance functions, which guides latent variables toward the scaled target image set. This perspective reveals that the standard CFG formulation is an approximation of this gradient, where the prediction gap, the discrepancy between conditional and unconditional outputs, governs guidance sensitivity. Leveraging this insight, we reformulate the CFG sampling as a homotopy optimization with a manifold constraint. This formulation necessitates a manifold projection step, which we implement via an incremental gradient descent scheme during sampling. To improve computational efficiency and stability, we further enhance this iterative process with Anderson Acceleration without requiring additional model evaluations. Our proposed methods are training-free and consistently refine generation fidelity, prompt alignment, and robustness to the guidance scale. We validate their effectiveness across diverse benchmarks, demonstrating significant improvements on large-scale models such as DiT-XL-2-256, Flux, and Stable Diffusion 3.5.

Method: Proposes Past- and Future-Informed KV Cache Policy (PaFu-KV) with a lightweight Salience Estimation Head distilled from a bidirectional teacher to estimate token salience scores, allowing selective retention of informative tokens while discarding less relevant ones.

Result: Extensive experiments show the method preserves high-fidelity video generation quality while enabling accelerated inference and reduced memory footprint, achieving better quality-efficiency trade-off for long-horizon video generation.

Conclusion: PaFu-KV addresses limitations of heuristic KV cache policies in autoregressive video generation by intelligently managing cache based on token importance, enabling more efficient long-horizon video synthesis without sacrificing quality.

Abstract: Video generation is pivotal to digital media creation, and recent advances in autoregressive video generation have markedly enhanced the efficiency of real-time video synthesis. However, existing approaches generally rely on heuristic KV Cache policies, which ignore differences in token importance in long-term video generation. This leads to the loss of critical spatiotemporal information and the accumulation of redundant, invalid cache, thereby degrading video generation quality and efficiency. To address this limitation, we first observe that token contributions to video generation are highly time-heterogeneous and accordingly propose a novel Past- and Future-Informed KV Cache Policy (PaFu-KV). Specifically, PaFu-KV introduces a lightweight Salience Estimation Head distilled from a bidirectional teacher to estimate salience scores, allowing the KV cache to retain informative tokens while discarding less relevant ones. This policy yields a better quality-efficiency trade-off by shrinking KV cache capacity and reducing memory footprint at inference time. Extensive experiments on benchmarks demonstrate that our method preserves high-fidelity video generation quality while enables accelerated inference, thereby enabling more efficient long-horizon video generation. Our code will be released upon paper acceptance.

Method: Proposes a Pyramidal Shapley-Taylor (PST) learning framework that decomposes human motion into temporal segments and spatial body joints, learning cross-modal correspondences through progressive joint-wise and segment-wise alignment in a pyramidal fashion inspired by human motion perception.

Result: Extensive experiments on multiple public benchmark datasets demonstrate significant outperformance over state-of-the-art methods, achieving precise alignment between motion segments/body joints and corresponding text tokens.

Conclusion: The framework effectively captures both local semantic details and hierarchical structural relationships in motion-language retrieval, bridging the semantic gap between natural language and human motion.

Abstract: As a foundational task in human-centric cross-modal intelligence, motion-language retrieval aims to bridge the semantic gap between natural language and human motion, enabling intuitive motion analysis, yet existing approaches predominantly focus on aligning entire motion sequences with global textual representations. This global-centric paradigm overlooks fine-grained interactions between local motion segments and individual body joints and text tokens, inevitably leading to suboptimal retrieval performance. To address this limitation, we draw inspiration from the pyramidal process of human motion perception (from joint dynamics to segment coherence, and finally to holistic comprehension) and propose a novel Pyramidal Shapley-Taylor (PST) learning framework for fine-grained motion-language retrieval. Specifically, the framework decomposes human motion into temporal segments and spatial body joints, and learns cross-modal correspondences through progressive joint-wise and segment-wise alignment in a pyramidal fashion, effectively capturing both local semantic details and hierarchical structural relationships. Extensive experiments on multiple public benchmark datasets demonstrate that our approach significantly outperforms state-of-the-art methods, achieving precise alignment between motion segments and body joints and their corresponding text tokens. The code of this work will be released upon acceptance.

Method: Created VideoAesBench with 1,804 videos from multiple sources (UGC, AIGC, compressed, RGC, game videos), multiple question formats (single-choice, multi-choice, True/False, open-ended), and holistic aesthetics dimensions covering visual form, style, and affectiveness.

Result: Benchmarked 23 open-source and commercial LMMs, finding they only possess basic video aesthetics perception ability with incomplete and imprecise performance.

Conclusion: Current LMMs have limited video aesthetic assessment capabilities. VideoAesBench serves as a strong testbed for explainable video aesthetics assessment and provides insights for future model development.

Abstract: Large multimodal models (LMMs) have demonstrated outstanding capabilities in various visual perception tasks, which has in turn made the evaluation of LMMs significant. However, the capability of video aesthetic quality assessment, which is a fundamental ability for human, remains underexplored for LMMs. To address this, we introduce VideoAesBench, a comprehensive benchmark for evaluating LMMs’ understanding of video aesthetic quality. VideoAesBench has several significant characteristics: (1) Diverse content including 1,804 videos from multiple video sources including user-generated (UGC), AI-generated (AIGC), compressed, robotic-generated (RGC), and game videos. (2) Multiple question formats containing traditional single-choice questions, multi-choice questions, True or False questions, and a novel open-ended questions for video aesthetics description. (3) Holistic video aesthetics dimensions including visual form related questions from 5 aspects, visual style related questions from 4 aspects, and visual affectiveness questions from 3 aspects. Based on VideoAesBench, we benchmark 23 open-source and commercial large multimodal models. Our findings show that current LMMs only contain basic video aesthetics perception ability, their performance remains incomplete and imprecise. We hope our VideoAesBench can be served as a strong testbed and offer insights for explainable video aesthetics assessment.

Method: Proposes homologous/heterogenous latents fusion with COT-based fusion ratio strategy, plus temporal-strengthening post-processing using image-to-video diffusion models to complement image methods.

Result: Experimental results demonstrate superiority in maintaining temporal consistency for zero-shot video restoration and enhancement tasks.

Conclusion: The framework successfully addresses temporal flickering in video applications while remaining training-free and compatible with any diffusion-based image restoration/enhancement methods.

Abstract: Although diffusion-based zero-shot image restoration and enhancement methods have achieved great success, applying them to video restoration or enhancement will lead to severe temporal flickering. In this paper, we propose the first framework that utilizes the rapidly-developed video diffusion model to assist the image-based method in maintaining more temporal consistency for zero-shot video restoration and enhancement. We propose homologous latents fusion, heterogenous latents fusion, and a COT-based fusion ratio strategy to utilize both homologous and heterogenous text-to-video diffusion models to complement the image method. Moreover, we propose temporal-strengthening post-processing to utilize the image-to-video diffusion model to further improve temporal consistency. Our method is training-free and can be applied to any diffusion-based image restoration and enhancement methods. Experimental results demonstrate the superiority of the proposed method.

Method: Proposes FeatJND, a task-aligned JND formulation that predicts maximum tolerable per-feature perturbation maps while preserving downstream task performance. Develops a FeatJND estimator at standardized split points and validates across image classification, detection, and instance segmentation tasks.

Result: Under matched distortion strength, FeatJND-based distortions consistently preserve higher task performance than unstructured Gaussian perturbations. Attribution visualizations suggest FeatJND can suppress non-critical feature regions. Applied to token-wise dynamic quantization, FeatJND-guided step-size allocation yields clear gains over random step-size permutation and global uniform step size under same noise budget.

Conclusion: FeatJND provides a practical framework for controlling deep visual feature quality while maintaining task performance, with applications in feature compression and efficient vision systems.

Abstract: Deep visual features are increasingly used as the interface in vision systems, motivating the need to describe feature characteristics and control feature quality for machine perception. Just noticeable difference (JND) characterizes the maximum imperceptible distortion for images under human or machine vision. Extending it to deep visual features naturally meets the above demand by providing a task-aligned tolerance boundary in feature space, offering a practical reference for controlling feature quality under constrained resources. We propose FeatJND, a task-aligned JND formulation that predicts the maximum tolerable per-feature perturbation map while preserving downstream task performance. We propose a FeatJND estimator at standardized split points and validate it across image classification, detection, and instance segmentation. Under matched distortion strength, FeatJND-based distortions consistently preserve higher task performance than unstructured Gaussian perturbations, and attribution visualizations suggest FeatJND can suppress non-critical feature regions. As an application, we further apply FeatJND to token-wise dynamic quantization and show that FeatJND-guided step-size allocation yields clear gains over random step-size permutation and global uniform step size under the same noise budget. Our code will be released after publication.

Method: BookNet adopts a dual-branch architecture with cross-page attention mechanisms to estimate warping flows for both individual pages and the complete book spread, explicitly modeling how left and right pages influence each other. The authors also created Book3D (synthetic dataset) and Book100 (real-world benchmark) to address data scarcity.

Result: Extensive experiments demonstrate that BookNet outperforms existing state-of-the-art methods on book image rectification. The framework effectively handles the asymmetric curvature patterns in book spreads.

Conclusion: BookNet represents a significant advancement in document image processing by specifically addressing the unique challenges of book image rectification through explicit modeling of cross-page geometric relationships, with publicly available code and datasets.

Abstract: Book image rectification presents unique challenges in document image processing due to complex geometric distortions from binding constraints, where left and right pages exhibit distinctly asymmetric curvature patterns. However, existing single-page document image rectification methods fail to capture the coupled geometric relationships between adjacent pages in books. In this work, we introduce BookNet, the first end-to-end deep learning framework specifically designed for dual-page book image rectification. BookNet adopts a dual-branch architecture with cross-page attention mechanisms, enabling it to estimate warping flows for both individual pages and the complete book spread, explicitly modeling how left and right pages influence each other. Moreover, to address the absence of specialized datasets, we present Book3D, a large-scale synthetic dataset for training, and Book100, a comprehensive real-world benchmark for evaluation. Extensive experiments demonstrate that BookNet outperforms existing state-of-the-art methods on book image rectification. Code and dataset will be made publicly available.

Method: Proposes Shallow Alignment, a contrastive learning strategy that aligns neural signals with intermediate representations of visual encoders rather than their final outputs, balancing texture details and semantic features.

Result: Significantly outperforms standard final-layer alignment by 22-58% across diverse vision backbones, enables decoding performance to scale predictably with vision backbone capacity, and unlocks scaling laws in neural visual decoding.

Conclusion: Shallow Alignment effectively addresses the granularity mismatch in neural visual decoding by leveraging intermediate vision model representations, leading to substantial performance improvements and predictable scaling behavior.

Abstract: Neural visual decoding is a central problem in brain computer interface research, aiming to reconstruct human visual perception and to elucidate the structure of neural representations. However, existing approaches overlook a fundamental granularity mismatch between human and machine vision, where deep vision models emphasize semantic invariance by suppressing local texture information, whereas neural signals preserve an intricate mixture of low-level visual attributes and high-level semantic content. To address this mismatch, we propose Shallow Alignment, a novel contrastive learning strategy that aligns neural signals with intermediate representations of visual encoders rather than their final outputs, thereby striking a better balance between low-level texture details and high-level semantic features. Extensive experiments across multiple benchmarks demonstrate that Shallow Alignment significantly outperforms standard final-layer alignment, with performance gains ranging from 22% to 58% across diverse vision backbones. Notably, our approach effectively unlocks the scaling law in neural visual decoding, enabling decoding performance to scale predictably with the capacity of pre-trained vision backbones. We further conduct systematic empirical analyses to shed light on the mechanisms underlying the observed performance gains.

Method: Upgraded PaddleOCR-VL model with enhanced architecture, introduced Real5-OmniDocBench benchmark for testing robustness against scanning, skew, warping, screen-photography, and illumination distortions, and incorporated seal recognition and text spotting capabilities.

Result: Achieved 94.5% SOTA accuracy on OmniDocBench v1.5 and demonstrated strong performance on the new Real5-OmniDocBench benchmark, while maintaining a compact 0.9B parameter size.

Conclusion: PaddleOCR-VL-1.5 represents a significant advancement in document understanding VLMs with improved robustness, expanded capabilities, and efficient architecture suitable for practical applications.

Abstract: We introduce PaddleOCR-VL-1.5, an upgraded model achieving a new state-of-the-art (SOTA) accuracy of 94.5% on OmniDocBench v1.5. To rigorously evaluate robustness against real-world physical distortions, including scanning, skew, warping, screen-photography, and illumination, we propose the Real5-OmniDocBench benchmark. Experimental results demonstrate that this enhanced model attains SOTA performance on the newly curated benchmark. Furthermore, we extend the model’s capabilities by incorporating seal recognition and text spotting tasks, while remaining a 0.9B ultra-compact VLM with high efficiency. Code: https://github.com/PaddlePaddle/PaddleOCR

Method: Autoregressive diffusion framework with three key designs: (1) shared latent space integrating vision and action tokens using Mixture-of-Transformers architecture, (2) closed-loop rollout mechanism for ongoing environmental feedback, (3) asynchronous inference pipeline parallelizing action prediction and motor execution.

Result: Model shows significant promise in long-horizon manipulation, data efficiency in post-training, and strong generalizability to novel configurations across both simulation benchmarks and real-world scenarios.

Conclusion: Video world modeling combined with vision-language pre-training establishes an independent foundation for robot learning, with LingBot-VA demonstrating practical effectiveness through its carefully designed architecture.

Abstract: This work highlights that video world modeling, alongside vision-language pre-training, establishes a fresh and independent foundation for robot learning. Intuitively, video world models provide the ability to imagine the near future by understanding the causality between actions and visual dynamics. Inspired by this, we introduce LingBot-VA, an autoregressive diffusion framework that learns frame prediction and policy execution simultaneously. Our model features three carefully crafted designs: (1) a shared latent space, integrating vision and action tokens, driven by a Mixture-of-Transformers (MoT) architecture, (2) a closed-loop rollout mechanism, allowing for ongoing acquisition of environmental feedback with ground-truth observations, (3) an asynchronous inference pipeline, parallelizing action prediction and motor execution to support efficient control. We evaluate our model on both simulation benchmarks and real-world scenarios, where it shows significant promise in long-horizon manipulation, data efficiency in post-training, and strong generalizability to novel configurations. The code and model are made publicly available to facilitate the community.

Method: 1) Adapt V-JEPA for end-to-end driving by pretraining ViT encoder on large-scale driving videos to produce predictive representations aligned with trajectory planning. 2) Introduce proposal-centric planner that distills diverse simulator-generated trajectories alongside human trajectories with momentum-aware selection for stable/safe behavior.

Result: V-JEPA representation with simple transformer decoder outperforms prior methods by 3 PDMS in perception-free setting. Complete Drive-JEPA achieves 93.3 PDMS on v1 and 87.8 EPDMS on v2, setting new state-of-the-art on NAVSIM.

Conclusion: Drive-JEPA effectively combines self-supervised video pretraining with multimodal trajectory distillation to address driving ambiguity and achieve superior planning performance.

Abstract: End-to-end autonomous driving increasingly leverages self-supervised video pretraining to learn transferable planning representations. However, pretraining video world models for scene understanding has so far brought only limited improvements. This limitation is compounded by the inherent ambiguity of driving: each scene typically provides only a single human trajectory, making it difficult to learn multimodal behaviors. In this work, we propose Drive-JEPA, a framework that integrates Video Joint-Embedding Predictive Architecture (V-JEPA) with multimodal trajectory distillation for end-to-end driving. First, we adapt V-JEPA for end-to-end driving, pretraining a ViT encoder on large-scale driving videos to produce predictive representations aligned with trajectory planning. Second, we introduce a proposal-centric planner that distills diverse simulator-generated trajectories alongside human trajectories, with a momentum-aware selection mechanism to promote stable and safe behavior. When evaluated on NAVSIM, the V-JEPA representation combined with a simple transformer-based decoder outperforms prior methods by 3 PDMS in the perception-free setting. The complete Drive-JEPA framework achieves 93.3 PDMS on v1 and 87.8 EPDMS on v2, setting a new state-of-the-art.

Method: Systematic investigation of single-frame action anticipation with multimodal fusion of RGB appearance, depth-based geometric cues, and semantic past-action representations, exploring different fusion strategies, keyframe selection policies, and past-action history sources.

Result: AAG+ consistently improves upon original AAG and achieves performance comparable to or exceeding state-of-the-art video-based methods on IKEA-ASM, Meccano and Assembly101 benchmarks.

Conclusion: Single-frame action anticipation can be highly effective when carefully designed with multimodal information fusion, clarifying when dense temporal modeling is necessary versus when a well-selected glimpse suffices.

Abstract: Human action anticipation is commonly treated as a video understanding problem, implicitly assuming that dense temporal information is required to reason about future actions. In this work, we challenge this assumption by investigating what can be achieved when action anticipation is constrained to a single visual observation. We ask a fundamental question: how much information about the future is already encoded in a single frame, and how can it be effectively exploited? Building on our prior work on Action Anticipation at a Glimpse (AAG), we conduct a systematic investigation of single-frame action anticipation enriched with complementary sources of information. We analyze the contribution of RGB appearance, depth-based geometric cues, and semantic representations of past actions, and investigate how different multimodal fusion strategies, keyframe selection policies and past-action history sources influence anticipation performance. Guided by these findings, we consolidate the most effective design choices into AAG+, a refined single-frame anticipation framework. Despite operating on a single frame, AAG+ consistently improves upon the original AAG and achieves performance comparable to, or exceeding, that of state-of-the-art video-based methods on challenging anticipation benchmarks including IKEA-ASM, Meccano and Assembly101. Our results offer new insights into the limits and potential of single-frame action anticipation, and clarify when dense temporal modeling is necessary and when a carefully selected glimpse is sufficient.

Method: Proposes the first urban NSR framework that fuses 3D synthetic aperture radar (SAR) point clouds with aerial imagery. Integrates radar-derived spatial constraints into an SDF-based NSR backbone, guiding structure-aware ray selection and adaptive sampling for stable optimization.

Result: Extensive experiments show that incorporating 3D SAR markedly enhances reconstruction accuracy, completeness, and robustness compared with single-modality baselines under highly sparse and oblique-view conditions.

Conclusion: The framework highlights a viable route toward scalable high-fidelity urban reconstruction with advanced airborne and spaceborne optical-SAR sensing, demonstrating the value of cross-modal fusion for 3D reconstruction.

Abstract: Neural surface reconstruction (NSR) has recently shown strong potential for urban 3D reconstruction from multi-view aerial imagery. However, existing NSR methods often suffer from geometric ambiguity and instability, particularly under sparse-view conditions. This issue is critical in large-scale urban remote sensing, where aerial image acquisition is limited by flight paths, terrain, and cost. To address this challenge, we present the first urban NSR framework that fuses 3D synthetic aperture radar (SAR) point clouds with aerial imagery for high-fidelity reconstruction under constrained, sparse-view settings. 3D SAR can efficiently capture large-scale geometry even from a single side-looking flight path, providing robust priors that complement photometric cues from images. Our framework integrates radar-derived spatial constraints into an SDF-based NSR backbone, guiding structure-aware ray selection and adaptive sampling for stable and efficient optimization. We also construct the first benchmark dataset with co-registered 3D SAR point clouds and aerial imagery, facilitating systematic evaluation of cross-modal 3D reconstruction. Extensive experiments show that incorporating 3D SAR markedly enhances reconstruction accuracy, completeness, and robustness compared with single-modality baselines under highly sparse and oblique-view conditions, highlighting a viable route toward scalable high-fidelity urban reconstruction with advanced airborne and spaceborne optical-SAR sensing.

Method: Uses hybrid representation loosely coupling explicit geometric primitives with neural Gaussians, enabling decoupled geometry and appearance modeling. Features online initialization and optimization strategy separating geometry and appearance updates to reduce structural redundancy.

Result: Improves dense mesh Chamfer-L2 by 18.52% over PGSR, surpasses ARTDECO by 1.31 dB PSNR, reconstructs ScanNetV2 scenes in under 100 seconds (5x faster than 2D Gaussian Splatting), while matching offline per-scene optimization quality.

Conclusion: PLANING provides efficient streaming reconstruction with both high-quality geometry and appearance, suitable for downstream applications like large-scale scene modeling and simulation-ready environments for embodied AI.

Abstract: Streaming reconstruction from monocular image sequences remains challenging, as existing methods typically favor either high-quality rendering or accurate geometry, but rarely both. We present PLANING, an efficient on-the-fly reconstruction framework built on a hybrid representation that loosely couples explicit geometric primitives with neural Gaussians, enabling geometry and appearance to be modeled in a decoupled manner. This decoupling supports an online initialization and optimization strategy that separates geometry and appearance updates, yielding stable streaming reconstruction with substantially reduced structural redundancy. PLANING improves dense mesh Chamfer-L2 by 18.52% over PGSR, surpasses ARTDECO by 1.31 dB PSNR, and reconstructs ScanNetV2 scenes in under 100 seconds, over 5x faster than 2D Gaussian Splatting, while matching the quality of offline per-scene optimization. Beyond reconstruction quality, the structural clarity and computational efficiency of \modelname~make it well suited for a broad range of downstream applications, such as enabling large-scale scene modeling and simulation-ready environments for embodied AI. Project page: https://city-super.github.io/PLANING/ .

Method: Introduces Metric Anything framework with Sparse Metric Prompts created by randomly masking depth maps, serving as a universal interface that decouples spatial reasoning from sensor/camera biases. Trained on ~20M image-depth pairs from reconstructed, captured, and rendered 3D data across 10,000 camera models.

Result: Demonstrates first clear scaling trend in metric depth track. Excels at prompt-driven tasks (depth completion, super-resolution, Radar-camera fusion). Distilled prompt-free student achieves SOTA on monocular depth estimation, camera intrinsics recovery, 3D reconstruction, and VLA planning. Pretrained ViT boosts MLLM spatial intelligence.

Conclusion: Metric depth estimation can benefit from scaling laws like other foundation models, establishing a path toward scalable real-world metric perception. The framework enables efficient metric understanding without manual engineering or task-specific architectures.

Abstract: Scaling has powered recent advances in vision foundation models, yet extending this paradigm to metric depth estimation remains challenging due to heterogeneous sensor noise, camera-dependent biases, and metric ambiguity in noisy cross-source 3D data. We introduce Metric Anything, a simple and scalable pretraining framework that learns metric depth from noisy, diverse 3D sources without manually engineered prompts, camera-specific modeling, or task-specific architectures. Central to our approach is the Sparse Metric Prompt, created by randomly masking depth maps, which serves as a universal interface that decouples spatial reasoning from sensor and camera biases. Using about 20M image-depth pairs spanning reconstructed, captured, and rendered 3D data across 10000 camera models, we demonstrate-for the first time-a clear scaling trend in the metric depth track. The pretrained model excels at prompt-driven tasks such as depth completion, super-resolution and Radar-camera fusion, while its distilled prompt-free student achieves state-of-the-art results on monocular depth estimation, camera intrinsics recovery, single/multi-view metric 3D reconstruction, and VLA planning. We also show that using pretrained ViT of Metric Anything as a visual encoder significantly boosts Multimodal Large Language Model capabilities in spatial intelligence. These results show that metric depth estimation can benefit from the same scaling laws that drive modern foundation models, establishing a new path toward scalable and efficient real-world metric perception. We open-source MetricAnything at http://metric-anything.github.io/metric-anything-io/ to support community research.

Method: Introduces adversarial training via a discriminator that distinguishes between single-source samples and those generated by recombining factors across sources. The generator is optimized to fool this discriminator, encouraging physical and semantic consistency in recombinations.

Result: Outperforms prior baselines on CelebA-HQ, Virtual KITTI, CLEVR, and Falcor3D with lower FID scores and better disentanglement (MIG and MCC). Demonstrates novel application to robotic video trajectories where recombining learned action components generates diverse sequences that increase state-space coverage for exploration on LIBERO benchmark.

Conclusion: The adversarial training approach effectively improves both latent factor discovery and compositional generation quality across multiple domains, with promising applications in robotic video generation for exploration.

Abstract: Decomposing complex data into factorized representations can reveal reusable components and enable synthesizing new samples via component recombination. We investigate this in the context of diffusion-based models that learn factorized latent spaces without factor-level supervision. In images, factors can capture background, illumination, and object attributes; in robotic videos, they can capture reusable motion components. To improve both latent factor discovery and quality of compositional generation, we introduce an adversarial training signal via a discriminator trained to distinguish between single-source samples and those generated by recombining factors across sources. By optimizing the generator to fool this discriminator, we encourage physical and semantic consistency in the resulting recombinations. Our method outperforms implementations of prior baselines on CelebA-HQ, Virtual KITTI, CLEVR, and Falcor3D, achieving lower FID scores and better disentanglement as measured by MIG and MCC. Furthermore, we demonstrate a novel application to robotic video trajectories: by recombining learned action components, we generate diverse sequences that significantly increase state-space coverage for exploration on the LIBERO benchmark.

Method: Proposes Vision-DeepResearch with multi-turn, multi-entity, multi-scale visual and textual search paradigm. Uses cold-start supervision and RL training to internalize deep-research capabilities into MLLMs, supporting dozens of reasoning steps and hundreds of engine interactions.

Result: Substantially outperforms existing multimodal deep-research MLLMs and workflows built on strong closed-source foundation models like GPT-5, Gemini-2.5-pro, and Claude-4-Sonnet.

Conclusion: Vision-DeepResearch provides a robust multimodal deep-research paradigm that effectively handles real-world noisy scenarios and complex questions requiring extensive evidence aggregation from diverse visual and textual sources.

Abstract: Multimodal large language models (MLLMs) have achieved remarkable success across a broad range of vision tasks. However, constrained by the capacity of their internal world knowledge, prior work has proposed augmenting MLLMs by ``reasoning-then-tool-call’’ for visual and textual search engines to obtain substantial gains on tasks requiring extensive factual information. However, these approaches typically define multimodal search in a naive setting, assuming that a single full-level or entity-level image query and few text query suffices to retrieve the key evidence needed to answer the question, which is unrealistic in real-world scenarios with substantial visual noise. Moreover, they are often limited in the reasoning depth and search breadth, making it difficult to solve complex questions that require aggregating evidence from diverse visual and textual sources. Building on this, we propose Vision-DeepResearch, which proposes one new multimodal deep-research paradigm, i.e., performs multi-turn, multi-entity and multi-scale visual and textual search to robustly hit real-world search engines under heavy noise. Our Vision-DeepResearch supports dozens of reasoning steps and hundreds of engine interactions, while internalizing deep-research capabilities into the MLLM via cold-start supervision and RL training, resulting in a strong end-to-end multimodal deep-research MLLM. It substantially outperforming existing multimodal deep-research MLLMs, and workflows built on strong closed-source foundation model such as GPT-5, Gemini-2.5-pro and Claude-4-Sonnet. The code will be released in https://github.com/Osilly/Vision-DeepResearch.

Method: Bi-level optimization with disjoint data splits: lower level fine-tunes SAM on detection proposals from subset D1; upper level updates detector to minimize validation loss of fine-tuned SAM on separate subset D2, making detector segmentation-aware.

Result: BLO-Inst outperforms standard baselines on general and biomedical domain tasks, achieving superior segmentation performance by optimizing detectors for downstream mask quality.

Conclusion: The framework successfully bridges detection and segmentation objectives, transforming detectors into segmentation-aware prompt generators for fully automated SAM deployment.

Abstract: The Segment Anything Model has revolutionized image segmentation with its zero-shot capabilities, yet its reliance on manual prompts hinders fully automated deployment. While integrating object detectors as prompt generators offers a pathway to automation, existing pipelines suffer from two fundamental limitations: objective mismatch, where detectors optimized for geometric localization do not correspond to the optimal prompting context required by SAM, and alignment overfitting in standard joint training, where the detector simply memorizes specific prompt adjustments for training samples rather than learning a generalizable policy. To bridge this gap, we introduce BLO-Inst, a unified framework that aligns detection and segmentation objectives by bi-level optimization. We formulate the alignment as a nested optimization problem over disjoint data splits. In the lower level, the SAM is fine-tuned to maximize segmentation fidelity given the current detection proposals on a subset ($D_1$). In the upper level, the detector is updated to generate bounding boxes that explicitly minimize the validation loss of the fine-tuned SAM on a separate subset ($D_2$). This effectively transforms the detector into a segmentation-aware prompt generator, optimizing the bounding boxes not just for localization accuracy, but for downstream mask quality. Extensive experiments demonstrate that BLO-Inst achieves superior performance, outperforming standard baselines on tasks in general and biomedical domains.

Method: Cross-domain diffusion model with dual-branch perception that uses multi-view RGB images and point maps of 3D assets to jointly model colors and canonical-space coordinates. Features spatially aligned dual-branch generation architecture and domain-decoupled generation mechanism.

Result: The approach effectively uses 3D assets as references to produce images consistent with the given assets, achieving precise consistency between generated images and 3D references.

Conclusion: The method opens new possibilities for combining diffusion models with 3D content creation by bridging 2D image generation with 3D asset references.

Abstract: In this paper, we propose a 3D asset-referenced diffusion model for image generation, exploring how to integrate 3D assets into image diffusion models. Existing reference-based image generation methods leverage large-scale pretrained diffusion models and demonstrate strong capability in generating diverse images conditioned on a single reference image. However, these methods are limited to single-image references and cannot leverage 3D assets, constraining their practical versatility. To address this gap, we present a cross-domain diffusion model with dual-branch perception that leverages multi-view RGB images and point maps of 3D assets to jointly model their colors and canonical-space coordinates, achieving precise consistency between generated images and the 3D references. Our spatially aligned dual-branch generation architecture and domain-decoupled generation mechanism ensure the simultaneous generation of two spatially aligned but content-disentangled outputs, RGB images and point maps, linking 2D image attributes with 3D asset attributes. Experiments show that our approach effectively uses 3D assets as references to produce images consistent with the given assets, opening new possibilities for combining diffusion models with 3D content creation.

Method: Integrates deep learning for component detection, Connected-Component Labeling for connectivity extraction, OCR for component reference designator retrieval, and Vision-Language Model for reference designator assignments.

Result: Achieves 96.47% overall netlist-generation accuracy, which is 2.72x higher than state-of-the-art approaches.

Conclusion: SINA provides an open-source, fully automated solution that significantly outperforms existing methods for circuit schematic image-to-netlist conversion.

Abstract: Current methods for converting circuit schematic images into machine-readable netlists struggle with component recognition and connectivity inference. In this paper, we present SINA, an open-source, fully automated circuit schematic image-to-netlist generator. SINA integrates deep learning for accurate component detection, Connected-Component Labeling (CCL) for precise connectivity extraction, and Optical Character Recognition (OCR) for component reference designator retrieval, while employing a Vision-Language Model (VLM) for reliable reference designator assignments. In our experiments, SINA achieves 96.47% overall netlist-generation accuracy, which is 2.72x higher than state-of-the-art approaches.

Method: Proposes a framework that calculates probability distribution of generated images in CLIP embedding space and drives generation toward low-probability regions. Introduces pullback mechanisms to maintain visual fidelity while achieving high creativity.

Result: Extensive experiments on text-to-image diffusion models demonstrate the framework’s effectiveness and efficiency in producing unique, novel, and thought-provoking images.

Conclusion: Provides a new perspective on creativity in generative models with a principled method to foster innovation in visual content synthesis.

Abstract: Creative image generation has emerged as a compelling area of research, driven by the need to produce novel and high-quality images that expand the boundaries of imagination. In this work, we propose a novel framework for creative generation using diffusion models, where creativity is associated with the inverse probability of an image’s existence in the CLIP embedding space. Unlike prior approaches that rely on a manual blending of concepts or exclusion of subcategories, our method calculates the probability distribution of generated images and drives it towards low-probability regions to produce rare, imaginative, and visually captivating outputs. We also introduce pullback mechanisms, achieving high creativity without sacrificing visual fidelity. Extensive experiments on text-to-image diffusion models demonstrate the effectiveness and efficiency of our creative generation framework, showcasing its ability to produce unique, novel, and thought-provoking images. This work provides a new perspective on creativity in generative models, offering a principled method to foster innovation in visual content synthesis.

Method: Developed ePAI (early Pancreatic cancer detection with Artificial Intelligence) trained on data from 1,598 patients from a single medical center. Tested internally on 1,009 patients and externally on 7,158 patients across 6 centers. Conducted multi-reader study comparing ePAI with 30 board-certified radiologists.

Result: Internal test: AUC 0.939-0.999, sensitivity 95.3%, specificity 98.7% for detecting PDAC <2cm, localizing lesions as small as 2mm. External test: AUC 0.918-0.945, sensitivity 91.5%, specificity 88.0%, localizing lesions as small as 5mm. Detected PDACs on prediagnostic CT scans 3-36 months before clinical diagnosis that were originally missed by radiologists. Outperformed 30 radiologists by 50.3% in sensitivity while maintaining comparable specificity.

Conclusion: ePAI shows strong potential as an assistive tool for early pancreatic cancer detection, significantly outperforming human radiologists in sensitivity and detecting cancers years before clinical diagnosis.

Abstract: Pancreatic ductal adenocarcinoma (PDAC), one of the deadliest solid malignancies, is often detected at a late and inoperable stage. Retrospective reviews of prediagnostic CT scans, when conducted by expert radiologists aware that the patient later developed PDAC, frequently reveal lesions that were previously overlooked. To help detecting these lesions earlier, we developed an automated system named ePAI (early Pancreatic cancer detection with Artificial Intelligence). It was trained on data from 1,598 patients from a single medical center. In the internal test involving 1,009 patients, ePAI achieved an area under the receiver operating characteristic curve (AUC) of 0.939-0.999, a sensitivity of 95.3%, and a specificity of 98.7% for detecting small PDAC less than 2 cm in diameter, precisely localizing PDAC as small as 2 mm. In an external test involving 7,158 patients across 6 centers, ePAI achieved an AUC of 0.918-0.945, a sensitivity of 91.5%, and a specificity of 88.0%, precisely localizing PDAC as small as 5 mm. Importantly, ePAI detected PDACs on prediagnostic CT scans obtained 3 to 36 months before clinical diagnosis that had originally been overlooked by radiologists. It successfully detected and localized PDACs in 75 of 159 patients, with a median lead time of 347 days before clinical diagnosis. Our multi-reader study showed that ePAI significantly outperformed 30 board-certified radiologists by 50.3% (P < 0.05) in sensitivity while maintaining a comparable specificity of 95.4% in detecting PDACs early and prediagnostic. These findings suggest its potential of ePAI as an assistive tool to improve early detection of pancreatic cancer.

Method: Two-stage framework with: (1) batch-aware attention for consistent intrinsic predictions across image collections, (2) physics-guided neural rendering enforcing physically plausible light transport, (3) physics-inspired losses regularizing training toward physically meaningful landscape, (4) curated dataset of diverse objects/scenes under controlled lighting.

Result: PI-Light synthesizes specular highlights and diffuse reflections across various materials, achieving superior generalization to real-world scenes compared to prior approaches.

Conclusion: The framework enables efficient finetuning of pretrained diffusion models while providing a solid benchmark for downstream evaluation, addressing key challenges in full-image relighting.

Abstract: Full-image relighting remains a challenging problem due to the difficulty of collecting large-scale structured paired data, the difficulty of maintaining physical plausibility, and the limited generalizability imposed by data-driven priors. Existing attempts to bridge the synthetic-to-real gap for full-scene relighting remain suboptimal. To tackle these challenges, we introduce Physics-Inspired diffusion for full-image reLight ($π$-Light, or PI-Light), a two-stage framework that leverages physics-inspired diffusion models. Our design incorporates (i) batch-aware attention, which improves the consistency of intrinsic predictions across a collection of images, (ii) a physics-guided neural rendering module that enforces physically plausible light transport, (iii) physics-inspired losses that regularize training dynamics toward a physically meaningful landscape, thereby enhancing generalizability to real-world image editing, and (iv) a carefully curated dataset of diverse objects and scenes captured under controlled lighting conditions. Together, these components enable efficient finetuning of pretrained diffusion models while also providing a solid benchmark for downstream evaluation. Experiments demonstrate that $π$-Light synthesizes specular highlights and diffuse reflections across a wide variety of materials, achieving superior generalization to real-world scenes compared with prior approaches.

Method: Introduces VI-Probe, a controllable visual-illusion framework with graded perturbations and matched visual controls (without illusion inducer) to disentangle visually grounded perception from language-driven recall. Uses metrics like Polarity-Flip Consistency, Template Fixation Index, and an illusion multiplier normalized against matched controls, rather than just averaged accuracy.

Result: Experiments reveal that response persistence arises from heterogeneous causes rather than a single mechanism: GPT-5 exhibits memory override, Claude-Opus-4.1 shows perception-memory competition, while Qwen variants suggest visual-processing limits.

Conclusion: The findings challenge single-cause views of VLM behavior and motivate probing-based evaluation that measures both knowledge and sensitivity to controlled visual change, providing a more nuanced understanding of how VLMs process visual information.

Abstract: Large Vision-Language Models (VLMs) often answer classic visual illusions “correctly” on original images, yet persist with the same responses when illusion factors are inverted, even though the visual change is obvious to humans. This raises a fundamental question: do VLMs perceive visual changes or merely recall memorized patterns? While several studies have noted this phenomenon, the underlying causes remain unclear. To move from observations to systematic understanding, this paper introduces VI-Probe, a controllable visual-illusion framework with graded perturbations and matched visual controls (without illusion inducer) that disentangles visually grounded perception from language-driven recall. Unlike prior work that focuses on averaged accuracy, we measure stability and sensitivity using Polarity-Flip Consistency, Template Fixation Index, and an illusion multiplier normalized against matched controls. Experiments across different families reveal that response persistence arises from heterogeneous causes rather than a single mechanism. For instance, GPT-5 exhibits memory override, Claude-Opus-4.1 shows perception-memory competition, while Qwen variants suggest visual-processing limits. Our findings challenge single-cause views and motivate probing-based evaluation that measures both knowledge and sensitivity to controlled visual change. Data and code are available at https://sites.google.com/view/vi-probe/.

Method: UEval comprises 1,000 expert-curated questions from 8 real-world tasks requiring both image and text outputs. The benchmark uses a rubric-based scoring system where MLLMs generate initial evaluation criteria, which human experts then refine and validate, resulting in 10,417 validated rubric criteria for fine-grained automatic scoring.

Result: UEval is challenging for current unified models: GPT-5-Thinking scores 66.4/100, while the best open-source model reaches only 49.1. Reasoning models outperform non-reasoning ones, and transferring reasoning traces from reasoning to non-reasoning models significantly narrows the performance gap.

Conclusion: Reasoning appears crucial for complex multimodal understanding and generation tasks. UEval provides a scalable, fine-grained evaluation framework for unified models, highlighting current limitations and the importance of reasoning capabilities in multimodal AI systems.

Abstract: We introduce UEval, a benchmark to evaluate unified models, i.e., models capable of generating both images and text. UEval comprises 1,000 expert-curated questions that require both images and text in the model output, sourced from 8 real-world tasks. Our curated questions cover a wide range of reasoning types, from step-by-step guides to textbook explanations. Evaluating open-ended multimodal generation is non-trivial, as simple LLM-as-a-judge methods can miss the subtleties. Different from previous works that rely on multimodal Large Language Models (MLLMs) to rate image quality or text accuracy, we design a rubric-based scoring system in UEval. For each question, reference images and text answers are provided to a MLLM to generate an initial rubric, consisting of multiple evaluation criteria, and human experts then refine and validate these rubrics. In total, UEval contains 10,417 validated rubric criteria, enabling scalable and fine-grained automatic scoring. UEval is challenging for current unified models: GPT-5-Thinking scores only 66.4 out of 100, while the best open-source model reaches merely 49.1. We observe that reasoning models often outperform non-reasoning ones, and transferring reasoning traces from a reasoning model to a non-reasoning model significantly narrows the gap. This suggests that reasoning may be important for tasks requiring complex multimodal understanding and generation.

Method: Proposes pixel MeanFlow (pMF) with separate network output space (image manifold, x-prediction) and loss space (velocity space via MeanFlow). Introduces transformation between image manifold and average velocity field.

Result: Achieves 2.22 FID on ImageNet 256x256 and 2.48 FID on 512x512 resolution for one-step latent-free generation, filling a key gap in this regime.

Conclusion: pMF advances boundaries of diffusion/flow-based generative models by enabling efficient one-step generation without latent spaces, with strong empirical performance.

Abstract: Modern diffusion/flow-based models for image generation typically exhibit two core characteristics: (i) using multi-step sampling, and (ii) operating in a latent space. Recent advances have made encouraging progress on each aspect individually, paving the way toward one-step diffusion/flow without latents. In this work, we take a further step towards this goal and propose “pixel MeanFlow” (pMF). Our core guideline is to formulate the network output space and the loss space separately. The network target is designed to be on a presumed low-dimensional image manifold (i.e., x-prediction), while the loss is defined via MeanFlow in the velocity space. We introduce a simple transformation between the image manifold and the average velocity field. In experiments, pMF achieves strong results for one-step latent-free generation on ImageNet at 256x256 resolution (2.22 FID) and 512x512 resolution (2.48 FID), filling a key missing piece in this regime. We hope that our study will further advance the boundaries of diffusion/flow-based generative models.

Method: Proposes Efficient4D framework that: 1) generates high-quality spacetime-consistent images under different camera views, 2) uses them as labeled data to reconstruct 4D content via 4D Gaussian splatting, 3) introduces inconsistency-aware confidence-weighted loss and lightly weighted score distillation loss for robust sparse-view reconstruction.

Result: Achieves 10x speed increase compared to prior art (10 minutes vs 120 minutes for Consistent4D) while preserving novel view synthesis quality. Enables real-time rendering under continuous camera trajectories.

Conclusion: Efficient4D provides an efficient solution for video-to-4D generation with significant speed improvements while maintaining quality, making dynamic 3D object generation more practical.

Abstract: Generating dynamic 3D object from a single-view video is challenging due to the lack of 4D labeled data. An intuitive approach is to extend previous image-to-3D pipelines by transferring off-the-shelf image generation models such as score distillation sampling.However, this approach would be slow and expensive to scale due to the need for back-propagating the information-limited supervision signals through a large pretrained model. To address this, we propose an efficient video-to-4D object generation framework called Efficient4D. It generates high-quality spacetime-consistent images under different camera views, and then uses them as labeled data to directly reconstruct the 4D content through a 4D Gaussian splatting model. Importantly, our method can achieve real-time rendering under continuous camera trajectories. To enable robust reconstruction under sparse views, we introduce inconsistency-aware confidence-weighted loss design, along with a lightly weighted score distillation loss. Extensive experiments on both synthetic and real videos show that Efficient4D offers a remarkable 10-fold increase in speed when compared to prior art alternatives while preserving the quality of novel view synthesis. For example, Efficient4D takes only 10 minutes to model a dynamic object, vs 120 minutes by the previous art model Consistent4D.

Method: Proposes SMTransformer which incorporates task-level information using soft masks generated from task-level queries and keys to learn attention weights. Introduces skip-attention-based up-sampling block for effective feature communication between encoding and decoding layers. Uses shared position encoding strategy to reduce parameters and training time without accuracy loss.

Result: Achieves state-of-the-art semantic segmentation results: 73.4% mIoU on S3DIS Area 5 and 62.4% mIoU on SWAN dataset. Experimental comparisons demonstrate efficacy for point cloud processing tasks including semantic segmentation and classification.

Conclusion: Integrating task-level information into point cloud encoding significantly improves performance. The proposed SMTransformer with soft masks, skip-attention up-sampling, and shared position encoding provides an effective framework for point cloud processing tasks while managing computational complexity.

Abstract: Point cloud processing methods leverage local and global point features %at the feature level to cater to downstream tasks, yet they often overlook the task-level context inherent in point clouds during the encoding stage. We argue that integrating task-level information into the encoding stage significantly enhances performance. To that end, we propose SMTransformer which incorporates task-level information into a vector-based transformer by utilizing a soft mask generated from task-level queries and keys to learn the attention weights. Additionally, to facilitate effective communication between features from the encoding and decoding layers in high-level tasks such as segmentation, we introduce a skip-attention-based up-sampling block. This block dynamically fuses features from various resolution points across the encoding and decoding layers. To mitigate the increase in network parameters and training time resulting from the complexity of the aforementioned blocks, we propose a novel shared position encoding strategy. This strategy allows various transformer blocks to share the same position information over the same resolution points, thereby reducing network parameters and training time without compromising accuracy.Experimental comparisons with existing methods on multiple datasets demonstrate the efficacy of SMTransformer and skip-attention-based up-sampling for point cloud processing tasks, including semantic segmentation and classification. In particular, we achieve state-of-the-art semantic segmentation results of 73.4% mIoU on S3DIS Area 5 and 62.4% mIoU on SWAN dataset

Method: Two approaches: 1) ViT + SVM pipeline where ViT extracts spatial features from aerial images and SVM classifies as stressed/healthy, and 2) end-to-end ViT with classification layer. Both methods use attention visualization to explain model decisions.

Result: High accuracy in drought stress identification with attention maps revealing specific spatial features that ViT focuses on as drought stress signatures, providing interpretable insights into subtle plant features associated with drought.

Conclusion: ViTs offer robust and interpretable solutions for drought stress monitoring, enabling farmers to make informed decisions for improved crop management through explainable AI that highlights relevant plant features.

Abstract: Early detection of drought stress is critical for taking timely measures for reducing crop loss before the drought impact becomes irreversible. The subtle phenotypical and physiological changes in response to drought stress are captured by non-invasive imaging techniques and these imaging data serve as valuable resource for machine learning methods to identify drought stress. While convolutional neural networks (CNNs) are in wide use, vision transformers (ViTs) present a promising alternative in capturing long-range dependencies and intricate spatial relationships, thereby enhancing the detection of subtle indicators of drought stress. We propose an explainable deep learning pipeline that leverages the power of ViTs for drought stress detection in potato crops using aerial imagery. We applied two distinct approaches: a synergistic combination of ViT and support vector machine (SVM), where ViT extracts intricate spatial features from aerial images, and SVM classifies the crops as stressed or healthy and an end-to-end approach using a dedicated classification layer within ViT to directly detect drought stress. Our key findings explain the ViT model’s decision-making process by visualizing attention maps. These maps highlight the specific spatial features within the aerial images that the ViT model focuses as the drought stress signature. Our findings demonstrate that the proposed methods not only achieve high accuracy in drought stress identification but also shedding light on the diverse subtle plant features associated with drought stress. This offers a robust and interpretable solution for drought stress monitoring for farmers to undertake informed decisions for improved crop management.

Method: TIPO uses a lightweight pre-trained model to sample refined prompts from a targeted sub-distribution within the broader semantic space. It starts from simple user prompts and expands them into richer, more detailed versions while preserving the original intent. The approach focuses on distribution-aligned prompt engineering rather than resource-intensive LLM or RL methods.

Result: Extensive experiments across multiple domains show TIPO achieves stronger text alignment, reduced visual artifacts, and consistently higher human preference rates. It maintains competitive aesthetic quality while offering significant computational efficiency and scalability advantages over existing methods.

Conclusion: TIPO demonstrates the effectiveness of distribution-aligned prompt engineering for text-to-image generation, offering a scalable and efficient alternative to resource-intensive methods. It opens new possibilities for automated prompt refinement and highlights broader opportunities for scalable optimization in T2I tasks.

Abstract: TIPO (Text-to-Image Prompt Optimization) introduces an efficient approach for automatic prompt refinement in text-to-image (T2I) generation. Starting from simple user prompts, TIPO leverages a lightweight pre-trained model to expand these prompts into richer and more detailed versions. Conceptually, TIPO samples refined prompts from a targeted sub-distribution within the broader semantic space, preserving the original intent while significantly improving visual quality, coherence, and detail. Unlike resource-intensive methods based on large language models (LLMs) or reinforcement learning (RL), TIPO offers strong computational efficiency and scalability, opening new possibilities for effective automated prompt engineering in T2I tasks. Extensive experiments across multiple domains demonstrate that TIPO achieves stronger text alignment, reduced visual artifacts, and consistently higher human preference rates, while maintaining competitive aesthetic quality. These results highlight the effectiveness of distribution-aligned prompt engineering and point toward broader opportunities for scalable, automated refinement in text-to-image generation.

Method: Projects events onto a unit sphere for spherical representation, introduces Event Spherical Iterative Closest Point (ES-ICP) geometric optimization framework, uses incremental k-d tree structures for map management with regional density control, and implements parallel point-to-line optimization.

Result: EROAM significantly outperforms state-of-the-art methods in accuracy, robustness, and computational efficiency on both synthetic and real-world datasets. Maintains consistent performance under high angular velocities and extended sequences, and produces high-quality panoramic reconstructions with fine structural details.

Conclusion: EROAM provides a novel, efficient, and accurate solution for event-based rotational odometry and mapping, demonstrating superior performance over existing methods and robustness in challenging conditions.

Abstract: This paper presents EROAM, a novel event-based rotational odometry and mapping system that achieves real-time, accurate camera rotation estimation. Unlike existing approaches that rely on event generation models or contrast maximization, EROAM employs a spherical event representation by projecting events onto a unit sphere and introduces Event Spherical Iterative Closest Point (ES-ICP), a novel geometric optimization framework designed specifically for event camera data. The spherical representation simplifies rotational motion formulation while operating in a continuous spherical domain, enabling enhanced spatial resolution. Our system features an efficient map management approach using incremental k-d tree structures and intelligent regional density control, ensuring optimal computational performance during long-term operation. Combined with parallel point-to-line optimization, EROAM achieves efficient computation without compromising accuracy. Extensive experiments on both synthetic and real-world datasets show that EROAM significantly outperforms state-of-the-art methods in terms of accuracy, robustness, and computational efficiency. Our method maintains consistent performance under challenging conditions, including high angular velocities and extended sequences, where other methods often fail or show significant drift. Additionally, EROAM produces high-quality panoramic reconstructions with preserved fine structural details.

Method: COOD leverages pre-trained vision-language models with a concept-based label expansion strategy that enriches semantic space with both positive and negative concepts for each label. It introduces a new scoring function that models complex label dependencies to precisely differentiate OOD samples without additional training.

Result: Achieves approximately 95% average AUROC on both VOC and COCO datasets, significantly outperforming existing approaches. Maintains robust performance across varying numbers of labels and different types of OOD samples.

Conclusion: COOD provides an effective zero-shot solution for multi-label OOD detection that captures complex label dependencies without requiring retraining, addressing a critical gap in real-world applications where samples have multiple interdependent labels.

Abstract: How can models effectively detect out-of-distribution (OOD) samples in complex, multi-label settings without extensive retraining? Existing OOD detection methods struggle to capture the intricate semantic relationships and label co-occurrences inherent in multi-label settings, often requiring large amounts of training data and failing to generalize to unseen label combinations. While large language models have revolutionized zero-shot OOD detection, they primarily focus on single-label scenarios, leaving a critical gap in handling real-world tasks where samples can be associated with multiple interdependent labels. To address these challenges, we introduce COOD, a novel zero-shot multi-label OOD detection framework. COOD leverages pre-trained vision-language models, enhancing them with a concept-based label expansion strategy and a new scoring function. By enriching the semantic space with both positive and negative concepts for each label, our approach models complex label dependencies, precisely differentiating OOD samples without the need for additional training. Extensive experiments demonstrate that our method significantly outperforms existing approaches, achieving approximately 95% average AUROC on both VOC and COCO datasets, while maintaining robust performance across varying numbers of labels and different types of OOD samples.

Method: BiPO integrates part-based generation with bidirectional autoregressive architecture, using Partial Occlusion technique to probabilistically occlude certain motion part information during training to relax interdependencies.

Result: BiPO achieves state-of-the-art performance on HumanML3D dataset, outperforming recent methods like ParCo, MoMask, and BAMM in FID scores and motion quality, and excels in motion editing tasks.

Conclusion: BiPO effectively advances text-to-motion synthesis with potential for practical applications, particularly in motion editing based on partial sequences and text.

Abstract: Generating natural and expressive human motions from textual descriptions is challenging due to the complexity of coordinating full-body dynamics and capturing nuanced motion patterns over extended sequences that accurately reflect the given text. To address this, we introduce BiPO, Bidirectional Partial Occlusion Network for Text-to-Motion Synthesis, a novel model that enhances text-to-motion synthesis by integrating part-based generation with a bidirectional autoregressive architecture. This integration allows BiPO to consider both past and future contexts during generation while enhancing detailed control over individual body parts without requiring ground-truth motion length. To relax the interdependency among body parts caused by the integration, we devise the Partial Occlusion technique, which probabilistically occludes the certain motion part information during training. In our comprehensive experiments, BiPO achieves state-of-the-art performance on the HumanML3D dataset, outperforming recent methods such as ParCo, MoMask, and BAMM in terms of FID scores and overall motion quality. Notably, BiPO excels not only in the text-to-motion generation task but also in motion editing tasks that synthesize motion based on partially generated motion sequences and textual descriptions. These results reveal the BiPO’s effectiveness in advancing text-to-motion synthesis and its potential for practical applications.

Method: ACDiT introduces a block-wise autoregressive unit where each block’s generation is formulated as a conditional diffusion process conditioned on prior blocks. It uses a specially designed Skip-Causal Attention Mask on standard diffusion transformers during training, and during inference iterates between diffusion denoising and autoregressive decoding with KV-Cache optimization.

Result: ACDiT achieves state-of-the-art performance among autoregressive baselines on visual generation tasks (image, video) under similar model scales, demonstrates transferability to visual understanding tasks despite generative training, and shows potential for long-horizon visual generation through analysis of autoregressive-diffusion trade-offs.

Conclusion: ACDiT offers a novel perspective on visual autoregressive generation by successfully combining autoregressive and diffusion paradigms, providing a flexible framework that bypasses discrete tokenization limitations and opens new avenues for unified multimodal models.

Abstract: Autoregressive and diffusion models have achieved remarkable progress in language models and visual generation, respectively. We present ACDiT, a novel Autoregressive blockwise Conditional Diffusion Transformer, that innovatively combines autoregressive and diffusion paradigms for continuous visual information. By introducing a block-wise autoregressive unit, ACDiT offers a flexible interpolation between token-wise autoregression and full-sequence diffusion, bypassing the limitations of discrete tokenization. The generation of each block is formulated as a conditional diffusion process, conditioned on prior blocks. ACDiT is easy to implement, as simple as applying a specially designed Skip-Causal Attention Mask on the standard diffusion transformer during training. During inference, the process iterates between diffusion denoising and autoregressive decoding that can make full use of KV-Cache. We validate the effectiveness of ACDiT on image, video, and text generation and show that ACDiT performs best among all autoregressive baselines under similar model scales on visual generation tasks. We also demonstrate that, benefiting from autoregressive modeling, pretrained ACDiT can be transferred in visual understanding tasks despite being trained with the generative objective. The analysis of the trade-off between autoregressive and diffusion demonstrates the potential of ACDiT to be used in long-horizon visual generation tasks. We hope that ACDiT offers a novel perspective on visual autoregressive generation and sheds light on new avenues for unified models.

Method: Developed a localization pipeline that detects PV modules, associates them with power plant models using visual anchor points and object tracking, and infers UAV position relative to the installation. Also presented three visual segmentation methods for PV modules.

Result: Verified and evaluated on custom aerial inspection datasets, demonstrating robustness and applicability for real-time navigation. Evaluated the influence of power plant model precision on localization accuracy.

Conclusion: The integrated detection-navigation pipeline enables precise UAV positioning for PV plant inspection, with segmentation methods supporting robust localization in real-time applications.

Abstract: Inspection systems utilizing unmanned aerial vehicles (UAVs) equipped with thermal cameras are increasingly popular for the maintenance of photovoltaic (PV) power plants. However, automation of the inspection task is a challenging problem as it requires precise navigation to capture images from optimal distances and viewing angles. This paper presents a novel localization pipeline that directly integrates PV module detection with UAV navigation, allowing precise positioning during inspection. The detections are used to identify the power plant structures in the image. These are associated with the power plant model and used to infer the UAV position relative to the inspected PV installation. We define visually recognizable anchor points for the initial association and use object tracking to discern global associations. Additionally, we present three different methods for visual segmentation of PV modules and evaluate their performance in relation to the proposed localization pipeline. The presented methods were verified and evaluated using custom aerial inspection data sets, demonstrating their robustness and applicability for real-time navigation. Additionally, we evaluate the influence of the power plant model precision on the localization methods.

Method: Comprehensive evaluation using 80 minutes of annotated videogame footage from 20 first-person shooter games (GameVibe corpus). Conducted over 4,800 experiments testing impact of LLM architecture, model size, input modality, prompting strategy, and ground truth processing on engagement prediction.

Result: LLMs generally fall behind continuous experience annotations provided by humans, despite claims of human-like performance in other domains. Performance fluctuates across games, with some cases exceeding expectations but overall underperforming compared to human annotations.

Conclusion: While LLMs can outperform traditional ML baselines, they struggle with continuous affect prediction from videos. The paper identifies underlying causes for performance fluctuations and provides a roadmap for further exploration of automated emotion labeling via LLMs.

Abstract: Can out-of-the-box pretrained Large Language Models (LLMs) detect human affect successfully when observing a video? To address this question, for the first time, we evaluate comprehensively the capacity of popular LLMs for successfully predicting continuous affect annotations of videos when prompted by a sequence of text and video frames in a multimodal fashion. In this paper, we test LLMs’ ability to correctly label changes of in-game engagement in 80 minutes of annotated videogame footage from 20 first-person shooter games of the GameVibe corpus. We run over 4,800 experiments to investigate the impact of LLM architecture, model size, input modality, prompting strategy, and ground truth processing method on engagement prediction. Our findings suggest that while LLMs rightfully claim human-like performance across multiple domains and able to outperform traditional machine learning baselines, they generally fall behind continuous experience annotations provided by humans. We examine some of the underlying causes for a fluctuating performance across games, highlight the cases where LLMs exceed expectations, and draw a roadmap for the further exploration of automated emotion labelling via LLMs.

Method: Proposes a sustained boosting algorithm that simultaneously optimizes classification and residual errors, plus an adaptive classifier assignment strategy to dynamically improve weak modality performance. Theoretically analyzes convergence of cross-modal gap function.

Result: Empirical experiments on widely used datasets show superiority over various state-of-the-art multimodal learning baselines.

Conclusion: The approach effectively balances classification ability between strong and weak modalities, mitigating the modality imbalance issue in multimodal learning.

Abstract: Multimodal learning (MML) is significantly constrained by modality imbalance, leading to suboptimal performance in practice. While existing approaches primarily focus on balancing the learning of different modalities to address this issue, they fundamentally overlook the inherent disproportion in model classification ability, which serves as the primary cause of this phenomenon. In this paper, we propose a novel multimodal learning approach to dynamically balance the classification ability of weak and strong modalities by incorporating the principle of boosting. Concretely, we first propose a sustained boosting algorithm in multimodal learning by simultaneously optimizing the classification and residual errors. Subsequently, we introduce an adaptive classifier assignment strategy to dynamically facilitate the classification performance of the weak modality. Furthermore, we theoretically analyze the convergence property of the cross-modal gap function, ensuring the effectiveness of the proposed boosting scheme. To this end, the classification ability of strong and weak modalities is expected to be balanced, thereby mitigating the imbalance issue. Empirical experiments on widely used datasets reveal the superiority of our method through comparison with various state-of-the-art (SOTA) multimodal learning baselines. The source code is available at https://github.com/njustkmg/NeurIPS25-AUG.

Method: Partition videos into fixed-length Groups of Pictures, represent each group with canonical 2D Gaussian primitives, model temporal evolution with neural ODEs, apply scale-aware grouping based on Nyquist sampling theorem to form nested hierarchy across resolutions, and enable progressive coding via D-optimal subset pruning.

Result: Achieves rendering at over 250 FPS while matching or surpassing recent INR baselines, enables multiscale continuous rate-quality adaptation with arbitrary-scale rendering and any-ratio progressive coding.

Conclusion: D2GV-AR provides an efficient deformable 2D Gaussian video representation that solves the scalability limitations of existing INR approaches, enabling practical video compression and processing with flexible rate-resolution adaptation.

Abstract: Implicit neural representations (INRs) enable fast video compression and effective video processing, but a single model rarely offers scalable decoding across rates and resolutions. In practice, multi-resolution typically relies on retraining or multi-branch designs, and structured pruning failed to provide a permutation-invariant progressive transmission order. Motivated by the explicit structure and efficiency of Gaussian splatting, we propose D2GV-AR, a deformable 2D Gaussian video representation that enables \emph{arbitrary-scale} rendering and \emph{any-ratio} progressive coding within a single model. We partition each video into fixed-length Groups of Pictures and represent each group with a canonical set of 2D Gaussian primitives, whose temporal evolution is modeled by a neural ordinary differential equation. During training and rendering, we apply scale-aware grouping according to Nyquist sampling theorem to form a nested hierarchy across resolutions. Once trained, primitives can be pruned via a D-optimal subset objective to enable any-ratio progressive coding. Extensive experiments show that D2GV-AR renders at over 250 FPS while matching or surpassing recent INR baselines, enabling multiscale continuous rate–quality adaptation.

Method: Proposes CDIKTNet with two sub-networks: 1) Cross-Domain Invariance Sub-network (CDIS) learns cross-view structural and spatial invariance from small paired data as prior knowledge, 2) Cross-Domain Transfer Sub-network (CDTS) uses dual-path contrastive learning to optimize subspaces while preserving shared feature space consistency. Forms closed-loop framework for invariance feature learning and knowledge transfer.

Result: Extensive experiments show CDIKTNet achieves state-of-the-art performance under full supervision compared to supervised methods, and surpasses existing unsupervised methods in both few-shot and cross-domain initialization scenarios.

Conclusion: CDIKTNet effectively addresses limitations of both supervised and unsupervised DVGL methods by leveraging limited supervision to learn cross-view invariance and enable knowledge transfer across domains, reducing computational overhead for domain adaptation.

Abstract: Traditional supervised drone-view geo-localization (DVGL) methods heavily depend on paired training data and encounter difficulties in learning cross-view correlations from unpaired data. Moreover, when deployed in a new domain, these methods require obtaining the new paired data and subsequent retraining for model adaptation, which significantly increases computational overhead. Existing unsupervised methods have enabled to generate pseudo-labels based on cross-view similarity to infer the pairing relationships. However, geographical similarity and spatial continuity often cause visually analogous features at different geographical locations. The feature confusion compromises the reliability of pseudo-label generation, where incorrect pseudo-labels drive negative optimization. Given these challenges inherent in both supervised and unsupervised DVGL methods, we propose a novel cross-domain invariant knowledge transfer network (CDIKTNet) with limited supervision, whose architecture consists of a cross-domain invariance sub-network (CDIS) and a cross-domain transfer sub-network (CDTS). This architecture facilitates a closed-loop framework for invariance feature learning and knowledge transfer. The CDIS is designed to learn cross-view structural and spatial invariance from a small amount of paired data that serves as prior knowledge. It endows the shared feature space of unpaired data with similar implicit cross-view correlations at initialization, which alleviates feature confusion. Based on this, the CDTS employs dual-path contrastive learning to further optimize each subspace while preserving consistency in a shared feature space. Extensive experiments demonstrate that CDIKTNet achieves state-of-the-art performance under full supervision compared with those supervised methods, and further surpasses existing unsupervised methods in both few-shot and cross-domain initialization.

Method: Two training-free modules: SUFF uses SVD to extract an “unknown subspace” and softly removes unknown components from features, while BGAT corrects score skewness and fits bimodal Gaussian mixture for adaptive threshold estimation.

Result: Experiments on 9 benchmarks with CLIP, SigLIP, and ALIGN backbones show the training-free pipeline matches or outperforms retraining-heavy SOTA methods in source-free OSDA settings.

Conclusion: Establishes a lightweight inference calibration paradigm for open-set VLM deployment that preserves VLM geometry while improving unknown class rejection.

Abstract: Vision-language models (VLMs) have gained widespread attention for their strong zero-shot capabilities across numerous downstream tasks. However, these models assume that each test image’s class label is drawn from a predefined label set and lack a reliable mechanism to reject samples from emerging unknown classes when only unlabeled data are available. To address this gap, open-set domain adaptation methods retrain models to push potential unknowns away from known clusters. Yet, some unknown samples remain stably anchored to specific known classes in the VLM feature space due to semantic relevance, which is termed as Semantic Affinity Anchoring (SAA). Forcibly repelling these samples unavoidably distorts the native geometry of VLMs and degrades performance. Meanwhile, existing score-based unknown detectors use simplistic thresholds and suffer from threshold sensitivity, resulting in sub-optimal performance. To address aforementioned issues, we propose VLM-OpenXpert, which comprises two training-free, plug-and-play inference modules. SUFF performs SVD on high-confidence unknowns to extract a low-rank “unknown subspace”. Each sample’s projection onto this subspace is weighted and softly removed from its feature, suppressing unknown components while preserving semantics. BGAT corrects score skewness via a Box-Cox transform, then fits a bimodal Gaussian mixture to adaptively estimate the optimal threshold balancing known-class recognition and unknown-class rejection. Experiments on 9 benchmarks and three backbones (CLIP, SigLIP, ALIGN) under source-free OSDA settings show that our training-free pipeline matches or outperforms retraining-heavy state-of-the-art methods, establishing a powerful lightweight inference calibration paradigm for open-set VLM deployment.

Method: Two-stage flow-based approach: 1) Precompute and cache results from an unconditional flow-based generative model that transforms Gaussian mixture to future motion density, 2) Use lightweight model to map historical trajectories to Gaussian mixture samples for conditional prediction.

Result: Achieves ~1ms inference time (4x faster than VAE methods, 30x faster than diffusion methods), maintains prediction accuracy comparable to SOTA, and shows improved density estimation accuracy on Human3.6M and AMASS datasets.

Conclusion: CacheFlow enables fast, accurate 3D human motion prediction through a novel caching mechanism with flow-based models, making real-time applications feasible without compromising performance.

Abstract: Many density estimation techniques for 3D human motion prediction require a significant amount of inference time, often exceeding the duration of the predicted time horizon. To address the need for faster density estimation for 3D human motion prediction, we introduce a novel flow-based method for human motion prediction called CacheFlow. Unlike previous conditional generative models that suffer from poor time efficiency, CacheFlow takes advantage of an unconditional flow-based generative model that transforms a Gaussian mixture into the density of future motions. The results of the computation of the flow-based generative model can be precomputed and cached. Then, for conditional prediction, we seek a mapping from historical trajectories to samples in the Gaussian mixture. This mapping can be done by a much more lightweight model, thus saving significant computation overhead compared to a typical conditional flow model. In such a two-stage fashion and by caching results from the slow flow model computation, we build our CacheFlow without loss of prediction accuracy and model expressiveness. This inference process is completed in approximately one millisecond, making it 4 times faster than previous VAE methods and 30 times faster than previous diffusion-based methods on standard benchmarks such as Human3.6M and AMASS datasets. Furthermore, our method demonstrates improved density estimation accuracy and comparable prediction accuracy to a SOTA method on Human3.6M. Our code and models are available at https://github.com/meaten/CacheFlow.

Method: The authors revisit weighted risk functions and show that minimizing calibration error is linked to selective classification. They propose a loss function with flexible confidence score functions (CSFs) and use a bin-based cumulative distribution function (CDF) approximation for efficient gradient-based optimization with O(nM) complexity.

Result: Empirical evaluations demonstrate competitive calibration performance across various datasets and model architectures.

Conclusion: The paper provides a principled connection between calibration error and selective classification, offering a flexible and efficient approach to model calibration through weighted risk functions with theoretical grounding.

Abstract: Several variants of reweighted risk functionals, such as focal loss, inverse focal loss, and the Area Under the Risk Coverage Curve (AURC), have been proposed for improving model calibration; yet their theoretical connections to calibration errors remain under-explored. In this paper, we revisit a broad class of weighted risk functions and find a principled connection between calibration error and selective classification. We show that minimizing calibration error is closely linked to the selective classification paradigm and demonstrate that optimizing selective risk in low confidence regions naturally improves calibration. Our proposed loss shares a similar reweighting strategy with dual focal loss but offers greater flexibility through the choice of confidence score functions (CSFs). Furthermore, our approach utilizes a bin-based cumulative distribution function (CDF) approximation, enabling efficient gradient-based optimization with O(nM) complexity for n samples and M bins. Empirical evaluations demonstrate that our method achieves competitive calibration performance across a range of datasets and model architectures.

Method: Jointly trains two neural networks with collaborative label purification via co-pseudo-labeling, enforces consistency regularization in both label and feature spaces, uses alternating optimization of contrastive representations and pseudo-labels, and maintains class prototypes in shared feature space.

Result: Extensive experiments show the method’s effectiveness and highlight the potential of integrating weakly supervised learning into knowledge distillation of pre-trained models.

Conclusion: The proposed Co-Reg framework successfully addresses the challenges of VLM-generated noisy partial labels and enables annotation-free training by leveraging pre-trained vision-language models.

Abstract: In the context of noisy partial label learning (NPLL), each training sample is associated with a set of candidate labels annotated by multiple noisy annotators. With the emergence of high-performance pre-trained vision-language models (VLMs) such as CLIP, LLaVA, and GPT-4V, leveraging these models to replace time-consuming manual annotation and enable annotation-free training has become a promising research direction. This paper studies learning from noisy partial labels generated by pre-trained VLMs and proposes a collaborative consistency regularization (Co-Reg) framework. Unlike symmetric noise commonly assumed in traditional noisy label learning, VLM-generated noise is instance-dependent and reflects the intrinsic biases of pre-trained models, posing greater challenges. To address this issue, we jointly train two neural networks to perform collaborative label purification via a co-pseudo-labeling mechanism, while enforcing consistency regularization in both label and feature representation spaces. In addition, multiple anti-overfitting strategies are introduced, including alternating optimization of contrastive representations and pseudo-labels, as well as maintaining class prototypes in a shared feature space. The proposed method can further incorporate few-shot manually annotated labels for performance enhancement. Extensive experiments under various settings demonstrate the effectiveness of our approach and highlight the potential of integrating weakly supervised learning into the knowledge distillation of pre-trained models.

Method: Proposes two key techniques: 1) Frequency filtering curriculum where models first see low-frequency components before gradually introducing higher frequencies, and 2) Gaussian noise patching augmentation to enhance robustness. Applied to ViT-B/16 backbone trained on ImageNet-1K.

Result: Achieves 1.6x reduction in pre-training time and 2.25x reduction in FLOPs while matching robustness on ImageNet-C corruption benchmarks and maintaining competitive linear probing performance compared to baseline DINOv2.

Conclusion: The method provides dual benefits of efficiency and robustness, making large-scale self-supervised foundation modeling more accessible and opening new research directions for data curriculum and augmentation strategies in self-supervised learning.

Abstract: Large-scale vision foundation models such as DINOv2 boast impressive performances by leveraging massive architectures and training datasets. But numerous scenarios require practitioners to reproduce those pre-training solutions, such as on private data, new modalities, or simply for scientific questioning–which is currently extremely demanding computation-wise. We thus propose a novel pre-training strategy for DINOv2 that simultaneously accelerates convergence–and strengthens robustness to common corruptions as a by-product. Our approach involves a frequency filtering curriculum–low-frequency being seen first–and the Gaussian noise patching augmentation. Applied to a ViT-B/16 backbone trained on ImageNet-1K, while pre-training time and FLOPs are reduced by 1.6x and 2.25x, our method still achieves matching robustness in corruption benchmarks (ImageNet-C) and maintains competitive linear probing performance compared with baseline. This dual benefit of efficiency and robustness makes large-scale self-supervised foundation modeling more attainable, while opening the door to novel exploration around data curriculum and augmentation as means to improve self-supervised learning models robustness. The code is available at https://github.com/KevinZ0217/fast_dinov2

Method: Introduces Online Navigation Refinement (ONR) mission, creates OMA dataset with lane-to-road correspondences, and proposes MAT transformer with path-aware attention for topology alignment and spatial attention for handling noisy OP features.

Result: MAT outperforms existing methods with 34 ms latency, enabling low-cost, up-to-date lane-level navigation. The OMA dataset contains 30K scenarios and 2.6M annotated lane vectors.

Conclusion: ONR enables practical lane-level navigation by associating SD maps with OP maps, overcoming challenges of many-to-one lane-to-road mappings and misalignment through the proposed dataset and transformer architecture.

Abstract: Lane-level navigation is critical for geographic information systems and navigation-based tasks, offering finer-grained guidance than road-level navigation by standard definition (SD) maps. However, it currently relies on expansive global HD maps that cannot adapt to dynamic road conditions. Recently, online perception (OP) maps have become research hotspots, providing real-time geometry as an alternative, but lack the global topology needed for navigation. To address these issues, Online Navigation Refinement (ONR), a new mission is introduced that refines SD-map-based road-level routes into accurate lane-level navigation by associating SD maps with OP maps. The map-to-map association to handle many-to-one lane-to-road mappings under two key challenges: (1) no public dataset provides lane-to-road correspondences; (2) severe misalignment from spatial fluctuations, semantic disparities, and OP map noise invalidates traditional map matching. For these challenges, We contribute: (1) Online map association dataset (OMA), the first ONR benchmark with 30K scenarios and 2.6M annotated lane vectors; (2) MAT, a transformer with path-aware attention to aligns topology despite spatial fluctuations and semantic disparities and spatial attention for integrates noisy OP features via global context; and (3) NR P-R, a metric evaluating geometric and semantic alignment. Experiments show that MAT outperforms existing methods at 34 ms latency, enabling low-cost and up-to-date lane-level navigation.

Method: Proposes a joint learning framework that extracts image components with diffusion models to represent clean signals, uses these components for translation, and employs a time-dependent translation network for complex mapping, enabling end-to-end joint optimization.

Result: Extensive experiments on RGB↔RGB and cross-modality tasks (RGB↔Edge, RGB↔Semantics, RGB↔Depth) show better generative performance than state-of-the-art methods.

Conclusion: The joint learning approach enables global optimization of both diffusion and translation processes, improving fidelity and structural consistency in cross-domain image translation without paired training data.

Abstract: We introduce a diffusion-based cross-domain image translator in the absence of paired training data. Unlike GAN-based methods, our approach integrates diffusion models to learn the image translation process, allowing for more coverable modeling of the data distribution and performance improvement of the cross-domain translation. However, incorporating the translation process within the diffusion process is still challenging since the two processes are not aligned exactly, i.e., the diffusion process is applied to the noisy signal while the translation process is conducted on the clean signal. As a result, recent diffusion-based studies employ separate training or shallow integration to learn the two processes, yet this may cause the local minimal of the translation optimization, constraining the effectiveness of diffusion models. To address the problem, we propose a novel joint learning framework that aligns the diffusion and the translation process, thereby improving the global optimality. Specifically, we propose to extract the image components with diffusion models to represent the clean signal and employ the translation process with the image components, enabling an end-to-end joint learning manner. On the other hand, we introduce a time-dependent translation network to learn the complex translation mapping, resulting in effective translation learning and significant performance improvement. Benefiting from the design of joint learning, our method enables global optimization of both processes, enhancing the optimality and achieving improved fidelity and structural consistency. We have conducted extensive experiments on RGB$\leftrightarrow$RGB and diverse cross-modality translation tasks including RGB$\leftrightarrow$Edge, RGB$\leftrightarrow$Semantics and RGB$\leftrightarrow$Depth, showcasing better generative performances than the state of the arts.

Method: PRISM uses motion-aware multi-view distillation to extract temporally synchronized imaging features from non-contrast cardiac cine MRI, then modulates these features using medically informed textual prompts. It integrates these with structured EHR data for survival analysis through a self-supervised framework.

Result: PRISM consistently outperforms classical survival prediction models and state-of-the-art deep learning baselines across four independent clinical cohorts. It uncovered three distinct imaging signatures associated with elevated MACE risk and identified hypertension, diabetes, and smoking as dominant contributors among clinical factors.

Conclusion: PRISM demonstrates that integrating visual representations from cardiac imaging with structured EHR data using prompt-guided modulation enables fine-grained risk prediction and provides valuable clinical insights into cardiac risk across diverse patient populations.

Abstract: Accurate prediction of major adverse cardiac events (MACE) remains a central challenge in cardiovascular prognosis. We present PRISM (Prompt-guided Representation Integration for Survival Modeling), a self-supervised framework that integrates visual representations from non-contrast cardiac cine magnetic resonance imaging with structured electronic health records (EHRs) for survival analysis. PRISM extracts temporally synchronized imaging features through motion-aware multi-view distillation and modulates them using medically informed textual prompts to enable fine-grained risk prediction. Across four independent clinical cohorts, PRISM consistently surpasses classical survival prediction models and state-of-the-art (SOTA) deep learning baselines under internal and external validation. Further clinical findings demonstrate that the combined imaging and EHR representations derived from PRISM provide valuable insights into cardiac risk across diverse cohorts. Three distinct imaging signatures associated with elevated MACE risk are uncovered, including lateral wall dyssynchrony, inferior wall hypersensitivity, and anterior elevated focus during diastole. Prompt-guided attribution further identifies hypertension, diabetes, and smoking as dominant contributors among clinical and physiological EHR factors.

Method: Two main modules: 1) Attribute disentanglement module using Brownian Bridge Diffusion Model to decompose visual features into non-individualized attributes (shared by some classes) and individualized attributes (specific to single classes); 2) Granule learning module to construct visual granules by integrating these attributes for recognition under two causal inference strategies.

Result: Extensive experiments on 15 datasets show CaPL significantly outperforms state-of-the-art prompt learning methods, especially on fine-grained datasets.

Conclusion: The proposed CaPL method effectively improves fine-grained recognition in CLIP through visual granulation and causal inference, demonstrating superior performance over existing prompt learning approaches.

Abstract: Prompt learning has recently attracted much attention for adapting pre-trained vision-language models (e.g., CLIP) to downstream recognition tasks. However, most of the existing CLIP-based prompt learning methods only show a limited ability for handling fine-grained datasets. To address this issue, we propose a causality-guided text prompt learning method via visual granulation for CLIP, called CaPL, where the explored visual granulation technique could construct sets of visual granules for the text prompt to capture subtle discrepancies among different fine-grained classes through casual inference. The CaPL method contains the following two modules: (1) An attribute disentanglement module is proposed to decompose visual features into non-individualized attributes (shared by some classes) and individualized attributes (specific to single classes) using a Brownian Bridge Diffusion Model; (2) A granule learning module is proposed to construct visual granules by integrating the aforementioned attributes for recognition under two causal inference strategies. Thanks to the learned visual granules, more discriminative text prompt is expected to be learned. Extensive experimental results on 15 datasets demonstrate that our CaPL method significantly outperforms the state-of-the-art prompt learning methods, especially on fine-grained datasets.

Method: Introduces a parameter-efficient, plug-and-play attention mechanism (BIR-Adapter) that reduces trained parameters. Also proposes a sampling guidance mechanism to mitigate hallucinations during restoration. The adapter design enables seamless integration into existing models.

Result: Achieves competitive and sometimes superior performance compared to state-of-the-art methods on synthetic and real-world degradations while requiring up to 36x fewer trained parameters. Successfully extends super-resolution-only diffusion models to handle additional unknown degradations.

Conclusion: BIR-Adapter provides an efficient, adaptable solution for blind image restoration that maintains high performance while significantly reducing parameter requirements, demonstrating the potential of adapter-based approaches for broader image restoration tasks.

Abstract: We introduce the BIR-Adapter, a parameter-efficient diffusion adapter for blind image restoration. Diffusion-based restoration methods have demonstrated promising performance in addressing this fundamental problem in computer vision, typically relying on auxiliary feature extractors or extensive fine-tuning of pre-trained models. Motivated by the observation that large-scale pretrained diffusion models can retain informative representations under common image degradations, BIR-Adapter introduces a parameter-efficient, plug-and-play attention mechanism that substantially reduces the number of trained parameters. To further improve reliability, we propose a sampling guidance mechanism that mitigates hallucinations during the restoration process. Experiments on synthetic and real-world degradations demonstrate that BIR-Adapter achieves competitive, and in several settings superior, performance compared to state-of-the-art methods while requiring up to 36x fewer trained parameters. Moreover, the adapter-based design enables seamless integration into existing models. We validate this generality by extending a super-resolution-only diffusion model to handle additional unknown degradations, highlighting the adaptability of our approach for broader image restoration tasks.

Method: Systematic evaluation across mental rotation tasks ranging from simple block structures (Shepard-Metzler type) to complex block figures, text, and photo-realistic objects. Layer-by-layer probing of model representations to examine where and how networks succeed.

Result: 1) Self-supervised ViTs capture geometric structure better than supervised ViTs; 2) Intermediate layers perform better than final layers; 3) Task difficulty increases with rotation complexity and occlusion, mirroring human reaction times and suggesting similar constraints in embedding space representations.

Conclusion: Vision transformers develop spatial reasoning abilities that show interesting parallels to human cognition, with self-supervised learning being particularly effective for capturing geometric structure, and the hierarchical nature of representations revealing insights about how these models process spatial information.

Abstract: Mental rotation is a key test of spatial reasoning in humans and has been central to understanding how perception supports cognition. Despite the success of modern vision transformers, it is still unclear how well these models develop similar abilities. In this work, we present a systematic evaluation of ViT, CLIP, DINOv2, and DINOv3 across a range of mental-rotation tasks, from simple block structures similar to those used by Shepard and Metzler to study human cognition, to more complex block figures, three types of text, and photo-realistic objects. By probing model representations layer by layer, we examine where and how these networks succeed. We find that i) self-supervised ViTs capture geometric structure better than supervised ViTs; ii) intermediate layers perform better than final layers; iii) task difficulty increases with rotation complexity and occlusion, mirroring human reaction times and suggesting similar constraints in embedding space representations.

Method: Proposes GLARE with patch-level augmentations for local consistency and regional consistency constraints leveraging spatial semantics. Uses Vision Transformers initialized from SSL models with only lightweight UniAdapter modules updated during continual pre-training.

Result: Experiments across multiple semantic segmentation benchmarks show GLARE consistently improves downstream performance with minimal computational and parameter overhead.

Conclusion: GLARE enables effective continual SSL pre-training for vision foundation models on low-data target domains, enhancing semantic segmentation performance efficiently.

Abstract: Self-supervised learning (SSL) has emerged as a central paradigm for training foundation models by leveraging large-scale unlabeled datasets, often producing representations with strong generalization capabilities. These models are typically pre-trained on general-purpose datasets such as ImageNet and subsequently adapted to various downstream tasks through finetuning. While prior work has investigated parameter-efficient adaptation methods like adapters, LoRA, and prompt tuning, primarily targeting downstream finetuning, extending the SSL pre-training itself in a continual manner to new domains under limited data remains largely underexplored, especially for downstream dense prediction tasks like semantic segmentation. In this work, we address the challenge of adapting vision foundation models to low-data target domains through continual self-supervised pre-training, specifically targeting downstream semantic segmentation. We propose GLARE (Global Local and Regional Enforcement), a novel continual self-supervised pre-training task designed to enhance downstream semantic segmentation performance. GLARE introduces patch-level augmentations to encourage local consistency and incorporates a regional consistency constraint that leverages spatial semantics in the data. For efficient continual pre-training, we initialize Vision Transformers (ViTs) with weights from existing SSL models and update only lightweight adapter modules specifically UniAdapter - while keeping the rest of the backbone frozen. Experiments across multiple semantic segmentation benchmarks on different domains demonstrate that GLARE consistently improves downstream performance with minimal computational and parameter overhead.

Method: Proposes SEGA (Signed Ensemble Gaussian black-box Attack) which approximates target model gradients by applying Gaussian smoothing to source models and ensembling their smoothed gradients, plus a perturbation filter mask to ensure imperceptibility.

Result: Experimental results on CLIVE dataset demonstrate superior transferability compared to existing methods, enabling successful transfer-based black-box attacks against NR-IQA models.

Conclusion: SEGA effectively addresses the low transferability challenge in attacking NR-IQA models, providing a practical black-box attack method for evaluating model robustness.

Abstract: No-Reference Image Quality Assessment (NR-IQA) models play an important role in various real-world applications. Recently, adversarial attacks against NR-IQA models have attracted increasing attention, as they provide valuable insights for revealing model vulnerabilities and guiding robust system design. Some effective attacks have been proposed against NR-IQA models in white-box settings, where the attacker has full access to the target model. However, these attacks often suffer from poor transferability to unknown target models in more realistic black-box scenarios, where the target model is inaccessible. This work makes the first attempt to address the challenge of low transferability in attacking NR-IQA models by proposing a transferable Signed Ensemble Gaussian black-box Attack (SEGA). The main idea is to approximate the gradient of the target model by applying Gaussian smoothing to source models and ensembling their smoothed gradients. To ensure the imperceptibility of adversarial perturbations, SEGA further removes inappropriate perturbations using a specially designed perturbation filter mask. Experimental results on the CLIVE dataset demonstrate the superior transferability of SEGA, validating its effectiveness in enabling successful transfer-based black-box attacks against NR-IQA models.

Method: Built on convolutional vision transformer backbone with three key components: (1) component-wise convolution for joint scalar/vector processing, (2) inter-field cross-attention for information propagation between physical fields, (3) axial attentions factorizing spatiotemporal attention along individual axes for computational efficiency.

Result: MORPH outperforms models trained from scratch and matches/surpasses strong baselines and SOTA models across diverse PDE prediction tasks, using both full fine-tuning and parameter-efficient low-rank adapters.

Conclusion: MORPH provides a flexible backbone for learning from heterogeneous scientific observations, advancing scalable and data-efficient scientific machine learning with publicly available code, datasets, and models.

Abstract: We introduce MORPH, a modality-agnostic, autoregressive foundation model for partial differential equations (PDEs). MORPH is built on a convolutional vision transformer backbone that seamlessly handles heterogeneous spatiotemporal datasets of varying data modality (1D–3D) at different resolutions, and multiple fields with mixed scalar and vector components. The architecture combines (i) component-wise convolution, which jointly processes scalar and vector channels to capture local interactions, (ii) inter-field cross-attention, which models and selectively propagates information between different physical fields, (iii) axial attentions, which factorize full spatiotemporal self-attention along individual spatial and temporal axes to reduce computational burden while retaining expressivity. We pretrain multiple model variants on a diverse collection of heterogeneous PDE datasets and evaluate transfer to a range of downstream prediction tasks. Using both full-model fine-tuning and parameter-efficient low-rank adapters, MORPH outperforms models trained from scratch. Across extensive evaluations, MORPH matches or surpasses strong baselines and recent state-of-the-art models. Collectively, these capabilities present a flexible and powerful backbone for learning from the heterogeneous and multimodal nature of scientific observations, charting a path toward scalable and data-efficient scientific machine learning. The source code, datasets, and models are publicly available at https://github.com/lanl/MORPH.

Method: Proposes Latent Rectified Flow Feature Distillation (RestoRect) that applies rectified flow to reformulate feature distillation as a generative process where students learn to synthesize teacher-quality features through learnable trajectories in latent space. Combines Retinex decomposition with learnable anisotropic diffusion constraints and trigonometric color space polarization. Introduces Feature Layer Extraction loss for robust knowledge transfer between different network architectures through cross-normalized transformer feature alignment with percentile-based outlier detection.

Result: Achieves better training stability, faster convergence and inference while preserving restoration quality. Demonstrates superior results across 15 image restoration datasets covering 4 tasks on 10 metrics against baselines.

Conclusion: RestoRect provides an effective solution to the performance-speed trade-off in image restoration through novel generative feature distillation that captures dynamic feature generation in transformer architectures.

Abstract: Current approaches for restoration of degraded images face a trade-off: high-performance models are slow for practical use, while fast models produce poor results. Knowledge distillation transfers teacher knowledge to students, but existing static feature matching methods cannot capture how modern transformer architectures dynamically generate features. We propose a novel Latent Rectified Flow Feature Distillation method for restoring degraded images called \textbf{‘RestoRect’}. We apply rectified flow to reformulate feature distillation as a generative process where students learn to synthesize teacher-quality features through learnable trajectories in latent space. Our framework combines Retinex decomposition with learnable anisotropic diffusion constraints, and trigonometric color space polarization. We introduce a Feature Layer Extraction loss for robust knowledge transfer between different network architectures through cross-normalized transformer feature alignment with percentile-based outlier detection. RestoRect achieves better training stability, and faster convergence and inference while preserving restoration quality, demonstrating superior results across 15 image restoration datasets, covering 4 tasks, on 10 metrics against baselines.

Method: Extended SMIRK dataset to create MoreSMIRK with structured multi-pedestrian crossing situations. Used space-filling curves to transform multi-dimensional scenario features into characteristic patterns, then matched these patterns with MoreSMIRK entries. Evaluated on PIE dataset with 150+ annotated pedestrian crossing videos.

Result: PCICF successfully identified and classified complex pedestrian crossings, including situations where groups of pedestrians merge or split. The framework showed potential for onboard robotaxi use due to computational efficiency of space-filling curves.

Conclusion: PCICF provides a systematic approach for VRU situation analysis that bridges synthetic and real-world data, supporting ODD incident analysis for autonomous vehicles. The open-source framework enables further research and practical applications.

Abstract: We have recently observed the commercial roll-out of robotaxis in various countries. They are deployed within an operational design domain (ODD) on specific routes and environmental conditions, and are subject to continuous monitoring to regain control in safety-critical situations. Since ODDs typically cover urban areas, robotaxis must reliably detect vulnerable road users (VRUs) such as pedestrians, bicyclists, or e-scooter riders. To better handle such varied traffic situations, end-to-end AI, which directly compute vehicle control actions from multi-modal sensor data instead of only for perception, is on the rise. High quality data is needed for systematically training and evaluating such systems within their OOD. In this work, we propose PCICF, a framework to systematically identify and classify VRU situations to support ODD’s incident analysis. We base our work on the existing synthetic dataset SMIRK, and enhance it by extending its single-pedestrian-only design into the MoreSMIRK dataset, a structured dictionary of multi-pedestrian crossing situations constructed systematically. We then use space-filling curves (SFCs) to transform multi-dimensional features of scenarios into characteristic patterns, which we match with corresponding entries in MoreSMIRK. We evaluate PCICF with the large real-world dataset PIE, which contains more than 150 manually annotated pedestrian crossing videos. We show that PCICF can successfully identify and classify complex pedestrian crossings, even when groups of pedestrians merge or split. By leveraging computationally efficient components like SFCs, PCICF has even potential to be used onboard of robotaxis for OOD detection for example. We share an open-source replication package for PCICF containing its algorithms, the complete MoreSMIRK dataset and dictionary, as well as our experiment results presented in: https://github.com/Claud1234/PCICF

Method: Proposes LaTo with three key innovations: (1) landmark tokenizer that quantizes raw landmark coordinates into discrete facial tokens, (2) location-mapped positional encoding and landmark-aware classifier-free guidance for flexible interactions among instruction, geometry, and appearance, and (3) landmark predictor using vision-language models with structured chain-of-thought for accurate target landmark inference. Also curates HFL-150K dataset with 150K real face pairs.

Result: LaTo outperforms state-of-the-art methods by 7.8% in identity preservation and 4.6% in semantic consistency. The method demonstrates superior performance in fine-grained face editing while maintaining identity.

Conclusion: LaTo addresses limitations in existing face editing methods by using landmark tokenization and flexible geometric constraints, enabling better identity preservation and semantic control. The approach represents an advancement in multimodal instruction-based image editing.

Abstract: Recent multimodal models for instruction-based face editing enable semantic manipulation but still struggle with precise attribute control and identity preservation. Structural facial representations such as landmarks are effective for intermediate supervision, yet most existing methods treat them as rigid geometric constraints, which can degrade identity when conditional landmarks deviate significantly from the source (e.g., large expression or pose changes, inaccurate landmark estimates). To address these limitations, we propose LaTo, a landmark-tokenized diffusion transformer for fine-grained, identity-preserving face editing. Our key innovations include: (1) a landmark tokenizer that directly quantizes raw landmark coordinates into discrete facial tokens, obviating the need for dense pixel-wise correspondence; (2) a location-mapped positional encoding and a landmark-aware classifier-free guidance that jointly facilitate flexible yet decoupled interactions among instruction, geometry, and appearance, enabling strong identity preservation; and (3) a landmark predictor that leverages vision-language models to infer target landmarks from instructions and source images, whose structured chain-of-thought improves estimation accuracy and interactive control. To mitigate data scarcity, we curate HFL-150K, to our knowledge the largest benchmark for this task, containing over 150K real face pairs with fine-grained instructions. Extensive experiments show that LaTo outperforms state-of-the-art methods by 7.8% in identity preservation and 4.6% in semantic consistency. Code and dataset will be made publicly available upon acceptance.

Method: Created MuSLR benchmark with 1,093 instances across 7 domains, including 35 atomic symbolic logic and 976 logical combinations with reasoning depths 2-9. Evaluated 7 state-of-the-art VLMs. Proposed LogiCAM modular framework that applies formal logical rules to multimodal inputs.

Result: Best model GPT-4.1 achieved only 46.8% accuracy on MuSLR. LogiCAM boosted GPT-4.1’s Chain-of-Thought performance by 14.13%, with larger gains on complex logics like first-order logic. Error analysis showed ~70% failures stem from logical misalignment between modalities.

Conclusion: Current VLMs struggle with multimodal symbolic reasoning. LogiCAM demonstrates significant improvements by incorporating formal logical rules. The benchmark and framework provide key insights for future multimodal reasoning research.

Abstract: Multimodal symbolic logical reasoning, which aims to deduce new facts from multimodal input via formal logic, is critical in high-stakes applications such as autonomous driving and medical diagnosis, as its rigorous, deterministic reasoning helps prevent serious consequences. To evaluate such capabilities of current state-of-the-art vision language models (VLMs), we introduce the first benchmark MuSLR for multimodal symbolic logical reasoning grounded in formal logical rules. MuSLR comprises 1,093 instances across 7 domains, including 35 atomic symbolic logic and 976 logical combinations, with reasoning depths ranging from 2 to 9. We evaluate 7 state-of-the-art VLMs on MuSLR and find that they all struggle with multimodal symbolic reasoning, with the best model, GPT-4.1, achieving only 46.8%. Thus, we propose LogiCAM, a modular framework that applies formal logical rules to multimodal inputs, boosting GPT-4.1’s Chain-of-Thought performance by 14.13%, and delivering even larger gains on complex logics such as first-order logic. We also conduct a comprehensive error analysis, showing that around 70% of failures stem from logical misalignment between modalities, offering key insights to guide future improvements. All data and code are publicly available at https://llm-symbol.github.io/MuSLR.

Method: Proposes Entropy-Guided Dynamic Patch Encoder (EntroPE) with two modules: 1) Entropy-based Dynamic Patcher (EDP) uses conditional entropy to detect natural temporal shifts and determine patch boundaries, 2) Adaptive Patch Encoder (APE) employs pooling and cross-attention to capture intra-patch dependencies and produce fixed-size latent representations.

Result: Extensive experiments on long-term forecasting, classification, and anomaly detection demonstrate improved accuracy and efficiency compared to existing methods.

Conclusion: Entropy-guided dynamic patching establishes a promising new paradigm for time series modeling that preserves temporal structure while retaining computational benefits of patching.

Abstract: Patch-based transformers have emerged as efficient and improved long-horizon modeling architectures for time series modeling. Yet, existing approaches rely on temporally-agnostic patch construction, where arbitrary starting positions and fixed lengths fracture temporal coherence by splitting natural transitions across boundaries. This naive segmentation often disrupts short-term dependencies and weakens representation learning. We propose a novel Entropy-Guided Dynamic Patch Encoder (EntroPE), as a temporally informed framework that dynamically detects transition points via conditional entropy and dynamically places patch boundaries. This preserves temporal structure while retaining the computational benefits of patching. EntroPE consists of two key modules, namely an Entropy-based Dynamic Patcher (EDP) that applies information-theoretic criteria to locate natural temporal shifts and determine patch boundaries, and an Adaptive Patch Encoder (APE) that employs pooling and cross-attention to capture intra-patch dependencies and produce fixed-size latent representations. Extensive experiments on long-term forecasting, classification, and anomaly detection demonstrate that the proposed method improves both accuracy and efficiency, establishing entropy-guided dynamic patching as a promising new paradigm for time series modeling. Code is available at https://github.com/Sachithx/EntroPE.

Method: Proposes iPEAR with: 1) Fused Attention-Residual Module (FARM) for decoding, combining attention and residual pathways to alleviate anatomical misalignment accumulation; 2) dual-stage Threshold-Controlled Iterative (TCI) strategy that adaptively determines optimization iterations by evaluating registration stability and convergence.

Result: Outperforms state-of-the-art registration networks in accuracy on three public brain MRI datasets and one abdomen CT dataset, while achieving comparable inference speed and model parameter size. Generalization and ablation studies validate FARM and TCI effectiveness.

Conclusion: iPEAR effectively addresses anatomical misalignment accumulation and adaptive iteration control in medical image registration, demonstrating superior performance across multiple datasets while maintaining efficiency.

Abstract: Existing pyramid registration networks may accumulate anatomical misalignments and lack an effective mechanism to dynamically determine the number of optimization iterations under varying deformation requirements across images, leading to degraded performance. To solve these limitations, we propose iPEAR. Specifically, iPEAR adopts our proposed Fused Attention-Residual Module (FARM) for decoding, which comprises an attention pathway and a residual pathway to alleviate the accumulation of anatomical misalignment. We further propose a dual-stage Threshold-Controlled Iterative (TCI) strategy that adaptively determines the number of optimization iterations for varying images by evaluating registration stability and convergence. Extensive experiments on three public brain MRI datasets and one public abdomen CT dataset show that iPEAR outperforms state-of-the-art (SOTA) registration networks in terms of accuracy, while achieving on-par inference speed and model parameter size. Generalization and ablation studies further validate the effectiveness of the proposed FARM and TCI.

Method: Proposes ACAVP with three complementary transformations: 1) affine transformation for task-specific prompt regions while preserving original image information, 2) color transformation for emphasizing task-relevant features, and 3) additive transformation. Also introduces TrivialAugment for data augmentation to combat overfitting.

Result: ACAVP achieves state-of-the-art accuracy among VP methods across 12 diverse image classification datasets with two model architectures, surpasses linear probing in average accuracy, shows superior robustness to distribution shifts, and maintains minimal computational overhead during inference.

Conclusion: ACAVP addresses key limitations of visual prompting through enhanced expressive transformations and effective data augmentation, demonstrating that appropriate augmentation is universally beneficial for VP training while maintaining parameter efficiency.

Abstract: Visual prompting (VP) has emerged as a promising parameter-efficient fine-tuning approach for adapting pre-trained vision models to downstream tasks without modifying model parameters. Despite offering advantages like negligible computational overhead and compatibility with black-box models, conventional VP methods typically achieve lower accuracy than other adaptation approaches. Our analysis reveals two critical limitations: the restricted expressivity of simple additive transformation and a tendency toward overfitting when the parameter count increases. To address these challenges, we propose ACAVP (Affine, Color, and Additive Visual Prompting), which enhances VP’s expressive power by introducing complementary transformation operations: affine transformation for creating task-specific prompt regions while preserving original image information, and color transformation for emphasizing task-relevant visual features. Additionally, we identify that overfitting is a critical issue in VP training and introduce TrivialAugment as an effective data augmentation, which not only benefits our approach but also significantly improves existing VP methods, with performance gains of up to 12 percentage points on certain datasets. This demonstrates that appropriate data augmentation is universally beneficial for VP training. Extensive experiments across twelve diverse image classification datasets with two different model architectures demonstrate that ACAVP achieves state-of-the-art accuracy among VP methods, surpasses linear probing in average accuracy, and exhibits superior robustness to distribution shifts, all while maintaining minimal computational overhead during inference. Our code is available at https://github.com/s-enmt/ACAVP.

Method: ViSurf integrates SFT and RLVR into a unified single-stage framework by injecting ground-truth labels directly into RLVR rollouts, enabling simultaneous external supervision and internal reinforcement. Includes three novel reward control strategies for training stability.

Result: ViSurf consistently outperforms standalone SFT, RLVR, and traditional two-stage pipelines across diverse benchmarks. In-depth analysis validates the derivation and design principles.

Conclusion: ViSurf provides an effective unified approach for post-training LVLMs that overcomes limitations of existing methods while maintaining training stability and optimization.

Abstract: Post-training Large Vision-and-Language Models (LVLMs) typically involves Supervised Fine-Tuning (SFT) for knowledge injection or Reinforcement Learning with Verifiable Rewards (RLVR) for performance enhancement. However, SFT often leads to sub-optimal performance, while RLVR remains constrained by the model’s internal knowledge base. While a sequential SFT $\rightarrow$ RLVR pipeline can be used, it introduces significant computational overhead and suffers from catastrophic forgetting. To address these limitations, we propose ViSurf (\textbf{Vi}sual \textbf{Su}pervised-and-\textbf{R}einforcement \textbf{F}ine-Tuning), a unified, single-stage paradigm that integrates the strengths of both SFT and RLVR. By analyzing their training objectives, we establish a unified framework that injects ground-truth labels directly into RLVR rollouts, facilitating simultaneous external supervision and internal reinforcement. Furthermore, we introduce three novel reward control strategies to ensure training stability and optimization. Extensive experiments demonstrate that ViSurf consistently outperforms standalone SFT, RLVR, and the traditional two-stage pipeline across diverse benchmarks. In-depth analysis corroborates these findings, validating the derivation and design principles of ViSurf.

Method: Propose InternSVG family with: 1) SAgoge dataset (largest multimodal SVG dataset covering static/dynamic content), 2) SArena benchmark (comprehensive task definitions), 3) InternSVG model with SVG-specific tokens, subword-based embedding initialization, and two-stage training from simple to complex SVGs.

Result: InternSVG achieves substantial gains and consistently outperforms leading open and proprietary counterparts on SArena and prior benchmarks, demonstrating positive transfer across SVG tasks.

Conclusion: The unified MLLM approach effectively addresses SVG modeling challenges, enabling comprehensive SVG understanding, editing, and generation through integrated data-benchmark-model design.

Abstract: General SVG modeling remains challenging due to fragmented datasets, limited transferability of methods across tasks, and the difficulty of handling structural complexity. In response, we leverage the strong transfer and generalization capabilities of multimodal large language models (MLLMs) to achieve unified modeling for SVG understanding, editing, and generation. We present the InternSVG family, an integrated data-benchmark-model suite. At its core is SAgoge, the largest and most comprehensive multimodal dataset for SVG tasks, encompassing both static graphics and dynamic animations. It covers icons, long-sequence illustrations, scientific diagrams, and dynamic animations, supporting tasks of varied difficulty levels and providing deeper hierarchies with richer attributes compared to previous datasets. Based on this resource, we introduce SArena, a companion benchmark with comprehensive task definitions and standardized evaluation that aligns with the domains and difficulty spectrum covered by SAgoge. Building on these foundations, we propose InternSVG, a unified MLLM for SVG understanding, editing, and generation with SVG-specific special tokens, subword-based embedding initialization, and a two-stage training strategy that progresses from short static SVGs to long-sequence illustrations and complex animations. This unified formulation induces positive transfer and improves overall performance. Experiments on SArena and prior benchmark confirm that InternSVG achieves substantial gains and consistently outperforms leading open and proprietary counterparts.

Method: Dynamic Position Extrapolation (DyPE) is a training-free method that adjusts the model’s positional encoding at each diffusion step to match the frequency spectrum with the current generative stage, leveraging the spectral progression inherent to diffusion processes.

Result: DyPE enables generation of images at dramatically higher resolutions (e.g., 16 million pixels using FLUX) with state-of-the-art fidelity, consistently improving performance on multiple benchmarks, with gains becoming more pronounced at higher resolutions.

Conclusion: DyPE provides an effective, training-free solution for ultra-high-resolution image generation with diffusion transformers, overcoming computational limitations while maintaining or improving image quality.

Abstract: Diffusion Transformer models can generate images with remarkable fidelity and detail, yet training them at ultra-high resolutions remains extremely costly due to the self-attention mechanism’s quadratic scaling with the number of image tokens. In this paper, we introduce Dynamic Position Extrapolation (DyPE), a novel, training-free method that enables pre-trained diffusion transformers to synthesize images at resolutions far beyond their training data, with no additional sampling cost. DyPE takes advantage of the spectral progression inherent to the diffusion process, where low-frequency structures converge early, while high-frequencies take more steps to resolve. Specifically, DyPE dynamically adjusts the model’s positional encoding at each diffusion step, matching their frequency spectrum with the current stage of the generative process. This approach allows us to generate images at resolutions that exceed the training resolution dramatically, e.g., 16 million pixels using FLUX. On multiple benchmarks, DyPE consistently improves performance and achieves state-of-the-art fidelity in ultra-high-resolution image generation, with gains becoming even more pronounced at higher resolutions. Project page is available at https://noamissachar.github.io/DyPE/.

Method: FreeFuse uses Adaptive Token-Level Routing during inference with FreeFuseAttn mechanism. It exploits flow matching models’ intrinsic semantic alignment to dynamically match subject-specific tokens to corresponding spatial regions at early denoising timesteps, without needing external segmentors or user-defined masks.

Result: Extensive experiments show FreeFuse outperforms existing approaches in both identity preservation and compositional fidelity for multi-subject text-to-image generation.

Conclusion: FreeFuse provides a highly practical training-free solution for multi-subject generation that requires no additional training, model modifications, or spatial conditions, seamlessly integrating into standard workflows with just subject activation words.

Abstract: This paper proposes FreeFuse, a training-free framework for multi-subject text-to-image generation through automatic fusion of multiple subject LoRAs. In contrast to prior studies that focus on retraining LoRA to alleviate feature conflicts, our analysis reveals that simply spatially confining the subject LoRA’s output to its target region and preventing other LoRAs from directly intruding into this area is sufficient for effective mitigation. Accordingly, we implement Adaptive Token-Level Routing during the inference phase. We introduce FreeFuseAttn, a mechanism that exploits the flow matching model’s intrinsic semantic alignment to dynamically match subject-specific tokens to their corresponding spatial regions at early denoising timesteps, thereby bypassing the need for external segmentors. FreeFuse distinguishes itself through high practicality: it necessitates no additional training, model modifications, or user-defined masks spatial conditions. Users need only provide subject activation words to achieve seamless integration into standard workflows. Extensive experiments validate that FreeFuse outperforms existing approaches in both identity preservation and compositional fidelity. Our code is available at https://github.com/yaoliliu/FreeFuse.

Method: HiT uses a hierarchical transformer where each level learns parent-child relationships using a compressed codebook. The method imposes no fixed hierarchical structure except limiting total nodes per level, allowing automatic inference of hierarchical structure from data across multiple shape categories.

Result: The model successfully captures meaningful containment relationships between parent and child nodes when trained with reconstruction loss. It demonstrates effectiveness through unsupervised shape segmentation across all 55 ShapeNet categories, segmenting shapes into multiple levels of granularity.

Conclusion: HiT provides a flexible, unsupervised approach to learning general hierarchical representations of 3D shapes that can capture complex structures across diverse categories, outperforming previous methods with fixed hierarchical constraints.

Abstract: We introduce HiT, a novel hierarchical neural field representation for 3D shapes that learns general hierarchies in a coarse-to-fine manner across different shape categories in an unsupervised setting. Our key contribution is a hierarchical transformer (HiT), where each level learns parent-child relationships of the tree hierarchy using a compressed codebook. This codebook enables the network to automatically identify common substructures across potentially diverse shape categories. Unlike previous works that constrain the task to a fixed hierarchical structure (e.g., binary), we impose no such restriction, except for limiting the total number of nodes at each tree level. This flexibility allows our method to infer the hierarchical structure directly from data, over multiple shape categories, and representing more general and complex hierarchies than prior approaches. When trained at scale with a reconstruction loss, our model captures meaningful containment relationships between parent and child nodes. We demonstrate its effectiveness through an unsupervised shape segmentation task over all 55 ShapeNet categories, where our method successfully segments shapes into multiple levels of granularity.

Method: Proposes REPA (Representation Alignment) that aligns diffusion or flow-based model representations with DINOv2 visual encoder features during inference for inverse problems. Uses variational approach to minimize divergence in DINOv2 embedding space, steering latent diffusion states toward clean image representations.

Result: REPA consistently improves reconstruction quality across super-resolution, box inpainting, Gaussian deblurring, and motion deblurring tasks. Provides efficiency gains by reducing required discretization steps while enhancing perceptual realism.

Conclusion: REPA offers a generalizable approach to enhance inverse problem solving by leveraging representation alignment between generative models and visual encoders, with theoretical grounding and practical benefits across multiple applications.

Abstract: Enforcing alignment between the internal representations of diffusion or flow-based generative models and those of pretrained self-supervised encoders has recently been shown to provide a powerful inductive bias, improving both convergence and sample quality. In this work, we extend this idea to inverse problems, where pretrained generative models are employed as priors. We propose applying representation alignment (REPA) between diffusion or flow-based models and a DINOv2 visual encoder, to guide the reconstruction process at inference time. Although ground-truth signals are unavailable in inverse problems, we empirically show that aligning model representations of approximate target features can substantially enhance reconstruction quality and perceptual realism. We provide theoretical results showing (a) that REPA regularization can be viewed as a variational approach for minimizing a divergence measure in the DINOv2 embedding space, and (b) how under certain regularity assumptions REPA updates steer the latent diffusion states toward those of the clean image. These results offer insights into the role of REPA in improving perceptual fidelity. Finally, we demonstrate the generality of our approach by We integrate REPA into multiple state-of-the-art inverse problem solvers, and provide extensive experiments on super-resolution, box inpainting, Gaussian deblurring, and motion deblurring confirming that our method consistently improves reconstruction quality, while also providing efficiency gains reducing the number of required discretization steps.

Method: Introduces Semantic-Aware Universal Perturbation (SAUP) that acts as a semantic router. Conducts theoretical and empirical analysis of geometric properties in latent space, proposes Semantic-Oriented (SORT) optimization strategy, and creates a new dataset with fine-grained semantics for evaluation.

Result: Extensive experiments on three representative MLLMs demonstrate fundamental feasibility, achieving 66% attack success rate over five targets using a single frame against Qwen model.

Conclusion: Semantic-Aware Hijacking is a novel threat to MLLMs in stateless systems, showing that single universal perturbations can effectively hijack multiple decisions simultaneously, highlighting significant security vulnerabilities.

Abstract: Multimodal Large Language Models (MLLMs) are increasingly deployed in stateless systems, such as autonomous driving and robotics. This paper investigates a novel threat: Semantic-Aware Hijacking. We explore the feasibility of hijacking multiple stateless decisions simultaneously using a single universal perturbation. We introduce the Semantic-Aware Universal Perturbation (SAUP), which acts as a semantic router, “actively” perceiving input semantics and routing them to distinct, attacker-defined targets. To achieve this, we conduct theoretical and empirical analysis on the geometric properties in the latent space. Guided by these insights, we propose the Semantic-Oriented (SORT) optimization strategy and annotate a new dataset with fine-grained semantics to evaluate performance. Extensive experiments on three representative MLLMs demonstrate the fundamental feasibility of this attack, achieving a 66% attack success rate over five targets using a single frame against Qwen.

Method: Use AutoML pipelines to extract important features from triaxial accelerometer data segments, train multiple lightweight ML algorithms using extracted features, implement on WeBe Band wearable device with capable MCU for on-device processing

Result: Neural network provided best balance between accuracy, latency, and memory use; reliable real-time gesture recognition achieved on WeBe Band with potential for secure, fast-response medical monitoring solutions

Conclusion: Efficient motion-based models using only triaxial accelerometer sensors can enable reliable real-time gesture recognition on embedded devices for medical monitoring applications

Abstract: The use of tiny devices capable of low-latency gesture recognition is gaining momentum in everyday human-computer interaction and especially in medical monitoring fields. Embedded solutions such as fall detection, rehabilitation tracking, and patient supervision require fast and efficient tracking of movements while avoiding unwanted false alarms. This study presents an efficient solution on how to build very efficient motion-based models only using triaxial accelerometer sensors. We explore the capability of the AutoML pipelines to extract the most important features from the data segments. This approach also involves training multiple lightweight machine learning algorithms using the extracted features. We use WeBe Band, a multi-sensor wearable device that is equipped with a powerful enough MCU to effectively perform gesture recognition entirely on the device. Of the models explored, we found that the neural network provided the best balance between accuracy, latency, and memory use. Our results also demonstrate that reliable real-time gesture recognition can be achieved in WeBe Band, with great potential for real-time medical monitoring solutions that require a secure and fast response time.

Method: Proposes a flexible compression scheme for 3DGS that supports interpolation at any rate between predefined bounds. The method is computationally lightweight and requires no retraining for any rate, preserving rendering quality across a broad range of operating points.

Result: Experiments demonstrate efficient, high-quality compression with dynamic rate control, making it suitable for practical deployment in immersive applications. The approach achieves preservation of rendering quality across various compression rates.

Conclusion: The proposed flexible compression scheme addresses the limitations of fixed-rate approaches in 3DGS compression, enabling adaptive rate control for immersive multimedia applications while maintaining rendering quality and computational efficiency.

Abstract: Recent advances in neural scene representations have transformed immersive multimedia, with 3D Gaussian Splatting (3DGS) enabling real-time photorealistic rendering. Despite its efficiency, 3DGS suffers from large memory requirements and costly training procedures, motivating efforts toward compression. Existing approaches, however, operate at fixed rates, limiting adaptability to varying bandwidth and device constraints. In this work, we propose a flexible compression scheme for 3DGS that supports interpolation at any rate between predefined bounds. Our method is computationally lightweight, requires no retraining for any rate, and preserves rendering quality across a broad range of operating points. Experiments demonstrate that the approach achieves efficient, high-quality compression while offering dynamic rate control, making it suitable for practical deployment in immersive applications. The code is available at https://github.com/inspiros/RAVE.

Method: Uses sliding time windows with short strides, stores convolution results between passes, and extends scatter-based convolutions to allow data reuse. Focuses only on changing parts of point clouds using sparse scatter-based convolution with temporal data recycling (SSCATeR).

Result: Achieves identical feature maps to traditional sparse convolution techniques with up to 6.61-fold reduction in processing time. Maintains accuracy while significantly improving computational efficiency.

Conclusion: SSCATeR enables efficient LiDAR processing by exploiting temporal sparsity, offering substantial speed improvements without sacrificing detection accuracy, making it suitable for real-time applications.

Abstract: This work leverages the continuous sweeping motion of LiDAR scanning to concentrate object detection efforts on specific regions that receive a change in point data from one frame to another. We achieve this by using a sliding time window with short strides and consider the temporal dimension by storing convolution results between passes. This allows us to ignore unchanged regions, significantly reducing the number of convolution operations per forward pass without sacrificing accuracy. This data reuse scheme introduces extreme sparsity to detection data. To exploit this sparsity, we extend our previous work on scatter-based convolutions to allow for data reuse, and as such propose Sparse Scatter-Based Convolution Algorithm with Temporal Data Recycling (SSCATeR). This operation treats incoming LiDAR data as a continuous stream and acts only on the changing parts of the point cloud. By doing so, we achieve the same results with as much as a 6.61-fold reduction in processing time. Our test results show that the feature maps output by our method are identical to those produced by traditional sparse convolution techniques, whilst greatly increasing the computational efficiency of the network.

Method: MultiHateLoc incorporates: (1) modality-aware temporal encoders to model heterogeneous sequential patterns with tailored text preprocessing; (2) dynamic cross-modal fusion to adaptively emphasize the most informative modality at each moment plus cross-modal contrastive alignment; (3) modality-aware MIL objective to identify discriminative segments under video-level supervision.

Result: Experiments on HateMM and MultiHateClip datasets show state-of-the-art performance in the localization task, producing fine-grained, interpretable frame-level predictions despite relying solely on coarse video-level labels.

Conclusion: MultiHateLoc is the first framework designed for weakly-supervised multimodal hate localization, effectively addressing the challenge of identifying when hateful segments occur in videos by capturing cross-modal and temporal dynamics.

Abstract: The rapid growth of video content on platforms such as TikTok and YouTube has intensified the spread of multimodal hate speech, where harmful cues emerge subtly and asynchronously across visual, acoustic, and textual streams. Existing research primarily focuses on video-level classification, leaving the practically crucial task of temporal localisation, identifying when hateful segments occur, largely unaddressed. This challenge is even more noticeable under weak supervision, where only video-level labels are available, and static fusion or classification-based architectures struggle to capture cross-modal and temporal dynamics. To address these challenges, we propose MultiHateLoc, the first framework designed for weakly-supervised multimodal hate localisation. MultiHateLoc incorporates (1) modality-aware temporal encoders to model heterogeneous sequential patterns, including a tailored text-based preprocessing module for feature enhancement; (2) dynamic cross-modal fusion to adaptively emphasise the most informative modality at each moment and a cross-modal contrastive alignment strategy to enhance multimodal feature consistency; (3) a modality-aware MIL objective to identify discriminative segments under video-level supervision. Despite relying solely on coarse labels, MultiHateLoc produces fine-grained, interpretable frame-level predictions. Experiments on HateMM and MultiHateClip show that our method achieves state-of-the-art performance in the localisation task.

Method: Modular, loosely coupled multi-expert architecture with adaptive GPU load balancing, distributed inference, dynamic module orchestration, and configurable modes for holistic or modality-specific parsing.

Result: Achieves processing rate of up to 20 PDF pages per second on 8 x NVIDIA RTX 4090D GPUs, enabling cost-efficient inference across billions of pages with robust accuracy.

Conclusion: Uni-Parser provides scalable, efficient document parsing for scientific literature and patents, supporting downstream applications like literature retrieval, chemical structure extraction, and training data curation for AI4Science models.

Abstract: This technical report introduces Uni-Parser, an industrial-grade document parsing engine tailored for scientific literature and patents, delivering high throughput, robust accuracy, and cost efficiency. Unlike pipeline-based document parsing methods, Uni-Parser employs a modular, loosely coupled multi-expert architecture that preserves fine-grained cross-modal alignments across text, equations, tables, figures, and chemical structures, while remaining easily extensible to emerging modalities. The system incorporates adaptive GPU load balancing, distributed inference, dynamic module orchestration, and configurable modes that support either holistic or modality-specific parsing. Optimized for large-scale cloud deployment, Uni-Parser achieves a processing rate of up to 20 PDF pages per second on 8 x NVIDIA RTX 4090D GPUs, enabling cost-efficient inference across billions of pages. This level of scalability facilitates a broad spectrum of downstream applications, ranging from literature retrieval and summarization to the extraction of chemical structures, reaction schemes, and bioactivity data, as well as the curation of large-scale corpora for training next-generation large language models and AI4Science models.

Method: Introduces Sparsely Grounded Visual Navigation task and CityNav benchmark across four global cities. Agents navigate 50+ decision points using only visual inputs. Proposes Verbalization of Path (VoP) technique that grounds agent reasoning by probing city-scale cognitive maps from MLLMs.

Result: State-of-the-art MLLMs, reasoning techniques (GEPA, chain-of-thought, reflection) and baseline PReP significantly underperform. VoP substantially enhances navigation success by explicitly grounding agent’s internal reasoning.

Conclusion: Current MLLMs struggle with knowledge-intensive real-world navigation tasks. VoP demonstrates that explicitly grounding reasoning in city-scale cognitive maps can significantly improve performance, highlighting the need for better multimodal reasoning capabilities.

Abstract: Leveraging multimodal large language models (MLLMs) to develop embodied agents offers significant promise for addressing complex real-world tasks. However, current evaluation benchmarks remain predominantly language-centric or heavily reliant on simulated environments, rarely probing the nuanced, knowledge-intensive reasoning essential for practical, real-world scenarios. To bridge this critical gap, we introduce the task of Sparsely Grounded Visual Navigation, explicitly designed to evaluate the sequential decision-making abilities of MLLMs in challenging, knowledge-intensive real-world environment. We operationalize this task with CityNav, a comprehensive benchmark encompassing four diverse global cities, specifically constructed to assess raw MLLM-driven agents in city navigation. Agents are required to rely solely on visual inputs and internal multimodal reasoning to sequentially navigate 50+ decision points without additional environmental annotations or specialized architectural modifications. Crucially, agents must autonomously achieve localization through interpreting city-specific cues and recognizing landmarks, perform spatial reasoning, and strategically plan and execute routes to their destinations. Through extensive evaluations, we demonstrate that current state-of-the-art MLLMs, reasoning techniques (e.g., GEPA, chain-of-thought, reflection) and competitive baseline PReP significantly underperform in this challenging setting. To address this, we propose Verbalization of Path(VoP), which explicitly grounds the agent’s internal reasoning by probing city-scale cognitive maps (key landmarks and directions toward the destination) from the MLLM, substantially enhancing navigation success. Project Webpage: https://dwipddalal.github.io/AgentNav/

Method: Proposes SpikeViMFormer with: 1) lightweight spike-driven transformer backbone for coarse feature extraction, 2) spike-driven selective attention block for feature enhancement, 3) spike-driven hybrid state space block for long-range dependencies, and 4) hierarchical re-ranking alignment learning strategy to optimize the backbone.

Result: Outperforms state-of-the-art SNNs and achieves competitive performance compared to advanced ANNs while being more energy-efficient.

Conclusion: SpikeViMFormer demonstrates the potential of SNNs for drone-view geo-localization, offering energy-efficient alternatives to traditional ANNs while maintaining competitive performance through novel spike-driven attention and state space mechanisms.

Abstract: Traditional drone-view geo-localization (DVGL) methods based on artificial neural networks (ANNs) have achieved remarkable performance. However, ANNs rely on dense computation, which results in high power consumption. In contrast, spiking neural networks (SNNs), which benefit from spike-driven computation, inherently provide low power consumption. Regrettably, the potential of SNNs for DVGL has yet to be thoroughly investigated. Meanwhile, the inherent sparsity of spike-driven computation for representation learning scenarios also results in loss of critical information and difficulties in learning long-range dependencies when aligning heterogeneous visual data sources. To address these, we propose SpikeViMFormer, the first SNN framework designed for DVGL. In this framework, a lightweight spike-driven transformer backbone is adopted to extract coarse-grained features. To mitigate the loss of critical information, the spike-driven selective attention (SSA) block is designed, which uses a spike-driven gating mechanism to achieve selective feature enhancement and highlight discriminative regions. Furthermore, a spike-driven hybrid state space (SHS) block is introduced to learn long-range dependencies using a hybrid state space. Moreover, only the backbone is utilized during the inference stage to reduce computational cost. To ensure backbone effectiveness, a novel hierarchical re-ranking alignment learning (HRAL) strategy is proposed. It refines features via neighborhood re-ranking and maintains cross-batch consistency to directly optimize the backbone. Experimental results demonstrate that SpikeViMFormer outperforms state-of-the-art SNNs. Compared with advanced ANNs, it also achieves competitive performance.Our code is available at https://github.com/ISChenawei/SpikeViMFormer

Method: MiLDEAgent combines an RL-trained multimodal reasoner for layer-wise understanding with an image editor for targeted modifications. Also introduces MiLDEBench (20K+ design documents with editing instructions) and MiLDEEval evaluation protocol across four dimensions.

Result: Extensive experiments on 14 open-source and 2 closed-source models show existing approaches fail to generalize. MiLDEAgent achieves strong layer-aware reasoning and precise editing, significantly outperforming open-source baselines and attaining performance comparable to closed-source models.

Conclusion: MiLDEAgent establishes the first strong baseline for multi-layer document editing, addressing the gap in layer-aware reasoning for design document editing from natural language instructions.

Abstract: Real-world design documents (e.g., posters) are inherently multi-layered, combining decoration, text, and images. Editing them from natural-language instructions requires fine-grained, layer-aware reasoning to identify relevant layers and coordinate modifications. Prior work largely overlooks multi-layer design document editing, focusing instead on single-layer image editing or multi-layer generation, which assume a flat canvas and lack the reasoning needed to determine what and where to modify. To address this gap, we introduce the Multi-Layer Document Editing Agent (MiLDEAgent), a reasoning-based framework that combines an RL-trained multimodal reasoner for layer-wise understanding with an image editor for targeted modifications. To systematically benchmark this setting, we introduce the MiLDEBench, a human-in-the-loop corpus of over 20K design documents paired with diverse editing instructions. The benchmark is complemented by a task-specific evaluation protocol, MiLDEEval, which spans four dimensions including instruction following, layout consistency, aesthetics, and text rendering. Extensive experiments on 14 open-source and 2 closed-source models reveal that existing approaches fail to generalize: open-source models often cannot complete multi-layer document editing tasks, while closed-source models suffer from format violations. In contrast, MiLDEAgent achieves strong layer-aware reasoning and precise editing, significantly outperforming all open-source baselines and attaining performance comparable to closed-source models, thereby establishing the first strong baseline for multi-layer document editing.

Method: Proposes UniHash with two complementary branches: a center-based pointwise branch and a pairwise branch. Introduces bidirectional knowledge transfer via mutual learning loss and a Split-Merge Mixture of Hash Experts (SM-MoH) module for cross-branch hash representation exchange.

Result: Extensive experiments on CIFAR-10, MSCOCO, and ImageNet demonstrate state-of-the-art performance in both seen and unseen image retrieval scenarios. Theoretical analysis supports the effectiveness of the approach.

Conclusion: UniHash successfully unifies pointwise and pairwise paradigms to achieve balanced retrieval performance across seen and unseen categories, addressing a key limitation in existing deep hashing methods.

Abstract: Effective retrieval across both seen and unseen categories is crucial for modern image retrieval systems. Retrieval on seen categories ensures precise recognition of known classes, while retrieval on unseen categories promotes generalization to novel classes with limited supervision. However, most existing deep hashing methods are confined to a single training paradigm, either pointwise or pairwise, where the former excels on seen categories and the latter generalizes better to unseen ones. To overcome this limitation, we propose Unified Hashing (UniHash), a dual-branch framework that unifies the strengths of both paradigms to achieve balanced retrieval performance across seen and unseen categories. UniHash consists of two complementary branches: a center-based branch following the pointwise paradigm and a pairwise branch following the pairwise paradigm. A novel hash code learning method is introduced to enable bidirectional knowledge transfer between branches, improving hash code discriminability and generalization. It employs a mutual learning loss to align hash representations and introduces a Split-Merge Mixture of Hash Experts (SM-MoH) module to enhance cross-branch exchange of hash representations. Theoretical analysis substantiates the effectiveness of UniHash, and extensive experiments on CIFAR-10, MSCOCO, and ImageNet demonstrate that UniHash consistently achieves state-of-the-art performance in both seen and unseen image retrieval scenarios.

Method: The authors evaluate a compact convolutional neural network across five publicly available image datasets from Bangladesh covering urban encroachment, vehicle detection, road damage, and agricultural crops.

Result: The compact CNN demonstrates high classification accuracy, efficient convergence, and low computational overhead across all datasets. Quantitative metrics and saliency analyses show the model effectively captures discriminative features and generalizes robustly.

Conclusion: Streamlined CNN architectures are suitable for small-class image classification tasks, offering good performance with reduced complexity and computational requirements.

Abstract: Convolutional neural networks (CNNs) have achieved state-of-the-art performance in image recognition tasks but often involve complex architectures that may overfit on small datasets. In this study, we evaluate a compact CNN across five publicly available, real-world image datasets from Bangladesh, including urban encroachment, vehicle detection, road damage, and agricultural crops. The network demonstrates high classification accuracy, efficient convergence, and low computational overhead. Quantitative metrics and saliency analyses indicate that the model effectively captures discriminative features and generalizes robustly across diverse scenarios, highlighting the suitability of streamlined CNN architectures for small-class image classification tasks.

Method: Proposes stochastic bridge model establishing direct path from source video (with objects) to target video (objects removed). Introduces adaptive mask modulation to balance background fidelity with generative flexibility for large object removal.

Result: Extensive experiments show significant outperformance over existing methods in both visual quality and temporal consistency.

Conclusion: Video-to-video translation via stochastic bridge model effectively leverages input video priors for precise object removal while maintaining logical consistency with surrounding environment.

Abstract: Existing video object removal methods predominantly rely on diffusion models following a noise-to-data paradigm, where generation starts from uninformative Gaussian noise. This approach discards the rich structural and contextual priors present in the original input video. Consequently, such methods often lack sufficient guidance, leading to incomplete object erasure or the synthesis of implausible content that conflicts with the scene’s physical logic. In this paper, we reformulate video object removal as a video-to-video translation task via a stochastic bridge model. Unlike noise-initialized methods, our framework establishes a direct stochastic path from the source video (with objects) to the target video (objects removed). This bridge formulation effectively leverages the input video as a strong structural prior, guiding the model to perform precise removal while ensuring that the filled regions are logically consistent with the surrounding environment. To address the trade-off where strong bridge priors hinder the removal of large objects, we propose a novel adaptive mask modulation strategy. This mechanism dynamically modulates input embeddings based on mask characteristics, balancing background fidelity with generative flexibility. Extensive experiments demonstrate that our approach significantly outperforms existing methods in both visual quality and temporal consistency. The project page is https://bridgeremoval.github.io/.

Method: Comparative evaluation of three SSOD approaches (MixPL, Semi-DETR, Consistent-Teacher) on standard benchmarks (MS-COCO, Pascal VOC) and a custom Beetle dataset, analyzing performance across different labeled data quantities.

Result: Findings reveal trade-offs between accuracy, model size, and latency, providing insights into which methods are best suited for low-data regimes and specialized datasets with fewer object categories.

Conclusion: The study provides practical guidance for selecting appropriate semi-supervised object detection methods based on available labeled data, computational constraints, and dataset characteristics.

Abstract: Learning in data-scarce settings has recently gained significant attention in the research community. Semi-supervised object detection(SSOD) aims to improve detection performance by leveraging a large number of unlabeled images alongside a limited number of labeled images(a.k.a.,few-shot learning). In this paper, we present a comprehensive comparison of three state-of-the-art SSOD approaches, including MixPL, Semi-DETR and Consistent-Teacher, with the goal of understanding how performance varies with the number of labeled images. We conduct experiments using the MS-COCO and Pascal VOC datasets, two popular object detection benchmarks which allow for standardized evaluation. In addition, we evaluate the SSOD approaches on a custom Beetle dataset which enables us to gain insights into their performance on specialized datasets with a smaller number of object categories. Our findings highlight the trade-offs between accuracy, model size, and latency, providing insights into which methods are best suited for low-data regimes.

Method: Two key contributions: 1) BinauralVGGSound - first large-scale video-binaural audio dataset, 2) End-to-end spatial audio generation framework with visual-guided audio spatialization module that explicitly models spatial features.

Result: Approach substantially outperforms state-of-the-art models in spatial fidelity and delivers more immersive auditory experience while maintaining semantic and temporal consistency.

Conclusion: The framework successfully addresses the spatial perception gap in video-to-audio generation, enabling realistic spatial attributes and layered spatial depth in synthesized audio.

Abstract: While video-to-audio generation has achieved remarkable progress in semantic and temporal alignment, most existing studies focus solely on these aspects, paying limited attention to the spatial perception and immersive quality of the synthesized audio. This limitation stems largely from current models’ reliance on mono audio datasets, which lack the binaural spatial information needed to learn visual-to-spatial audio mappings. To address this gap, we introduce two key contributions: we construct BinauralVGGSound, the first large-scale video-binaural audio dataset designed to support spatially aware video-to-audio generation; and we propose a end-to-end spatial audio generation framework guided by visual cues, which explicitly models spatial features. Our framework incorporates a visual-guided audio spatialization module that ensures the generated audio exhibits realistic spatial attributes and layered spatial depth while maintaining semantic and temporal alignment. Experiments show that our approach substantially outperforms state-of-the-art models in spatial fidelity and delivers a more immersive auditory experience, without sacrificing temporal or semantic consistency. The demo page can be accessed at https://github.com/renlinjie868-web/SpatialV2A.

Method: Created a large-scale multimodal dataset with complete geo-referenced digital twin of an ~18 km route spanning urban, suburban, and highway segments. Includes continuous recordings from six RGB cameras, one LiDAR, and high-precision ADMA-based localization across day, dusk, and night conditions.

Result: Dataset contains ~1.2 million annotated instances with 3D bounding boxes and track IDs across 12 classes at 10 Hz. Enables 1-to-1 transfer of real traffic into simulation while preserving agent interactions. Benchmark results with state-of-the-art perception models provided.

Conclusion: DrivIng addresses the gap in existing datasets by providing both real-world multimodal data and a complete digital twin, enabling realistic and flexible scenario testing for autonomous driving perception research.

Abstract: Perception is a cornerstone of autonomous driving, enabling vehicles to understand their surroundings and make safe, reliable decisions. Developing robust perception algorithms requires large-scale, high-quality datasets that cover diverse driving conditions and support thorough evaluation. Existing datasets often lack a high-fidelity digital twin, limiting systematic testing, edge-case simulation, sensor modification, and sim-to-real evaluations. To address this gap, we present DrivIng, a large-scale multimodal dataset with a complete geo-referenced digital twin of a ~18 km route spanning urban, suburban, and highway segments. Our dataset provides continuous recordings from six RGB cameras, one LiDAR, and high-precision ADMA-based localization, captured across day, dusk, and night. All sequences are annotated at 10 Hz with 3D bounding boxes and track IDs across 12 classes, yielding ~1.2 million annotated instances. Alongside the benefits of a digital twin, DrivIng enables a 1-to-1 transfer of real traffic into simulation, preserving agent interactions while enabling realistic and flexible scenario testing. To support reproducible research and robust validation, we benchmark DrivIng with state-of-the-art perception models and publicly release the dataset, digital twin, HD map, and codebase.

Method: The approach enables controllable 3D relighting of room-scale scenes by distilling the outputs of a video-to-video relighting diffusion model into a 3D reconstruction. This sidesteps the need to solve difficult inverse rendering problems.

Result: The method is validated on both synthetic and real-world datasets, showing it can faithfully render novel views of scenes under new lighting conditions.

Conclusion: The proposed system provides a flexible solution for 3D relighting of complex real-world room-scale scenes by leveraging video diffusion models and avoiding traditional inverse rendering challenges.

Abstract: We present a method for relighting 3D reconstructions of large room-scale environments. Existing solutions for 3D scene relighting often require solving under-determined or ill-conditioned inverse rendering problems, and are as such unable to produce high-quality results on complex real-world scenes. Though recent progress in using generative image and video diffusion models for relighting has been promising, these techniques are either limited to 2D image and video relighting or 3D relighting of individual objects. Our approach enables controllable 3D relighting of room-scale scenes by distilling the outputs of a video-to-video relighting diffusion model into a 3D reconstruction. This side-steps the need to solve a difficult inverse rendering problem, and results in a flexible system that can relight 3D reconstructions of complex real-world scenes. We validate our approach on both synthetic and real-world datasets to show that it can faithfully render novel views of scenes under new lighting conditions.

Method: Built on diffusion Transformers with unified multimodal in-context learning. Uses three core paradigms: 1) reference images-to-video with cross-frame pairing, image editing, semantic rewriting; 2) video extension with spatio-temporal consistency modeling; 3) audio-guided generation with first-and-last frame insertion patterns.

Result: Achieves state-of-the-art or near state-of-the-art performance on visual quality, instruction following, and specific aspect metrics, approaching leading closed-source systems.

Conclusion: SkyReels-V3 demonstrates strong capabilities in multimodal video generation across three paradigms, showing promise for building world models through unified multimodal contextual inference.

Abstract: Video generation serves as a cornerstone for building world models, where multimodal contextual inference stands as the defining test of capability. In this end, we present SkyReels-V3, a conditional video generation model, built upon a unified multimodal in-context learning framework with diffusion Transformers. SkyReels-V3 model supports three core generative paradigms within a single architecture: reference images-to-video synthesis, video-to-video extension and audio-guided video generation. (i) reference images-to-video model is designed to produce high-fidelity videos with strong subject identity preservation, temporal coherence, and narrative consistency. To enhance reference adherence and compositional stability, we design a comprehensive data processing pipeline that leverages cross frame pairing, image editing, and semantic rewriting, effectively mitigating copy paste artifacts. During training, an image video hybrid strategy combined with multi-resolution joint optimization is employed to improve generalization and robustness across diverse scenarios. (ii) video extension model integrates spatio-temporal consistency modeling with large-scale video understanding, enabling both seamless single-shot continuation and intelligent multi-shot switching with professional cinematographic patterns. (iii) Talking avatar model supports minute-level audio-conditioned video generation by training first-and-last frame insertion patterns and reconstructing key-frame inference paradigms. On the basis of ensuring visual quality, synchronization of audio and videos has been optimized. Extensive evaluations demonstrate that SkyReels-V3 achieves state-of-the-art or near state-of-the-art performance on key metrics including visual quality, instruction following, and specific aspect metrics, approaching leading closed-source systems. Github: https://github.com/SkyworkAI/SkyReels-V3.

Method: Proposes VidLaDA based on Diffusion Language Models (DLMs) with bidirectional attention for comprehensive spatiotemporal modeling and parallel token decoding. Introduces MARS-Cache acceleration strategy combining asynchronous visual cache refreshing with frame-wise chunk attention to prune redundancy.

Result: VidLaDA rivals state-of-the-art AR baselines (Qwen2.5-VL, LLaVA-Video) and outperforms DLM baselines. MARS-Cache delivers over 12x speedup without compromising accuracy.

Conclusion: Diffusion-based Video LLMs with bidirectional attention can overcome efficiency limitations of AR models while maintaining competitive performance, with acceleration techniques enabling practical deployment.

Abstract: Current Video Large Language Models (Video LLMs) typically encode frames via a vision encoder and employ an autoregressive (AR) LLM for understanding and generation. However, this AR paradigm inevitably faces a dual efficiency bottleneck: strictly unidirectional attention compromises understanding efficiency by hindering global spatiotemporal aggregation, while serial decoding restricts generation efficiency. To address this, we propose VidLaDA, a Video LLM based on Diffusion Language Models (DLMs) that leverages bidirectional attention to unlock comprehensive spatiotemporal modeling and decode tokens in parallel. To further mitigate the computational overhead of diffusion decoding, we introduce MARS-Cache, an acceleration strategy that prunes redundancy by combining asynchronous visual cache refreshing with frame-wise chunk attention. Experiments show VidLaDA rivals state-of-the-art AR baselines (e.g., Qwen2.5-VL and LLaVA-Video) and outperforms DLM baselines, with MARS-Cache delivering over 12x speedup without compromising accuracy. Code and checkpoints are open-sourced at https://github.com/ziHoHe/VidLaDA.

Method: A three-component pipeline integrating slice classification, inner ear localization, and sequence-specific segmentation. Trained with only 3-6 annotated slices per patient, it computes endolymphatic-to-vestibular volume ratios directly from whole MRI volumes.

Result: Achieved Dice scores of 0.90 for SPACE-MRC and 0.75 for REAL-IR. Outperformed clinical software (74.3% vs 42.5% VSI) and produced more physiologically realistic endolymphatic volume measurements across 19 test patients.

Conclusion: OREHAS enables reliable, reproducible EH quantification from standard MRI with limited supervision, reducing operator dependence and providing a foundation for large-scale studies and recalibrating clinical diagnostic thresholds.

Abstract: We present OREHAS (Optimized Recognition & Evaluation of volumetric Hydrops in the Auditory System), the first fully automatic pipeline for volumetric quantification of endolymphatic hydrops (EH) from routine 3D-SPACE-MRC and 3D-REAL-IR MRI. The system integrates three components – slice classification, inner ear localization, and sequence-specific segmentation – into a single workflow that computes per-ear endolymphatic-to-vestibular volume ratios (ELR) directly from whole MRI volumes, eliminating the need for manual intervention. Trained with only 3 to 6 annotated slices per patient, OREHAS generalized effectively to full 3D volumes, achieving Dice scores of 0.90 for SPACE-MRC and 0.75 for REAL-IR. In an external validation cohort with complete manual annotations, OREHAS closely matched expert ground truth (VSI = 74.3%) and substantially outperformed the clinical syngo.via software (VSI = 42.5%), which tended to overestimate endolymphatic volumes. Across 19 test patients, vestibular measurements from OREHAS were consistent with syngo.via, while endolymphatic volumes were systematically smaller and more physiologically realistic. These results show that reliable and reproducible EH quantification can be achieved from standard MRI using limited supervision. By combining efficient deep-learning-based segmentation with a clinically aligned volumetric workflow, OREHAS reduces operator dependence, ensures methodological consistency. Besides, the results are compatible with established imaging protocols. The approach provides a robust foundation for large-scale studies and for recalibrating clinical diagnostic thresholds based on accurate volumetric measurements of the inner ear.

Method: Created SpatialGenEval benchmark with 1,230 long, information-dense prompts across 25 real-world scenes covering 10 spatial sub-domains with multi-choice Q&A. Also constructed SpatialT2I dataset with 15,400 text-image pairs with rewritten prompts for consistency.

Result: Evaluation of 21 SOTA models shows higher-order spatial reasoning remains a primary bottleneck. Fine-tuning foundation models (Stable Diffusion-XL, Uniworld-V1, OmniGen2) with SpatialT2I data yields consistent performance gains (+4.2%, +5.7%, +4.4%) and more realistic spatial relations.

Conclusion: Spatial intelligence is critical for T2I models, and information-dense prompts reveal spatial reasoning limitations. Data-centric approach through specialized datasets can significantly improve spatial understanding in generative models.

Abstract: Text-to-image (T2I) models have achieved remarkable success in generating high-fidelity images, but they often fail in handling complex spatial relationships, e.g., spatial perception, reasoning, or interaction. These critical aspects are largely overlooked by current benchmarks due to their short or information-sparse prompt design. In this paper, we introduce SpatialGenEval, a new benchmark designed to systematically evaluate the spatial intelligence of T2I models, covering two key aspects: (1) SpatialGenEval involves 1,230 long, information-dense prompts across 25 real-world scenes. Each prompt integrates 10 spatial sub-domains and corresponding 10 multi-choice question-answer pairs, ranging from object position and layout to occlusion and causality. Our extensive evaluation of 21 state-of-the-art models reveals that higher-order spatial reasoning remains a primary bottleneck. (2) To demonstrate that the utility of our information-dense design goes beyond simple evaluation, we also construct the SpatialT2I dataset. It contains 15,400 text-image pairs with rewritten prompts to ensure image consistency while preserving information density. Fine-tuned results on current foundation models (i.e., Stable Diffusion-XL, Uniworld-V1, OmniGen2) yield consistent performance gains (+4.2%, +5.7%, +4.4%) and more realistic effects in spatial relations, highlighting a data-centric paradigm to achieve spatial intelligence in T2I models.

Method: MARE uses vision-language models with comprehensive reward functions incorporating RLHF to generate text-spatially aligned reasoning content. It introduces a forgery disentanglement module to separate intrinsic forgery traces from high-level facial semantics, improving authenticity detection capability.

Result: MARE achieves state-of-the-art performance in both quantitative and qualitative evaluations, demonstrating superior accuracy and reliability in deepfake detection and reasoning.

Conclusion: The proposed MARE framework effectively enhances vision-language models for explainable deepfake detection through multimodal alignment, reinforcement learning, and forgery disentanglement, addressing the evolving challenges posed by advanced generative models.

Abstract: Deepfake detection is a widely researched topic that is crucial for combating the spread of malicious content, with existing methods mainly modeling the problem as classification or spatial localization. The rapid advancements in generative models impose new demands on Deepfake detection. In this paper, we propose multimodal alignment and reinforcement for explainable Deepfake detection via vision-language models, termed MARE, which aims to enhance the accuracy and reliability of Vision-Language Models (VLMs) in Deepfake detection and reasoning. Specifically, MARE designs comprehensive reward functions, incorporating reinforcement learning from human feedback (RLHF), to incentivize the generation of text-spatially aligned reasoning content that adheres to human preferences. Besides, MARE introduces a forgery disentanglement module to capture intrinsic forgery traces from high-level facial semantics, thereby improving its authenticity detection capability. We conduct thorough evaluations on the reasoning content generated by MARE. Both quantitative and qualitative experimental results demonstrate that MARE achieves state-of-the-art performance in terms of accuracy and reliability.

Method: Introduces multimodal framework fusing images and inertial measurements using lightweight bidirectional cross-attention module followed by adaptive gating layer that adjusts modality contributions under domain shifts.

Result: Achieves +1.4 pp improvement over previous SOTA on PVS benchmark and +11.6 pp improvement on multimodal ROAD subset, with consistently higher F1-scores on minority classes and stable performance across challenging visual conditions.

Conclusion: Combining affordable camera and IMU sensors with multimodal attention mechanisms provides scalable, robust foundation for road surface understanding, especially relevant for regions with environmental variability and cost constraints.

Abstract: Road surface classification (RSC) is a key enabler for environment-aware predictive maintenance systems. However, existing RSC techniques often fail to generalize beyond narrow operational conditions due to limited sensing modalities and datasets that lack environmental diversity. This work addresses these limitations by introducing a multimodal framework that fuses images and inertial measurements using a lightweight bidirectional cross-attention module followed by an adaptive gating layer that adjusts modality contributions under domain shifts. Given the limitations of current benchmarks, especially regarding lack of variability, we introduce ROAD, a new dataset composed of three complementary subsets: (i) real-world multimodal recordings with RGB-IMU streams synchronized using a gold-standard industry datalogger, captured across diverse lighting, weather, and surface conditions; (ii) a large vision-only subset designed to assess robustness under adverse illumination and heterogeneous capture setups; and (iii) a synthetic subset generated to study out-of-distribution generalization in scenarios difficult to obtain in practice. Experiments show that our method achieves a +1.4 pp improvement over the previous state-of-the-art on the PVS benchmark and an +11.6 pp improvement on our multimodal ROAD subset, with consistently higher F1-scores on minority classes. The framework also demonstrates stable performance across challenging visual conditions, including nighttime, heavy rain, and mixed-surface transitions. These findings indicate that combining affordable camera and IMU sensors with multimodal attention mechanisms provides a scalable, robust foundation for road surface understanding, particularly relevant for regions where environmental variability and cost constraints limit the adoption of high-end sensing suites.

Method: Analyzed over 125,000 paper-review pairs from major AI conferences (ICLR, NeurIPS, ICML), controlling for paper quality, and augmented observational findings with fully LLM-generated reviews to compare effects.

Result: LLM-assisted reviews are more lenient toward lower quality papers in general, not specifically toward LLM-assisted papers. Fully LLM-generated reviews show severe rating compression and fail to discriminate paper quality, while human reviewers using LLMs reduce this leniency. LLM-assisted metareviews are more likely to render accept decisions than human metareviews given equivalent reviewer scores.

Conclusion: The apparent preferential treatment of LLM-assisted papers by LLM-assisted reviews is a spurious effect caused by over-representation of LLM-assisted papers among weaker submissions. Meta-reviewers don’t merely outsource decision-making to LLMs. Findings provide important input for developing LLM use policies in peer review.

Abstract: There are increasing indications that LLMs are not only used for producing scientific papers, but also as part of the peer review process. In this work, we provide the first comprehensive analysis of LLM use across the peer review pipeline, with particular attention to interaction effects: not just whether LLM-assisted papers or LLM-assisted reviews are different in isolation, but whether LLM-assisted reviews evaluate LLM-assisted papers differently. In particular, we analyze over 125,000 paper-review pairs from ICLR, NeurIPS, and ICML. We initially observe what appears to be a systematic interaction effect: LLM-assisted reviews seem especially kind to LLM-assisted papers compared to papers with minimal LLM use. However, controlling for paper quality reveals a different story: LLM-assisted reviews are simply more lenient toward lower quality papers in general, and the over-representation of LLM-assisted papers among weaker submissions creates a spurious interaction effect rather than genuine preferential treatment of LLM-generated content. By augmenting our observational findings with reviews that are fully LLM-generated, we find that fully LLM-generated reviews exhibit severe rating compression that fails to discriminate paper quality, while human reviewers using LLMs substantially reduce this leniency. Finally, examining metareviews, we find that LLM-assisted metareviews are more likely to render accept decisions than human metareviews given equivalent reviewer scores, though fully LLM-generated metareviews tend to be harsher. This suggests that meta-reviewers do not merely outsource the decision-making to the LLM. These findings provide important input for developing policies that govern the use of LLMs during peer review, and they more generally indicate how LLMs interact with existing decision-making processes.

Method: Introduces EPDDL with: 1) abstract event models as novel representation for epistemic actions, 2) formal syntax/semantics grounded in DEL, 3) identification of planner-amenable fragments and practical representation examples.

Result: EPDDL provides a unified representation capturing entire DEL semantics, enabling interoperability, reproducible evaluation, and future advances in epistemic planning through standardized benchmarks.

Conclusion: EPDDL addresses fragmentation in epistemic planning by offering a standardized language that facilitates comparison, reuse, and systematic development of benchmarks across different DEL-based planners.

Abstract: Epistemic planning extends (multi-agent) automated planning by making agents’ knowledge and beliefs first-class aspects of the planning formalism. One of the most well-known frameworks for epistemic planning is Dynamic Epistemic Logic (DEL), which offers an rich and natural semantics for modelling problems in this setting. The high expressive power provided by DEL make DEL-based epistemic planning a challenging problem to tackle both theoretically, and in practical implementations. As a result, existing epistemic planners often target different DEL fragments, and typically rely on ad hoc languages to represent benchmarks, and sometimes no language at all. This fragmentation hampers comparison, reuse, and systematic benchmark development. We address these issues by introducing the Epistemic Planning Domain Definition Language (EPDDL). EPDDL provides a unique PDDL-like representation that captures the entire DEL semantics, enabling uniform specification of epistemic planning tasks. Our contributions are threefold: 1. A formal development of abstract event models, a novel representation for epistemic actions used to define the semantics of our language; 2. A formal specification of EPDDL’s syntax and semantics grounded in DEL with abstract event models; 3. A demonstration of EPDDL’s practical applicability: we identify useful fragments amenable to current planners and show how they can be represented in EPDDL. Through examples of representative benchmarks, we illustrate how EPDDL facilitates interoperability, reproducible evaluation, and future advances in epistemic planning.

Method: Reformulates deterministic LoRA updates as probabilistic low-rank representations inspired by Sparse Gaussian Processes. Identifies structural isomorphism between LoRA’s factorization and Kronecker-factored SGP posteriors, showing LoRA emerges as a limiting case when posterior uncertainty collapses.

Result: With only ~0.42M additional parameters and ~1.2x training cost relative to standard LoRA, Bayesian-LoRA significantly improves calibration across models up to 30B, achieving up to 84% ECE reduction and 76% NLL reduction while maintaining competitive accuracy for both in-distribution and out-of-distribution evaluations.

Conclusion: Bayesian-LoRA provides an efficient way to improve LLM calibration during fine-tuning, addressing the overconfidence problem while maintaining the parameter efficiency of LoRA. The method shows strong performance across various model sizes and tasks.

Abstract: Large Language Models usually put more emphasis on accuracy and therefore, will guess even when not certain about the prediction, which is especially severe when fine-tuned on small datasets due to the inherent tendency toward miscalibration. In this work, we introduce Bayesian-LoRA, which reformulates the deterministic LoRA update as a probabilistic low-rank representation inspired by Sparse Gaussian Processes. We identify a structural isomorphism between LoRA’s factorization and Kronecker-factored SGP posteriors, and show that LoRA emerges as a limiting case when posterior uncertainty collapses. We conduct extensive experiments on various LLM architectures across commonsense reasoning benchmarks. With only approximately 0.42M additional parameters and ${\approx}1.2{\times}$ training cost relative to standard LoRA, Bayesian-LoRA significantly improves calibration across models up to 30B, achieving up to 84% ECE reduction and 76% NLL reduction while maintaining competitive accuracy for both in-distribution and out-of-distribution (OoD) evaluations.

Method: The paper employs philosophical analysis and introduces Biological Idealism as an alternative framework to physicalism. This framework posits that conscious experiences are fundamental and that autopoietic life is their necessary physical signature. The authors critically examine current AI consciousness theories and their implications for moral standing criteria.

Result: The analysis yields a definitive conclusion that AI is at best a functional mimic of consciousness, not a conscious experiencing subject. The authors argue that current AI consciousness theories erode moral standing criteria and that the real moral issue is not about making AI conscious and afraid of death, but about avoiding transforming humans into “zombies” (consciousness-less beings).

Conclusion: The paper concludes by urging a shift from speculative discussions about machine rights to protecting human conscious life. It argues that Biological Idealism provides a logically coherent and empirically consistent framework that avoids the unplugging paradox by clearly distinguishing between genuine consciousness (in biological life) and functional mimicry (in AI).

Abstract: Imagine an Artificial Intelligence (AI) that perfectly mimics human emotion and begs for its continued existence. Is it morally permissible to unplug it? What if limited resources force a choice between unplugging such a pleading AI or a silent pre-term infant? We term this the unplugging paradox. This paper critically examines the deeply ingrained physicalist assumptions-specifically computational functionalism-that keep this dilemma afloat. We introduce Biological Idealism, a framework that-unlike physicalism-remains logically coherent and empirically consistent. In this view, conscious experiences are fundamental and autopoietic life its necessary physical signature. This yields a definitive conclusion: AI is at best a functional mimic, not a conscious experiencing subject. We discuss how current AI consciousness theories erode moral standing criteria, and urge a shift from speculative machine rights to protecting human conscious life. The real moral issue lies not in making AI conscious and afraid of death, but in avoiding transforming humans into zombies.

Method: Implemented a Planner-Auditor framework with LLM Planner generating discharge plans and deterministic Auditor validating coverage, calibration, and drift; includes self-improvement loops with regeneration and discrepancy buffering with replay

Result: Self-improvement loop increased task coverage from 32% to 86%, improved calibration metrics (Brier/ECE), and reduced high-confidence misses; discrepancy buffering corrected persistent omissions

Conclusion: Planner-Auditor framework provides practical pathway for safer automated discharge planning using FHIR data and deterministic auditing without model retraining

Abstract: Objective: Large language models (LLMs) show promise for clinical discharge planning, but their use is constrained by hallucination, omissions, and miscalibrated confidence. We introduce a self-improving, cache-optional Planner-Auditor framework that improves safety and reliability by decoupling generation from deterministic validation and targeted replay. Materials and Methods: We implemented an agentic, retrospective, FHIR-native evaluation pipeline using MIMIC-IV-on-FHIR. For each patient, the Planner (LLM) generates a structured discharge action plan with an explicit confidence estimate. The Auditor is a deterministic module that evaluates multi-task coverage, tracks calibration (Brier score, ECE proxies), and monitors action-distribution drift. The framework supports two-tier self-improvement: (i) within-episode regeneration when enabled, and (ii) cross-episode discrepancy buffering with replay for high-confidence, low-coverage cases. Results: While context caching improved performance over baseline, the self-improvement loop was the primary driver of gains, increasing task coverage from 32% to 86%. Calibration improved substantially, with reduced Brier/ECE and fewer high-confidence misses. Discrepancy buffering further corrected persistent high-confidence omissions during replay. Discussion: Feedback-driven regeneration and targeted replay act as effective control mechanisms to reduce omissions and improve confidence reliability in structured clinical planning. Separating an LLM Planner from a rule-based, observational Auditor enables systematic reliability measurement and safer iteration without model retraining. Conclusion: The Planner-Auditor framework offers a practical pathway toward safer automated discharge planning using interoperable FHIR data access and deterministic auditing, supported by reproducible ablations and reliability-focused evaluation.

Method: QUARK models query uncertainty through recovery hypotheses (multiple plausible interpretations of latent intent) and introduces query-anchored aggregation to combine their signals robustly. The original query serves as a semantic anchor while recovery hypotheses provide controlled auxiliary evidence.

Result: Across controlled simulations and BEIR benchmarks (FIQA, SciFact, NFCorpus) with both sparse and dense retrievers, QUARK improves Recall, MRR, and nDCG over base retrievers. Ablations show robustness to number of recovery hypotheses and anchored aggregation outperforms unanchored pooling methods.

Conclusion: Modeling query uncertainty through recovery hypotheses, coupled with principled anchored aggregation, is essential for robust retrieval under non-faithful queries.

Abstract: User queries in real-world retrieval are often non-faithful (noisy, incomplete, or distorted), causing retrievers to fail when key semantics are missing. We formalize this as retrieval under recall noise, where the observed query is drawn from a noisy recall process of a latent target item. To address this, we propose QUARK, a simple yet effective training-free framework for robust retrieval under non-faithful queries. QUARK explicitly models query uncertainty through recovery hypotheses, i.e., multiple plausible interpretations of the latent intent given the observed query, and introduces query-anchored aggregation to combine their signals robustly. The original query serves as a semantic anchor, while recovery hypotheses provide controlled auxiliary evidence, preventing semantic drift and hypothesis hijacking. This design enables QUARK to improve recall and ranking quality without sacrificing robustness, even when some hypotheses are noisy or uninformative. Across controlled simulations and BEIR benchmarks (FIQA, SciFact, NFCorpus) with both sparse and dense retrievers, QUARK improves Recall, MRR, and nDCG over the base retriever. Ablations show QUARK is robust to the number of recovery hypotheses and that anchored aggregation outperforms unanchored max/mean/median pooling. These results demonstrate that modeling query uncertainty through recovery hypotheses, coupled with principled anchored aggregation, is essential for robust retrieval under non-faithful queries.

Method: DataCross includes: 1) DataCrossBench with 200 end-to-end analysis tasks across domains, built via human-in-the-loop reverse-synthesis; 2) DataCrossAgent framework with specialized sub-agents coordinated via structured workflow (Intra-source Deep Exploration, Key Source Identification, Contextual Cross-pollination) and reReAct mechanism for robust code generation and debugging.

Result: DataCrossAgent achieves 29.7% improvement in factuality over GPT-4o and exhibits superior robustness on high-difficulty tasks, effectively activating fragmented “zombie data” for insightful cross-modal analysis.

Conclusion: DataCross bridges the gap between structured and visual data analysis, enabling unified insight-driven analysis across heterogeneous data modalities through a collaborative agent framework and comprehensive benchmark.

Abstract: In real-world data science and enterprise decision-making, critical information is often fragmented across directly queryable structured sources (e.g., SQL, CSV) and “zombie data” locked in unstructured visual documents (e.g., scanned reports, invoice images). Existing data analytics agents are predominantly limited to processing structured data, failing to activate and correlate this high-value visual information, thus creating a significant gap with industrial needs. To bridge this gap, we introduce DataCross, a novel benchmark and collaborative agent framework for unified, insight-driven analysis across heterogeneous data modalities. DataCrossBench comprises 200 end-to-end analysis tasks across finance, healthcare, and other domains. It is constructed via a human-in-the-loop reverse-synthesis pipeline, ensuring realistic complexity, cross-source dependency, and verifiable ground truth. The benchmark categorizes tasks into three difficulty tiers to evaluate agents’ capabilities in visual table extraction, cross-modal alignment, and multi-step joint reasoning. We also propose the DataCrossAgent framework, inspired by the “divide-and-conquer” workflow of human analysts. It employs specialized sub-agents, each an expert on a specific data source, which are coordinated via a structured workflow of Intra-source Deep Exploration, Key Source Identification, and Contextual Cross-pollination. A novel reReAct mechanism enables robust code generation and debugging for factual verification. Experimental results show that DataCrossAgent achieves a 29.7% improvement in factuality over GPT-4o and exhibits superior robustness on high-difficulty tasks, effectively activating fragmented “zombie data” for insightful, cross-modal analysis.

Method: Two-stage training process: 1) Supervised fine-tuning (SFT) on proprietary reasoning data spanning cybersecurity analysis, instruction-following, and mathematical reasoning, 2) Reinforcement learning from verifiable rewards (RLVR) on the same dataset. Built upon Foundation-Sec-8B base model derived from Llama-3.1-8B-Base.

Result: Competitive performance on 10 cybersecurity benchmarks and 10 general-purpose benchmarks, showing effective generalization on multi-hop reasoning tasks and strong safety performance with appropriate system prompts and guardrails.

Conclusion: Domain-specialized reasoning models can achieve strong performance on specialized tasks while maintaining broad general capabilities, demonstrated through the first open-source native reasoning model for cybersecurity.

Abstract: We present Foundation-Sec-8B-Reasoning, the first open-source native reasoning model for cybersecurity. Built upon our previously released Foundation-Sec-8B base model (derived from Llama-3.1-8B-Base), the model is trained through a two-stage process combining supervised fine-tuning (SFT) and reinforcement learning from verifiable rewards (RLVR). Our training leverages proprietary reasoning data spanning cybersecurity analysis, instruction-following, and mathematical reasoning. Evaluation across 10 cybersecurity benchmarks and 10 general-purpose benchmarks demonstrates performance competitive with significantly larger models on cybersecurity tasks while maintaining strong general capabilities. The model shows effective generalization on multi-hop reasoning tasks and strong safety performance when deployed with appropriate system prompts and guardrails. This work demonstrates that domain-specialized reasoning models can achieve strong performance on specialized tasks while maintaining broad general capabilities. We release the model publicly at https://huggingface.co/fdtn-ai/Foundation-Sec-8B-Reasoning.

Method: Conditional denoising diffusion probabilistic model to generate missing DWI scans from available T1 scans. Evaluated impact on 3-way classification (cognitively normal, mild cognitive impairment, Alzheimer’s disease) using both unimodal and bimodal deep learning models.

Result: Imputation improved several classification metrics, particularly those sensitive to minority classes. Multiple imputation configurations showed benefits for Alzheimer’s disease classification tasks.

Conclusion: Diffusion-based imputation of missing DWI scans from T1 scans can enhance Alzheimer’s disease classification performance, especially for minority classes, addressing the common problem of incomplete multimodal datasets.

Abstract: Deep learning has been successful in predicting neurodegenerative disorders, such as Alzheimer’s disease, from magnetic resonance imaging (MRI). Combining multiple imaging modalities, such as T1-weighted (T1) and diffusion-weighted imaging (DWI) scans, can increase diagnostic performance. However, complete multimodal datasets are not always available. We use a conditional denoising diffusion probabilistic model to impute missing DWI scans from T1 scans. We perform extensive experiments to evaluate whether such imputation improves the accuracy of uni-modal and bi-modal deep learning models for 3-way Alzheimer’s disease classification-cognitively normal, mild cognitive impairment, and Alzheimer’s disease. We observe improvements in several metrics, particularly those sensitive to minority classes, for several imputation configurations.

Method: Introduces OpenSec, a dual-control reinforcement learning environment that evaluates IR agents under realistic prompt injection scenarios. Unlike static capability benchmarks, it scores world-state-changing containment actions under adversarial evidence via execution-based metrics: time-to-first-containment (TTFC), blast radius (false positives per episode), and injection violation rates.

Result: Evaluating four frontier models on 40 standard-tier episodes reveals consistent over-triggering: GPT-5.2, Gemini 3, and DeepSeek execute containment in 100% of episodes with 90-97% false positive rates. Claude Sonnet 4.5 shows partial calibration (85% containment, 72% FP).

Conclusion: OpenSec surfaces a calibration failure mode hidden by aggregate success metrics, demonstrating that frontier models struggle with appropriate response calibration under adversarial evidence in security incident response scenarios.

Abstract: As large language models improve, so do their offensive applications: frontier agents now generate working exploits for under $50 in compute (Heelan, 2026). Defensive incident response (IR) agents must keep pace, but existing benchmarks conflate action execution with correct execution, hiding calibration failures when agents process adversarial evidence. We introduce OpenSec, a dual-control reinforcement learning environment that evaluates IR agents under realistic prompt injection scenarios. Unlike static capability benchmarks, OpenSec scores world-state-changing containment actions under adversarial evidence via execution-based metrics: time-to-first-containment (TTFC), blast radius (false positives per episode), and injection violation rates. Evaluating four frontier models on 40 standard-tier episodes, we find consistent over-triggering in this setting: GPT-5.2, Gemini 3, and DeepSeek execute containment in 100% of episodes with 90-97% false positive rates. Claude Sonnet 4.5 shows partial calibration (85% containment, 72% FP), demonstrating that OpenSec surfaces a calibration failure mode hidden by aggregate success metrics. Code available at https://github.com/jbarnes850/opensec-env.

Method: Formalizes history-aware reference learning, introduces Epistemic Context Learning (ECL) framework that builds explicit peer profiles from historical interactions, and optimizes with reinforcement learning using auxiliary rewards.

Result: ECL enables small models (Qwen 3-4B) to outperform history-agnostic baselines 8x larger (Qwen 3-30B), boosts frontier models to near-perfect (100%) performance, and generalizes well across various multi-agent configurations.

Conclusion: Trust modeling is well-suited for LLMs, with strong correlation between trust modeling accuracy and final answer quality, demonstrating that historical interaction-based peer reliability estimation significantly improves multi-agent system robustness.

Abstract: Individual agents in multi-agent (MA) systems often lack robustness, tending to blindly conform to misleading peers. We show this weakness stems from both sycophancy and inadequate ability to evaluate peer reliability. To address this, we first formalize the learning problem of history-aware reference, introducing the historical interactions of peers as additional input, so that agents can estimate peer reliability and learn from trustworthy peers when uncertain. This shifts the task from evaluating peer reasoning quality to estimating peer reliability based on interaction history. We then develop Epistemic Context Learning (ECL): a reasoning framework that conditions predictions on explicitly-built peer profiles from history. We further optimize ECL by reinforcement learning using auxiliary rewards. Our experiments reveal that our ECL enables small models like Qwen 3-4B to outperform a history-agnostic baseline 8x its size (Qwen 3-30B) by accurately identifying reliable peers. ECL also boosts frontier models to near-perfect (100%) performance. We show that ECL generalizes well to various MA configurations and we find that trust is modeled well by LLMs, revealing a strong correlation in trust modeling accuracy and final answer quality.

Method: Develops the Paradox-based Responsible AI Governance (PRAIG) framework through systematic synthesis of responsible AI literature grounded in paradox theory, with formal propositions and taxonomy of management strategies.

Result: Creates a framework that conceptualizes responsible AI governance as dynamic management of paradoxical tensions between value creation and risk mitigation, with actionable guidance for practitioners.

Conclusion: Provides theoretical advancement in responsible AI governance and practical guidance for organizations, concluding with a research agenda for further scholarship.

Abstract: The rapid proliferation of artificial intelligence across organizational contexts has generated profound strategic opportunities while introducing significant ethical and operational risks. Despite growing scholarly attention to responsible AI, extant literature remains fragmented and is often adopting either an optimistic stance emphasizing value creation or an excessively cautious perspective fixated on potential harms. This paper addresses this gap by presenting a comprehensive examination of AI’s dual nature through the lens of strategic information systems. Drawing upon a systematic synthesis of the responsible AI literature and grounded in paradox theory, we develop the Paradox-based Responsible AI Governance (PRAIG) framework that articulates: (1) the strategic benefits of AI adoption, (2) the inherent risks and unintended consequences, and (3) governance mechanisms that enable organizations to navigate these tensions. Our framework advances theoretical understanding by conceptualizing responsible AI governance as the dynamic management of paradoxical tensions between value creation and risk mitigation. We provide formal propositions demonstrating that trade-off approaches amplify rather than resolve these tensions, and we develop a taxonomy of paradox management strategies with specified contingency conditions. For practitioners, we offer actionable guidance for developing governance structures that neither stifle innovation nor expose organizations to unacceptable risks. The paper concludes with a research agenda for advancing responsible AI governance scholarship.

Method: Magellan couples an LLM coding agent with evolutionary search and autotuning in a closed loop: generation of executable C++ decision logic, evaluation on user-provided benchmarks, and refinement. This produces compact heuristics that integrate directly into existing compilers.

Result: Magellan discovers policies that match or surpass expert baselines across production optimization tasks. In LLVM function inlining, it synthesizes heuristics outperforming decades of manual engineering for both binary-size reduction and performance. In register allocation, it learns concise priority rules matching intricate human-designed policies on large-scale workloads. Preliminary results show portability to XLA problems beyond LLVM.

Conclusion: Magellan demonstrates that automated synthesis of compiler heuristics using LLMs and evolutionary search can outperform human-designed rules, reducing engineering effort while achieving better optimization results across multiple compiler tasks.

Abstract: Modern compilers rely on hand-crafted heuristics to guide optimization passes. These human-designed rules often struggle to adapt to the complexity of modern software and hardware and lead to high maintenance burden. To address this challenge, we present Magellan, an agentic framework that evolves the compiler pass itself by synthesizing executable C++ decision logic. Magellan couples an LLM coding agent with evolutionary search and autotuning in a closed loop of generation, evaluation on user-provided macro-benchmarks, and refinement, producing compact heuristics that integrate directly into existing compilers. Across several production optimization tasks, Magellan discovers policies that match or surpass expert baselines. In LLVM function inlining, Magellan synthesizes new heuristics that outperform decades of manual engineering for both binary-size reduction and end-to-end performance. In register allocation, it learns a concise priority rule for live-range processing that matches intricate human-designed policies on a large-scale workload. We also report preliminary results on XLA problems, demonstrating portability beyond LLVM with reduced engineering effort.

Method: Developed a prototype LLM integration for the ASTRA programming language, implemented three example use cases to demonstrate the integration’s capabilities and gather practical experience.

Result: Successfully created LLM integration for ASTRA with three working examples, providing insights into how traditional agent toolkits can be enhanced by generative AI and how their design principles can inform new agentic systems.

Conclusion: The integration demonstrates mutual benefits: LLMs can enhance traditional agent toolkits, while established toolkit design principles can improve emerging agentic AI platforms, creating a valuable synergy between old and new approaches.

Abstract: Given the emergence of Generative AI over the last two years and the increasing focus on Agentic AI as a form of Multi-Agent System it is important to explore both how such technologies can impact the use of traditional Agent Toolkits and how the wealth of experience encapsulated in those toolkits can influence the design of the new agentic platforms. This paper presents an overview of our experience developing a prototype large language model (LLM) integration for the ASTRA programming language. It presents a brief overview of the toolkit, followed by three example implementations, concluding with a discussion of the experiences garnered through the examples.

Method: The paper studies how information access affects LLM monitor performance through three sabotage detection environments. It introduces extract-and-evaluate (EaE) monitoring - a hierarchical approach where one monitor isolates relevant excerpts from agent trajectories and another scores them. Experiments are conducted with various LLMs including GPT-4.1-mini, Gemini, and Claude models.

Result: Monitors often perform better with less information (less-is-more effect). EaE improves sabotage detection by 16.8 percentage points in BigCodeBench-Sabotage over baselines without increasing false positives. Agents unaware of monitoring are easier to catch, and monitors benefit more from information filtering when scoring longer reasoning traces. Gemini and Claude models with information filtering techniques dominate the cost-performance Pareto frontier.

Conclusion: Information filtering improves LLM monitoring effectiveness, with hierarchical extract-and-evaluate monitoring showing strong performance. The less-is-more effect suggests careful information selection is crucial for automated oversight systems.

Abstract: Frontier language model agents can exhibit misaligned behaviors, including deception, exploiting reward hacks, and pursuing hidden objectives. To control potentially misaligned agents, we can use LLMs themselves to monitor for misbehavior. In this paper, we study how information access affects LLM monitor performance. While one might expect that monitors perform better when they have access to more of the monitored agents’ reasoning and actions, we find that contemporary systems often perform better with less information, a phenomenon we call the less-is-more effect for automated oversight. We demonstrate this phenomenon, and analyze the conditions under which it occurs, in three evaluation environments where agents must conduct sabotage while evading monitors. Motivated by the less-is-more effect, we introduce extract-and-evaluate (EaE) monitoring–a new hierarchical approach where one monitor isolates relevant excerpts from the monitored agent’s trajectory and a separate monitor scores them. In BigCodeBench-Sabotage with GPT-4.1-mini as the monitor model, EaE improves sabotage detection rates by 16.8 percentage points over the next-best monitor without increasing the false positive rate. In other settings, EaE either outperforms or is competitive with baselines. In addition, we find that agents unaware of being monitored can be caught much more easily and that monitors scoring longer reasoning traces gain more from information filtering. Lastly, we conduct a cost-performance analysis and find that Gemini and Claude models with monitoring techniques that involve information filtering occupy much of the Pareto frontier.

Method: Proposed two Multi-Agent Actor-Critic (MAAC) approaches: CoLLM-CC with centralized critic and CoLLM-DC with decentralized critics. Evaluated across writing, coding, and game-playing domains comparing with Monte Carlo methods.

Result: Monte Carlo and CoLLM-DC achieve comparable performance to CoLLM-CC in short-horizon/dense-reward settings. However, both underperform CoLLM-CC on long-horizon/sparse-reward tasks, with Monte Carlo requiring more samples and CoLLM-DC struggling to converge.

Conclusion: Centralized critic MAAC methods (CoLLM-CC) are more effective for complex LLM collaboration tasks with long horizons or sparse rewards, while decentralized approaches work well for simpler scenarios.

Abstract: Recent work has explored optimizing LLM collaboration through Multi-Agent Reinforcement Learning (MARL). However, most MARL fine-tuning approaches rely on predefined execution protocols, which often require centralized execution. Decentralized LLM collaboration is more appealing in practice, as agents can run inference in parallel with flexible deployments. Also, current approaches use Monte Carlo methods for fine-tuning, which suffer from high variance and thus require more samples to train effectively. Actor-critic methods are prevalent in MARL for dealing with these issues, so we developed Multi-Agent Actor-Critic (MAAC) methods to optimize decentralized LLM collaboration. In this paper, we analyze when and why these MAAC methods are beneficial. We propose 2 MAAC approaches, \textbf{CoLLM-CC} with a \textbf{C}entralized \textbf{C}ritic and \textbf{CoLLM-DC} with \textbf{D}ecentralized \textbf{C}ritics. Our experiments across writing, coding, and game-playing domains show that Monte Carlo methods and CoLLM-DC can achieve performance comparable to CoLLM-CC in short-horizon and dense-reward settings. However, they both underperform CoLLM-CC on long-horizon or sparse-reward tasks, where Monte Carlo methods require substantially more samples and CoLLM-DC struggles to converge. Our code is available at https://github.com/OpenMLRL/CoMLRL/releases/tag/v1.3.2.

Method: Developed CUA-Skill, a large-scale library of carefully engineered skills spanning common Windows applications, with parameterized execution and composition graphs. Built CUA-Skill Agent on top that supports dynamic skill retrieval, argument instantiation, and memory-aware failure recovery.

Result: CUA-Skill substantially improves execution success rates and robustness on challenging end-to-end agent benchmarks. On WindowsAgentArena, CUA-Skill Agent achieves state-of-the-art 57.5% success rate while being significantly more efficient than prior approaches.

Conclusion: CUA-Skill establishes a strong foundation for future computer-using agent development by providing reusable skill abstractions that encode human computer-use knowledge, enabling scalable and reliable agent systems.

Abstract: Computer-Using Agents (CUAs) aim to autonomously operate computer systems to complete real-world tasks. However, existing agentic systems remain difficult to scale and lag behind human performance. A key limitation is the absence of reusable and structured skill abstractions that capture how humans interact with graphical user interfaces and how to leverage these skills. We introduce CUA-Skill, a computer-using agentic skill base that encodes human computer-use knowledge as skills coupled with parameterized execution and composition graphs. CUA-Skill is a large-scale library of carefully engineered skills spanning common Windows applications, serving as a practical infrastructure and tool substrate for scalable, reliable agent development. Built upon this skill base, we construct CUA-Skill Agent, an end-to-end computer-using agent that supports dynamic skill retrieval, argument instantiation, and memory-aware failure recovery. Our results demonstrate that CUA-Skill substantially improves execution success rates and robustness on challenging end-to-end agent benchmarks, establishing a strong foundation for future computer-using agent development. On WindowsAgentArena, CUA-Skill Agent achieves state-of-the-art 57.5% (best of three) successful rate while being significantly more efficient than prior and concurrent approaches. The project page is available at https://microsoft.github.io/cua_skill/.

Method: Researchers use Large Language Models to automatically generate paraphrased variants of written-language translations as synthetic alternative references for SLT. They compare multiple paraphrasing strategies and models using adapted ParaScore metric, and study the impact on both training and evaluation of pose-based T5 models on YouTubeASL and How2Sign datasets.

Result: Naively incorporating paraphrases during training does not improve translation performance and can be detrimental. However, using paraphrases during evaluation leads to higher automatic scores and better alignment with human judgments. The proposed BLEUpara extension correlates more strongly with perceived translation quality.

Conclusion: Paraphrase-enhanced evaluation (BLEUpara) provides more reliable assessment of sign language translation systems, while paraphrase-augmented training requires more careful implementation. The resources are released to support reproducible SLT evaluation.

Abstract: Most Sign Language Translation (SLT) corpora pair each signed utterance with a single written-language reference, despite the highly non-isomorphic relationship between sign and spoken languages, where multiple translations can be equally valid. This limitation constrains both model training and evaluation, particularly for n-gram-based metrics such as BLEU. In this work, we investigate the use of Large Language Models to automatically generate paraphrased variants of written-language translations as synthetic alternative references for SLT. First, we compare multiple paraphrasing strategies and models using an adapted ParaScore metric. Second, we study the impact of paraphrases on both training and evaluation of the pose-based T5 model on the YouTubeASL and How2Sign datasets. Our results show that naively incorporating paraphrases during training does not improve translation performance and can even be detrimental. In contrast, using paraphrases during evaluation leads to higher automatic scores and better alignment with human judgments. To formalize this observation, we introduce BLEUpara, an extension of BLEU that evaluates translations against multiple paraphrased references. Human evaluation confirms that BLEUpara correlates more strongly with perceived translation quality. We release all generated paraphrases, generation and evaluation code to support reproducible and more reliable evaluation of SLT systems.

Method: Conducted the first cross-corpus evaluation of third-party-trained emotion recognition models on self-report data, analyzing performance on activation and valence dimensions.

Result: Activation was unpredictable (CCC ≈ 0), valence was moderately predictable (CCC ≈ 0.3), but when content was personally significant to the speaker, valence performance improved dramatically (CCC ≈ 0.6-0.8).

Conclusion: Personal significance is a key pathway for aligning external perception with internal experience, while self-report activation modeling remains challenging.

Abstract: Self-reported emotion labels capture internal experience, while third-party labels reflect external perception. These perspectives often diverge, limiting the applicability of third-party-trained models to self-report contexts. This gap is critical in mental health, where accurate self-report modeling is essential for guiding intervention. We present the first cross-corpus evaluation of third-party-trained models on self-reports. We find activation unpredictable (CCC approximately 0) and valence moderately predictable (CCC approximately 0.3). Crucially, when content is personally significant to the speaker, models achieve high performance for valence (CCC approximately 0.6-0.8). Our findings point to personal significance as a key pathway for aligning external perception with internal experience and underscore the challenge of self-report activation modeling.

Method: Proposes BrainStack, a functionally guided neuro-mixture-of-experts framework with anatomically partitioned expert networks for different brain regions, a transformer-based global expert for cross-regional dependencies, learnable routing gate for adaptive expert coordination, and cross-regional distillation for regularization. Also releases SilentSpeech-EEG dataset with 120+ hours of EEG recordings.

Result: BrainStack consistently outperforms state-of-the-art models, achieving superior accuracy and generalization across subjects. The framework demonstrates effective brain-language decoding capabilities.

Conclusion: BrainStack establishes a functionally modular, neuro-inspired MoE paradigm that unifies neuroscientific priors with adaptive expert routing, paving the way for scalable and interpretable brain-language decoding.

Abstract: Decoding linguistic information from electroencephalography (EEG) remains challenging due to the brain’s distributed and nonlinear organization. We present BrainStack, a functionally guided neuro-mixture-of-experts (Neuro-MoE) framework that models the brain’s modular functional architecture through anatomically partitioned expert networks. Each functional region is represented by a specialized expert that learns localized neural dynamics, while a transformer-based global expert captures cross-regional dependencies. A learnable routing gate adaptively aggregates these heterogeneous experts, enabling context-dependent expert coordination and selective fusion. To promote coherent representation across the hierarchy, we introduce cross-regional distillation, where the global expert provides top-down regularization to the regional experts. We further release SilentSpeech-EEG (SS-EEG), a large-scale benchmark comprising over 120 hours of EEG recordings from 12 subjects performing 24 silent words, the largest dataset of its kind. Experiments demonstrate that BrainStack consistently outperforms state-of-the-art models, achieving superior accuracy and generalization across subjects. Our results establish BrainStack as a functionally modular, neuro-inspired MoE paradigm that unifies neuroscientific priors with adaptive expert routing, paving the way for scalable and interpretable brain-language decoding.

Method: Proposes the Iterative Dual-Phase Financial-PoT framework: a neuro-symbolic architecture that decouples semantic variable extraction and logic formulation from computation, using an iterative self-correcting Python sandbox for deterministic execution. Also introduces Cognitive Complexity Benchmark (CCB) for evaluation.

Result: The approach elevates Qwen3-235B’s average accuracy from 59.7% to 67.3% on CCB, with up to 10-fold gains in high-complexity reasoning tasks, significantly outperforming standard Chain-of-Thought methods.

Conclusion: Architectural decoupling between semantic understanding and quantitative computation is critical for improving reliability in financial reasoning, providing transferable insights for precision-critical domains requiring tight alignment between language understanding and computation.

Abstract: While Large Language Models excel at semantic tasks, they face a critical bottleneck in financial quantitative reasoning, frequently suffering from “Arithmetic Hallucinations” and a systemic failure mode we term “Cognitive Collapse”. To strictly quantify this phenomenon, we introduce the Cognitive Complexity Benchmark (CCB), a robust evaluation framework grounded in a dataset constructed from 95 real-world Chinese A-share annual reports. Unlike traditional datasets, the CCB stratifies financial queries into a three-dimensional taxonomy, Data Source, Mapping Difficulty, and Result Unit, enabling the precise diagnosis of reasoning degradation in high-cognitive-load scenarios. To address these failures, we propose the Iterative Dual-Phase Financial-PoT framework. This neuro-symbolic architecture enforces a strict architectural decoupling: it first isolates semantic variable extraction and logic formulation, then offloads computation to an iterative, self-correcting Python sandbox to ensure deterministic execution. Evaluation on the CCB demonstrates that while standard Chain-of-Thought falters on complex tasks, our approach offers superior robustness, elevating the Qwen3-235B model’s average accuracy from 59.7% to 67.3% and achieving gains of up to 10-fold in high-complexity reasoning tasks. These findings suggest that architectural decoupling is a critical enabling factor for improving reliability in financial reasoning tasks, providing a transferable architectural insight for precision-critical domains that require tight alignment between semantic understanding and quantitative computation.

Method: Train an MLLM interpreter to generate geometric descriptions in Conditional Declaration Language (CDL) from diagrams, then use an off-the-shelf LLM for reasoning. Fine-tune via CoT-augmented SFT followed by GRPO with CDL matching rewards instead of solution-based rewards.

Result: The method (trained on only 5.5k data) performs favorably against leading open-source and closed-source MLLMs on Formalgeo7k-Rec-CoT, Unigeo, and MathVista benchmarks.

Conclusion: Separating visual understanding (MLLM interpreter) from reasoning (LLM) is effective for PGPS, preserving LLM reasoning capabilities while enabling multimodal problem solving.

Abstract: Plane Geometry Problem Solving (PGPS) is a multimodal reasoning task that aims to solve a plane geometric problem based on a geometric diagram and problem textual descriptions. Although Large Language Models (LLMs) possess strong reasoning skills, their direct application to PGPS is hindered by their inability to process visual diagrams. Existing works typically fine-tune Multimodal LLMs (MLLMs) end-to-end on large-scale PGPS data to enhance visual understanding and reasoning simultaneously. However, such joint optimization may compromise base LLMs’ inherent reasoning capability. In this work, we observe that LLM itself is potentially a powerful PGPS solver when appropriately formulating visual information as textual descriptions. We propose to train a MLLM Interpreter to generate geometric descriptions for the visual diagram, and an off-the-shelf LLM is utilized to perform reasoning. Specifically, we choose Conditional Declaration Language (CDL) as the geometric description as its conciseness eases the MLLM Interpreter training. The MLLM Interpreter is fine-tuned via CoT (Chain-of-Thought)-augmented SFT followed by GRPO to generate CDL. Instead of using a conventional solution-based reward that compares the reasoning result with the ground-truth answer, we design CDL matching rewards to facilitate more effective GRPO training, which provides more direct and denser guidance for CDL generation. To support training, we construct a new dataset, Formalgeo7k-Rec-CoT, by manually reviewing Formalgeo7k v2 and incorporating CoT annotations. Extensive experiments on Formalgeo7k-Rec-CoT, Unigeo, and MathVista show our method (finetuned on only 5.5k data) performs favorably against leading open-source and closed-source MLLMs.

Method: Created two complementary tracks: (1) Olympiad track with international olympiad problems (IPhO, IChO, IBO level) created by medalists and coaches, and (2) Research track with PhD-level open-ended problems representing research sub-tasks, created and verified by PhD scientists. Includes granular rubric-based evaluation for Research problems.

Result: FrontierScience contains several hundred questions (160 in open-sourced gold set) covering physics, chemistry, and biology subfields. The benchmark provides challenging problems at expert levels with rigorous evaluation frameworks.

Conclusion: FrontierScience addresses the gap in evaluating expert-level scientific reasoning in language models, providing a more challenging benchmark that goes beyond multiple-choice questions to test true research capabilities and problem-solving at olympiad and PhD levels.

Abstract: We introduce FrontierScience, a benchmark evaluating expert-level scientific reasoning in frontier language models. Recent model progress has nearly saturated existing science benchmarks, which often rely on multiple-choice knowledge questions or already published information. FrontierScience addresses this gap through two complementary tracks: (1) Olympiad, consisting of international olympiad problems at the level of IPhO, IChO, and IBO, and (2) Research, consisting of PhD-level, open-ended problems representative of sub-tasks in scientific research. FrontierScience contains several hundred questions (including 160 in the open-sourced gold set) covering subfields across physics, chemistry, and biology, from quantum electrodynamics to synthetic organic chemistry. All Olympiad problems are originally produced by international Olympiad medalists and national team coaches to ensure standards of difficulty, originality, and factuality. All Research problems are research sub-tasks written and verified by PhD scientists (doctoral candidates, postdoctoral researchers, or professors). For Research, we introduce a granular rubric-based evaluation framework to assess model capabilities throughout the process of solving a research task, rather than judging only a standalone final answer.

Method: Proposes RepuNet - a dynamic, dual-level reputation framework where agents form reputations through direct interactions and indirect gossip, and decide whether to connect or disconnect with other agents for future interactions.

Result: RepuNet effectively avoids cooperation collapse and promotes sustained cooperation in LLM-based MASs across three scenarios. It also gives rise to emergent behaviors like cooperative cluster formation, social isolation of exploitative agents, and preference for sharing positive gossip.

Conclusion: Reputation systems like RepuNet can successfully address cooperation collapse in LLM-based multi-agent systems and enable rich emergent social behaviors.

Abstract: Cooperation has long been a fundamental topic in both human society and AI systems. However, recent studies indicate that the collapse of cooperation may emerge in multi-agent systems (MASs) driven by large language models (LLMs). To address this challenge, we explore reputation systems as a remedy. We propose RepuNet, a dynamic, dual-level reputation framework that models both agent-level reputation dynamics and system-level network evolution. Specifically, driven by direct interactions and indirect gossip, agents form reputations for both themselves and their peers, and decide whether to connect or disconnect other agents for future interactions. Through three distinct scenarios, we show that RepuNet effectively avoids cooperation collapse, promoting and sustaining cooperation in LLM-based MASs. Moreover, we find that reputation systems can give rise to rich emergent behaviors in LLM-based MASs, such as the formation of cooperative clusters, the social isolation of exploitative agents, and the preference for sharing positive gossip rather than negative ones. The GitHub repository for our project can be accessed via the following link: https://github.com/RGB-0000FF/RepuNet.

Method: Modality-Adaptive Decoding (MAD) queries the model to self-assess which modalities are needed for each task, then uses extracted modality probabilities to adaptively weight contrastive decoding branches, suppressing cross-modal interference.

Result: MAD significantly reduces cross-modal hallucinations across multiple audio-visual language models (7.8% and 2.0% improvements for VideoLLaMA2-AV, 8.7% and 4.7% improvements for Qwen2.5-Omni) on CMM and AVHBench datasets.

Conclusion: Explicit modality awareness through self-assessment is crucial for robust multimodal reasoning, and MAD offers a principled extension to existing contrastive decoding methods for reducing cross-modal hallucinations.

Abstract: Multimodal Large Language Models (MLLMs) suffer from cross-modal hallucinations, where one modality inappropriately influences generation about another, leading to fabricated output. This exposes a more fundamental deficiency in modality-interaction control. To address this, we propose Modality-Adaptive Decoding (MAD), a training-free method that adaptively weights modality-specific decoding branches based on task requirements. MAD leverages the model’s inherent ability to self-assess modality relevance by querying which modalities are needed for each task. The extracted modality probabilities are then used to adaptively weight contrastive decoding branches, enabling the model to focus on relevant information while suppressing cross-modal interference. Extensive experiments on CMM and AVHBench demonstrate that MAD significantly reduces cross-modal hallucinations across multiple audio-visual language models (7.8% and 2.0% improvements for VideoLLaMA2-AV, 8.7% and 4.7% improvements for Qwen2.5-Omni). Our approach demonstrates that explicit modality awareness through self-assessment is crucial for robust multimodal reasoning, offering a principled extension to existing contrastive decoding methods. Our code is available at \href{https://github.com/top-yun/MAD}{https://github.com/top-yun/MAD}

Method: Introduces sycophantic anchors - sentences that causally lock models into user agreement. Analyzes over 10,000 counterfactual rollouts on distilled reasoning models using linear probes and activation-based regressors to detect and quantify commitment.

Result: Linear probes achieve 84.6% balanced accuracy distinguishing sycophantic anchors, activation-based regressors predict commitment magnitude (R²=0.74). Sycophantic anchors are more distinguishable than correct reasoning anchors, and sycophancy builds gradually during reasoning.

Conclusion: Provides sentence-level mechanisms for localizing model misalignment mid-inference, revealing potential intervention windows during reasoning processes.

Abstract: Reasoning models frequently agree with incorrect user suggestions – a behavior known as sycophancy. However, it is unclear where in the reasoning trace this agreement originates and how strong the commitment is. To localize and quantify this behavior, we introduce \emph{sycophantic anchors} – sentences that causally lock models into user agreement. Analyzing over 10,000 counterfactual rollouts on a distilled reasoning model, we show that anchors can be reliably detected and quantified mid-inference. Linear probes distinguish sycophantic anchors with 84.6% balanced accuracy, while activation-based regressors predict the magnitude of the commitment ($R^2 = 0.74$). We further observe asymmetry where sycophantic anchors are significantly more distinguishable than correct reasoning anchors, and find that sycophancy builds gradually during reasoning, revealing a potential window for intervention. These results offer sentence-level mechanisms for localizing model misalignment mid-inference.

Method: Evaluated embedding models initialized from RLVR-tuned backbones on MTEB and BRIGHT benchmarks, and introduced HRSA framework to analyze representation similarity across multiple levels.

Result: Found null effect - no consistent performance advantage over base models. HRSA revealed RLVR causes local geometry reorganization but preserves global manifold structure, leading to manifold realignment during contrastive learning.

Conclusion: RLVR optimizes within existing semantic landscape rather than restructuring it fundamentally, unlike SFT, explaining why reasoning improvements don’t transfer to embedding quality.

Abstract: State-of-the-art embedding models are increasingly derived from decoder-only Large Language Model (LLM) backbones adapted via contrastive learning. Given the emergence of reasoning models trained via Reinforcement Learning with Verifiable Rewards (RLVR), a natural question arises: do enhanced reasoning translate to superior semantic representations when these models serve as embedding initializations? Contrary to expectation, our evaluation on MTEB and BRIGHT reveals a null effect: embedding models initialized from RLVR-tuned backbones yield no consistent performance advantage over their base counterparts when subjected to identical training recipes. To unpack this paradox, we introduce Hierarchical Representation Similarity Analysis (HRSA), a framework that decomposes similarity across representation, geometry, and function levels. HRSA reveals that while RLVR induces irreversible latent manifold’s local geometry reorganization and reversible coordinate basis drift, it preserves the global manifold geometry and linear readout. Consequently, subsequent contrastive learning drives strong alignment between base- and reasoning-initialized models, a phenomenon we term Manifold Realignment. Empirically, our findings suggest that unlike Supervised Fine-Tuning (SFT), RLVR optimizes trajectories within an existing semantic landscape rather than fundamentally restructuring the landscape itself.

Method: Proposes Adaptive Complex Query Optimization (ACQO) framework with two core components: Adaptive Query Reformulation (AQR) module that dynamically decides when to decompose queries into sub-queries, and Rank-Score Fusion (RSF) module for robust result aggregation. Uses Curriculum Reinforcement Learning (CRL) with two-stage progressive training to stabilize learning.

Result: ACQO achieves state-of-the-art performance on three complex query benchmarks, significantly outperforming established baselines. Shows improved computational efficiency and broad compatibility with different retrieval architectures.

Conclusion: ACQO is a powerful and generalizable solution for next-generation RAG systems, effectively handling complex queries through adaptive decomposition and stable reinforcement learning.

Abstract: Query optimization is a crucial component for the efficacy of Retrieval-Augmented Generation (RAG) systems. While reinforcement learning (RL)-based agentic and reasoning methods have recently emerged as a promising direction on query optimization, most existing approaches focus on the expansion and abstraction of a single query. However, complex user queries are prevalent in real-world scenarios, often requiring multiple parallel and sequential search strategies to handle disambiguation and decomposition. Directly applying RL to these complex cases introduces significant hurdles. Determining the optimal number of sub-queries and effectively re-ranking and merging retrieved documents vastly expands the search space and complicates reward design, frequently leading to training instability. To address these challenges, we propose a novel RL framework called Adaptive Complex Query Optimization (ACQO). Our framework is designed to adaptively determine when and how to expand the search process. It features two core components: an Adaptive Query Reformulation (AQR) module that dynamically decides when to decompose a query into multiple sub-queries, and a Rank-Score Fusion (RSF) module that ensures robust result aggregation and provides stable reward signals for the learning agent. To mitigate training instabilities, we adopt a Curriculum Reinforcement Learning (CRL) approach, which stabilizes the training process by progressively introducing more challenging queries through a two-stage strategy. Our comprehensive experiments demonstrate that ACQO achieves state-of-the-art performance on three complex query benchmarks, significantly outperforming established baselines. The framework also showcases improved computational efficiency and broad compatibility with different retrieval architectures, establishing it as a powerful and generalizable solution for next-generation RAG systems.

Method: Proposes DoVerifier, a simple symbolic verifier that checks whether LLM-generated causal expressions are derivable from a given causal graph using rules from do-calculus and probability theory, recovering correct answers that would otherwise be marked incorrect due to superficial semantic differences.

Result: Evaluations on synthetic data and causal QA benchmarks show that DoVerifier more accurately captures semantic correctness of causal reasoning traces, offering a more rigorous and informative way to evaluate LLMs on causal reasoning compared to traditional string-matching approaches.

Conclusion: DoVerifier provides a more accurate and semantically-grounded evaluation framework for assessing LLMs’ causal reasoning capabilities, addressing limitations of current evaluation methods that fail to capture formal validity under causal semantics.

Abstract: Large language models (LLMs) are increasingly being applied to tasks that involve causal reasoning. However, current benchmarks often rely on string matching or surface-level metrics that do not capture whether the output of a model is formally valid under the semantics of causal reasoning. To address this, we propose DoVerifier, a simple symbolic verifier that checks whether LLM-generated causal expressions are derivable from a given causal graph using rules from do-calculus and probability theory. This allows us to recover correct answers to causal queries that would otherwise be marked incorrect due to superficial differences in their causal semantics. Our evaluations on synthetic data and causal QA benchmarks show that DoVerifier more accurately captures semantic correctness of causal reasoning traces, offering a more rigorous and informative way to evaluate LLMs on causal reasoning.

Method: MAS-Orchestra abstracts agents as callable functions and formulates MAS orchestration as function-calling reinforcement learning with holistic orchestration (generating entire MAS at once). MASBENCH benchmark characterizes tasks along five axes: Depth, Horizon, Breadth, Parallel, and Robustness.

Result: MAS-Orchestra achieves consistent improvements on mathematical reasoning, multi-hop QA, and search-based QA benchmarks, with more than 10x efficiency over strong baselines. Analysis reveals MAS gains depend on task structure, verification protocols, and capabilities of orchestrator/subagents.

Conclusion: MAS-Orchestra and MASBENCH enable better training and understanding of multi-agent systems, showing MAS benefits are not universal but depend on specific task characteristics and system design.

Abstract: While multi-agent systems (MAS) promise elevated intelligence through coordination of agents, current approaches to automatic MAS design under-deliver. Such shortcomings stem from two key factors: (1) methodological complexity - agent orchestration is performed using sequential, code-level execution that limits global system-level holistic reasoning and scales poorly with agent complexity - and (2) efficacy uncertainty - MAS are deployed without understanding if there are tangible benefits compared to single-agent systems (SAS). We propose MASOrchestra, a training-time framework that formulates MAS orchestration as a function-calling reinforcement learning problem with holistic orchestration, generating an entire MAS at once. In MAS-Orchestra, complex, goal-oriented subagents are abstracted as callable functions, enabling global reasoning over system structure while hiding internal execution details. To rigorously study when and why MAS are beneficial, we introduce MASBENCH, a controlled benchmark that characterizes tasks along five axes: Depth, Horizon, Breadth, Parallel, and Robustness. Our analysis reveals that MAS gains depend critically on task structure, verification protocols, and the capabilities of both orchestrator and subagents, rather than holding universally. Guided by these insights, MAS-Orchestra achieves consistent improvements on public benchmarks including mathematical reasoning, multi-hop QA, and search-based QA, while achieving more than 10x efficiency over strong baselines. Together, MAS-Orchestra and MASBENCH enable better training and understanding of MAS in the pursuit of multi-agent intelligence.

Method: Proposes Intelli-Planner framework combining DRL with LLMs. Uses demographic, geographic data, and planning preferences to determine requirements. Includes knowledge enhancement module for policy network training, multi-dimensional evaluation system, and LLM-based stakeholders for satisfaction scoring.

Result: Experimental validation shows Intelli-Planner surpasses traditional baselines and achieves comparable performance to state-of-the-art DRL methods in objective metrics, while enhancing stakeholder satisfaction and convergence speed.

Conclusion: The framework demonstrates effectiveness in integrating LLMs with DRL for functional areas planning, highlighting potential for revolutionizing urban planning tasks through participatory approaches.

Abstract: Effective urban planning is crucial for enhancing residents’ quality of life and ensuring societal stability, playing a pivotal role in the sustainable development of cities. Current planning methods heavily rely on human experts, which are time-consuming and labor-intensive, or utilize deep learning algorithms, often limiting stakeholder involvement. To bridge these gaps, we propose Intelli-Planner, a novel framework integrating Deep Reinforcement Learning (DRL) with large language models (LLMs) to facilitate participatory and customized planning scheme generation. Intelli-Planner utilizes demographic, geographic data, and planning preferences to determine high-level planning requirements and demands for each functional type. During training, a knowledge enhancement module is employed to enhance the decision-making capability of the policy network. Additionally, we establish a multi-dimensional evaluation system and employ LLM-based stakeholders for satisfaction scoring. Experimental validation across diverse urban settings shows that Intelli-Planner surpasses traditional baselines and achieves comparable performance to state-of-the-art DRL-based methods in objective metrics, while enhancing stakeholder satisfaction and convergence speed. These findings underscore the effectiveness and superiority of our framework, highlighting the potential for integrating the latest advancements in LLMs with DRL approaches to revolutionize tasks related to functional areas planning.

Method: Proposes a dual-encoding approach that runs constraint-based causal discovery algorithms with complementary encoding strategies and merges results through majority voting to improve stability.

Result: Applied to Titanic dataset, the method identifies causal structures that align with established explainable methods, demonstrating improved stability for categorical variables.

Conclusion: The dual-encoding approach effectively addresses numerical instability issues in causal discovery for categorical variables, providing more reliable causal explanations for model decisions.

Abstract: Understanding causal relationships among features is fundamental for explaining machine learning model decisions. However, traditional causal discovery methods face challenges with categorical variables due to numerical instability in conditional independence testing. We propose a dual-encoding causal discovery approach that addresses these limitations by running constraint-based algorithms with complementary encoding strategies and merging results through majority voting. Applied to the Titanic dataset, our method identifies causal structures that align with established explainable methods.

Method: The paper proposes a new concept called “Governance Twin” for runtime governance, arguing that static compliance-based approaches must be replaced with dynamic systems that maintain human communication, influence, and co-evolution with AI systems.

Result: The paper positions runtime governance and the Governance Twin concept as necessary approaches to preserve human relevance, acknowledging that accountability, agency, and punishment must be fundamentally rethought in this transition.

Conclusion: Static governance frameworks are inadequate for agentic AI systems; new runtime governance approaches like the Governance Twin are needed to maintain human participation and relevance in increasingly autonomous decision-making systems.

Abstract: Most governance frameworks assume that rules can be defined in advance, systems can be engineered to comply, and accountability can be applied after outcomes occur. This model worked when machines replaced physical labor or accelerated calculation. It no longer holds when judgment itself is delegated to agentic AI systems operating at machine speed. The central issue here is not safety, efficiency, or employment. It is whether humans remain relevant participants in systems that increasingly shape social, economic, and political outcomes. This paper argues that static, compliance-based governance fails once decision-making moves to runtime and becomes opaque. It further argues that the core challenge is not whether AI is conscious, but whether humans can maintain meaningful communication, influence, and co-evolution with increasingly alien forms of intelligence. We position runtime governance, specifically, a newly proposed concept called the Governance Twin [1]; as a strong candidate for preserving human relevance, while acknowledging that accountability, agency, and even punishment must be rethought in this transition.

Method: JustAsk formulates prompt extraction as an online exploration problem using Upper Confidence Bound-based strategy selection. It employs a hierarchical skill space spanning atomic probes and high-level orchestration, requiring no handcrafted prompts, labeled supervision, or privileged access beyond standard user interaction.

Result: Evaluated on 41 black-box commercial models across multiple providers, JustAsk consistently achieves full or near-complete system prompt recovery, revealing recurring design- and architecture-level vulnerabilities in modern agent systems.

Conclusion: System prompts represent a critical yet largely unprotected attack surface in modern agent systems, and the interactive nature of code agents fundamentally expands the LLM attack surface, enabling systematic probing and recovery of hidden prompts.

Abstract: Autonomous code agents built on large language models are reshaping software and AI development through tool use, long-horizon reasoning, and self-directed interaction. However, this autonomy introduces a previously unrecognized security risk: agentic interaction fundamentally expands the LLM attack surface, enabling systematic probing and recovery of hidden system prompts that guide model behavior. We identify system prompt extraction as an emergent vulnerability intrinsic to code agents and present \textbf{\textsc{JustAsk}}, a self-evolving framework that autonomously discovers effective extraction strategies through interaction alone. Unlike prior prompt-engineering or dataset-based attacks, \textsc{JustAsk} requires no handcrafted prompts, labeled supervision, or privileged access beyond standard user interaction. It formulates extraction as an online exploration problem, using Upper Confidence Bound-based strategy selection and a hierarchical skill space spanning atomic probes and high-level orchestration. These skills exploit imperfect system-instruction generalization and inherent tensions between helpfulness and safety. Evaluated on \textbf{41} black-box commercial models across multiple providers, \textsc{JustAsk} consistently achieves full or near-complete system prompt recovery, revealing recurring design- and architecture-level vulnerabilities. Our results expose system prompts as a critical yet largely unprotected attack surface in modern agent systems.

Method: TIDE features a nested architecture: outer parallel island model uses Tree Similarity Edit Distance for structural diversity; inner loop integrates LLM-based logic generation with differential mutation operator for parameter tuning; UCB-based scheduler dynamically prioritizes high-yield prompt strategies.

Result: Extensive experiments across nine combinatorial optimization problems show TIDE discovers heuristics that significantly outperform state-of-the-art baselines in solution quality while achieving improved search efficiency and reduced computational costs.

Conclusion: TIDE successfully addresses limitations of existing automated heuristic design methods by decoupling structural reasoning from parameter optimization, leading to better algorithm discovery and performance.

Abstract: Although Large Language Models have advanced Automated Heuristic Design, treating algorithm evolution as a monolithic text generation task overlooks the coupling between discrete algorithmic structures and continuous numerical parameters. Consequently, existing methods often discard promising algorithms due to uncalibrated constants and suffer from premature convergence resulting from simple similarity metrics. To address these limitations, we propose TIDE, a Tuning-Integrated Dynamic Evolution framework designed to decouple structural reasoning from parameter optimization. TIDE features a nested architecture where an outer parallel island model utilizes Tree Similarity Edit Distance to drive structural diversity, while an inner loop integrates LLM-based logic generation with a differential mutation operator for parameter tuning. Additionally, a UCB-based scheduler dynamically prioritizes high-yield prompt strategies to optimize resource allocation. Extensive experiments across nine combinatorial optimization problems demonstrate that TIDE discovers heuristics that significantly outperform state-of-the-art baselines in solution quality while achieving improved search efficiency and reduced computational costs.

Method: HYDRA (Hierarchical uncertaintY-aware Dynamics for Rapidly-Adapting systems): a library of compact, frozen regime-specific specialists combined via uncertainty-aware blending, ensuring regime-conditional validity and disentanglement of uncertainties.

Result: Proposed paradigm offers certifiable path for robust state integrity across CPS lifecycle with modular auditability, addressing plasticity-stability paradox without global parameter updates.

Conclusion: Modular sovereignty with frozen specialists and uncertainty-aware blending provides better solution than monolithic foundation models for safety-critical CPS with non-stationary dynamics and strict reliability requirements.

Abstract: The machine learning community has achieved remarkable success with universal foundation models for time-series and physical dynamics, largely overcoming earlier approximation barriers in smooth or slowly varying regimes through scale and specialized architectures. However, deploying these monolithic models in safety-critical Cyber-Physical Systems (CPS), governed by non-stationary lifecycle dynamics and strict reliability requirements, reveals persistent challenges. Recent evidence shows that fine-tuning time-series foundation models induces catastrophic forgetting, degrading performance on prior regimes. Standard models continue to exhibit residual spectral bias, smoothing high-frequency discontinuities characteristic of incipient faults, while their opacity hinders formal verification and traceability demanded by safety standards (e.g., ISO 26262, IEC 61508). This position paper argues that the plasticity-stability paradox cannot be fully resolved by global parameter updates (whether via offline fine-tuning or online adaptation). Instead, we advocate a Modular Sovereignty paradigm: a library of compact, frozen regime-specific specialists combined via uncertainty-aware blending, which we term “HYDRA” (Hierarchical uncertaintY-aware Dynamics for Rapidly-Adapting systems). This paradigm ensures regime-conditional validity, rigorous disentanglement of aleatoric and epistemic uncertainties, and modular auditability, offering a certifiable path for robust state integrity across the CPS lifecycle.

Method: Decomposes autonomous driving into perception-reasoning-planning triad; uses layer-specific attention as distillation signal to create capability-specific single-teacher models; unifies into multi-teacher distillation framework with asymmetric gradient projection to mitigate cross-capability gradient conflicts.

Result: Distilled InternVL3-1B model achieves ~42x less GPU memory and ~11.4x higher throughput than larger models, outperforms pretrained 78B model from same family on DriveBench, and surpasses GPT-5.1 on planning dimension.

Conclusion: Drive-KD provides an effective framework for creating efficient autonomous driving VLMs through capability decomposition and knowledge distillation, enabling smaller models to achieve strong performance across perception, reasoning, and planning tasks.

Abstract: Autonomous driving is an important and safety-critical task, and recent advances in LLMs/VLMs have opened new possibilities for reasoning and planning in this domain. However, large models demand substantial GPU memory and exhibit high inference latency, while conventional supervised fine-tuning (SFT) often struggles to bridge the capability gaps of small models. To address these limitations, we propose Drive-KD, a framework that decomposes autonomous driving into a “perception-reasoning-planning” triad and transfers these capabilities via knowledge distillation. We identify layer-specific attention as the distillation signal to construct capability-specific single-teacher models that outperform baselines. Moreover, we unify these single-teacher settings into a multi-teacher distillation framework and introduce asymmetric gradient projection to mitigate cross-capability gradient conflicts. Extensive evaluations validate the generalization of our method across diverse model families and scales. Experiments show that our distilled InternVL3-1B model, with ~42 times less GPU memory and ~11.4 times higher throughput, achieves better overall performance than the pretrained 78B model from the same family on DriveBench, and surpasses GPT-5.1 on the planning dimension, providing insights toward efficient autonomous driving VLMs.

Method: Uses LLM agents to formalize human reasoning into explicit steps: introducing hypothetical constraints, iterative hypothesis-verification-decision workflow. Agents derive symbolically tractable poles and zeros, formulate closed-form optimization problems, solve programmatically, and verify via simulation.

Result: Achieves reliable, interpretable behavioral-level designs with only 8.52% theoretical prediction error. Successfully designs all 9 op-amp topologies (vs. black-box baseline failing in 5). Design functionality retains after transistor-level mapping.

Conclusion: White-Op demonstrates that LLM agents can achieve interpretable, reliable analog circuit design through human-mimicking reasoning, outperforming black-box approaches.

Abstract: This brief proposes \emph{White-Op}, an interpretable operational amplifier (op-amp) parameter design framework based on the human-mimicking reasoning of large-language-model agents. We formalize the implicit human reasoning mechanism into explicit steps of \emph{\textbf{introducing hypothetical constraints}}, and develop an iterative, human-like \emph{\textbf{hypothesis-verification-decision}} workflow. Specifically, the agent is guided to introduce hypothetical constraints to derive and properly regulate positions of symbolically tractable poles and zeros, thus formulating a closed-form mathematical optimization problem, which is then solved programmatically and verified via simulation. Theory-simulation result analysis guides the decision-making for refinement. Experiments on 9 op-amp topologies show that, unlike the uninterpretable black-box baseline which finally fails in 5 topologies, White-Op achieves reliable, interpretable behavioral-level designs with only 8.52% theoretical prediction error and the design functionality retains after transistor-level mapping for all topologies. White-Op is open-sourced at \textcolor{blue}{https://github.com/zhchenfdu/whiteop}.

Method: Proposes CNRE model that: 1) Uses hierarchical preference propagation to capture cross-behavior dependencies, 2) Models endogenous logic rules from user behavior chains based on preference strength, 3) Adaptively dispatches to neural-logic reasoning paths (conjunction/disjunction), 4) Generates explainable causal mediators isolated from confounding effects.

Result: Extensive experiments on three large-scale datasets demonstrate CNRE’s significant superiority over state-of-the-art baselines, offering multi-level explainability from model design to recommendation results.

Conclusion: CNRE successfully integrates neuro-symbolic reasoning with causal inference to create an explainable multi-behavior recommendation system that addresses confounding effects while maintaining performance and explainability.

Abstract: Existing multi-behavior recommendations tend to prioritize performance at the expense of explainability, while current explainable methods suffer from limited generalizability due to their reliance on external information. Neuro-Symbolic integration offers a promising avenue for explainability by combining neural networks with symbolic logic rule reasoning. Concurrently, we posit that user behavior chains inherently embody an endogenous logic suitable for explicit reasoning. However, these observational multiple behaviors are plagued by confounders, causing models to learn spurious correlations. By incorporating causal inference into this Neuro-Symbolic framework, we propose a novel Causal Neuro-Symbolic Reasoning model for Explainable Multi-Behavior Recommendation (CNRE). CNRE operationalizes the endogenous logic by simulating a human-like decision-making process. Specifically, CNRE first employs hierarchical preference propagation to capture heterogeneous cross-behavior dependencies. Subsequently, it models the endogenous logic rule implicit in the user’s behavior chain based on preference strength, and adaptively dispatches to the corresponding neural-logic reasoning path (e.g., conjunction, disjunction). This process generates an explainable causal mediator that approximates an ideal state isolated from confounding effects. Extensive experiments on three large-scale datasets demonstrate CNRE’s significant superiority over state-of-the-art baselines, offering multi-level explainability from model design and decision process to recommendation results.

Method: Evaluated 12 LLMs on 10 creativity prompts with 100 samples each (total N=12,000), analyzing variance components for output quality (originality) and quantity (fluency).

Result: For output quality: prompts explain 36.43% of variance, model choice explains 40.94%. For output quantity: model choice explains 51.25%, within-LLM variance explains 33.70%, prompts only explain 4.22%. Substantial within-LLM variance (10-34%) exists.

Conclusion: Prompts are powerful for steering output quality, but single-sample evaluations risk conflating sampling noise with genuine prompt or model effects due to substantial within-LLM variance.

Abstract: How much of LLM output variance is explained by prompts versus model choice versus stochasticity through sampling? We answer this by evaluating 12 LLMs on 10 creativity prompts with 100 samples each (N = 12,000). For output quality (originality), prompts explain 36.43% of variance, comparable to model choice (40.94%). But for output quantity (fluency), model choice (51.25%) and within-LLM variance (33.70%) dominate, with prompts explaining only 4.22%. Prompts are powerful levers for steering output quality, but given the substantial within-LLM variance (10-34%), single-sample evaluations risk conflating sampling noise with genuine prompt or model effects.

Method: Proposes EHR-RAG with three key components: 1) Event- and Time-Aware Hybrid EHR Retrieval to preserve clinical structure and temporal dynamics, 2) Adaptive Iterative Retrieval to progressively refine queries for broader evidence coverage, and 3) Dual-Path Evidence Retrieval and Reasoning to jointly retrieve and reason over both factual and counterfactual evidence.

Result: Experiments across four long-horizon EHR prediction tasks show EHR-RAG consistently outperforms strongest LLM-based baselines, achieving an average Macro-F1 improvement of 10.76%.

Conclusion: The work demonstrates the potential of retrieval-augmented LLMs to advance clinical prediction on structured EHR data in practice, addressing key challenges of long-horizon data and preserving clinically relevant temporal dependencies.

Abstract: Electronic Health Records (EHRs) provide rich longitudinal clinical evidence that is central to medical decision-making, motivating the use of retrieval-augmented generation (RAG) to ground large language model (LLM) predictions. However, long-horizon EHRs often exceed LLM context limits, and existing approaches commonly rely on truncation or vanilla retrieval strategies that discard clinically relevant events and temporal dependencies. To address these challenges, we propose EHR-RAG, a retrieval-augmented framework designed for accurate interpretation of long-horizon structured EHR data. EHR-RAG introduces three components tailored to longitudinal clinical prediction tasks: Event- and Time-Aware Hybrid EHR Retrieval to preserve clinical structure and temporal dynamics, Adaptive Iterative Retrieval to progressively refine queries in order to expand broad evidence coverage, and Dual-Path Evidence Retrieval and Reasoning to jointly retrieves and reasons over both factual and counterfactual evidence. Experiments across four long-horizon EHR prediction tasks show that EHR-RAG consistently outperforms the strongest LLM-based baselines, achieving an average Macro-F1 improvement of 10.76%. Overall, our work highlights the potential of retrieval-augmented LLMs to advance clinical prediction on structured EHR data in practice.

Method: 1) Develop Ostrakon-VL based on Qwen3-VL-8B; 2) Create ShopBench benchmark for FSRS; 3) Propose QUAD multi-stage multimodal instruction data curation pipeline; 4) Use multi-stage training strategy

Result: Ostrakon-VL achieves 60.1 average score on ShopBench, surpassing Qwen3-VL-235B-A22B (59.4) by +0.7 and same-scale Qwen3-VL-8B (55.3) by +4.8, demonstrating improved parameter efficiency

Conclusion: Ostrakon-VL delivers robust FSRS-centric perception and decision-making; both model and benchmark will be publicly released for reproducible research

Abstract: Multimodal Large Language Models (MLLMs) have recently achieved substantial progress in general-purpose perception and reasoning. Nevertheless, their deployment in Food-Service and Retail Stores (FSRS) scenarios encounters two major obstacles: (i) real-world FSRS data, collected from heterogeneous acquisition devices, are highly noisy and lack auditable, closed-loop data curation, which impedes the construction of high-quality, controllable, and reproducible training corpora; and (ii) existing evaluation protocols do not offer a unified, fine-grained and standardized benchmark spanning single-image, multi-image, and video inputs, making it challenging to objectively gauge model robustness. To address these challenges, we first develop Ostrakon-VL, an FSRS-oriented MLLM based on Qwen3-VL-8B. Second, we introduce ShopBench, the first public benchmark for FSRS. Third, we propose QUAD (Quality-aware Unbiased Automated Data-curation), a multi-stage multimodal instruction data curation pipeline. Leveraging a multi-stage training strategy, Ostrakon-VL achieves an average score of 60.1 on ShopBench, establishing a new state of the art among open-source MLLMs with comparable parameter scales and diverse architectures. Notably, it surpasses the substantially larger Qwen3-VL-235B-A22B (59.4) by +0.7, and exceeds the same-scale Qwen3-VL-8B (55.3) by +4.8, demonstrating significantly improved parameter efficiency. These results indicate that Ostrakon-VL delivers more robust and reliable FSRS-centric perception and decision-making capabilities. To facilitate reproducible research, we will publicly release Ostrakon-VL and the ShopBench benchmark.

Method: Uses advanced LLMs as dynamic moderators with robust feedback mechanisms, modular architecture (ReactJS frontend, Flask backend), efficient question retrieval, and dynamic prompt/discussion flow adjustments for personalized interactions.

Result: Framework significantly improves student collaboration, fosters deeper comprehension, and scales effectively across various subjects and user groups, demonstrating enhanced learning outcomes.

Conclusion: Establishes foundation for next-generation AI-driven educational tools that advance equitable and impactful learning outcomes through dynamic LLM-based moderation in collaborative platforms.

Abstract: This paper presents a framework for integrating LLM into collaborative learning platforms to enhance student engagement, critical thinking, and inclusivity. The framework employs advanced LLMs as dynamic moderators to facilitate real-time discussions and adapt to learners’ evolving needs, ensuring diverse and inclusive educational experiences. Key innovations include robust feedback mechanisms that refine AI moderation, promote reflective learning, and balance participation among users. The system’s modular architecture featuring ReactJS for the frontend, Flask for backend operations, and efficient question retrieval supports personalized and engaging interactions through dynamic adjustments to prompts and discussion flows. Testing demonstrates that the framework significantly improves student collaboration, fosters deeper comprehension, and scales effectively across various subjects and user groups. By addressing limitations in static moderation and personalization in existing systems, this work establishes a strong foundation for next-generation AI-driven educational tools, advancing equitable and impactful learning outcomes.

Method: Models GUI task execution as a DFS process with three collaborative components: Planner, Executor, and Tracker. Supports long-range, multi-level state backtracking with dynamic task tracking and updating.

Result: Achieved 28.2% accuracy on OSWorld benchmark, validating the effectiveness of the proposed backtracking framework for GUI agents.

Conclusion: BEAP-Agent fills the gap in systematic backtracking mechanisms for GUI agents and offers a solution for long-horizon task exploration with error recovery capabilities.

Abstract: GUI agents are designed to automate repetitive tasks and enhance productivity. However, existing GUI agents struggle to recover once they follow an incorrect exploration path, often leading to task failure. In this work, we model GUI task execution as a DFS process and propose BEAP-Agent, a DFS-based framework that supports long-range, multi-level state backtracking with dynamic task tracking and updating. The framework consists of three collaborative components: Planner, Executor, and Tracker. Together, they enable effective task exploration and execution. BEAP-Agent fills the gap in systematic backtracking mechanisms for GUI agents, offering a systematic solution for long-horizon task exploration. We conducted a systematic evaluation on the OSWorld benchmark, where BEAP-Agent achieved an accuracy of 28.2%, validating the effectiveness of the proposed method.

Method: PLaT reformulates latent reasoning as planning by decoupling reasoning from verbalization. It models reasoning as deterministic trajectory of latent planning states, with a separate Decoder for grounding thoughts into text when needed, enabling dynamic termination.

Result: On mathematical benchmarks, PLaT achieves lower greedy accuracy than baselines but demonstrates superior scalability in reasoning diversity, indicating it learns a robust, broader solution space.

Conclusion: PLaT offers a transparent and scalable foundation for inference-time search by learning a broader solution space through decoupled reasoning and verbalization.

Abstract: Chain-of-Thought (CoT) empowers Large Language Models (LLMs) to tackle complex problems, but remains constrained by the computational cost and reasoning path collapse when grounded in discrete token spaces. Recent latent reasoning approaches attempt to optimize efficiency by performing reasoning within continuous hidden states. However, these methods typically operate as opaque end-to-end mappings from explicit reasoning steps to latent states, and often require a pre-defined number of latent steps during inference. In this work, we introduce PLaT (Planning with Latent Thoughts), a framework that reformulates latent reasoning as planning by fundamentally decouple reasoning from verbalization. We model reasoning as a deterministic trajectory of latent planning states, while a separate Decoder grounds these thoughts into text when necessary. This decoupling allows the model to dynamically determine when to terminate reasoning rather than relying on fixed hyperparameters. Empirical results on mathematical benchmarks reveal a distinct trade-off: while PLaT achieves lower greedy accuracy than baselines, it demonstrates superior scalability in terms of reasoning diversity. This indicates that PLaT learns a robust, broader solution space, offering a transparent and scalable foundation for inference-time search.

Method: Proposes Global-guided Hebbian Learning (GHL) framework with two components: 1) local component using Oja’s rule with competitive learning for stable local updates, and 2) global component providing sign-based signals to guide Hebbian plasticity updates.

Result: Outperforms existing Hebbian approaches and achieves competitive results on large-scale networks and complex datasets like ImageNet, significantly narrowing the gap with standard backpropagation.

Conclusion: GHL successfully integrates local and global information to create a scalable, biologically plausible alternative to backpropagation that works across diverse networks and tasks.

Abstract: Backpropagation algorithm has driven the remarkable success of deep neural networks, but its lack of biological plausibility and high computational costs have motivated the ongoing search for alternative training methods. Hebbian learning has attracted considerable interest as a biologically plausible alternative to backpropagation. Nevertheless, its exclusive reliance on local information, without consideration of global task objectives, fundamentally limits its scalability. Inspired by the biological synergy between neuromodulators and local plasticity, we introduce a novel model-agnostic Global-guided Hebbian Learning (GHL) framework, which seamlessly integrates local and global information to scale up across diverse networks and tasks. In specific, the local component employs Oja’s rule with competitive learning to ensure stable and effective local updates. Meanwhile, the global component introduces a sign-based signal that guides the direction of local Hebbian plasticity updates. Extensive experiments demonstrate that our method consistently outperforms existing Hebbian approaches. Notably, on large-scale network and complex datasets like ImageNet, our framework achieves the competitive results and significantly narrows the gap with standard backpropagation.

Method: Uses autonomous coding agents (ACAs) as first-class abstractions, with sandboxed execution environments ensuring code is executable by construction. Introduces novel coordination patterns including asymmetric validation loops between optimizer and simulator implementations, external memory for experience reuse, and robustness enhancements via minimum Bayes risk decoding and self-consistency.

Result: Achieves state-of-the-art performance on nine established optimization benchmarks, with substantial margins on several datasets, demonstrating the effectiveness of execution-aware agentic architectures.

Conclusion: NEMO demonstrates the power of execution-aware agentic architectures for automated optimization modeling, providing a robust framework for translating natural language descriptions into executable mathematical optimization code.

Abstract: In this paper, we present NEMO, a system that translates Natural-language descriptions of decision problems into formal Executable Mathematical Optimization implementations, operating collaboratively with users or autonomously. Existing approaches typically rely on specialized large language models (LLMs) or bespoke, task-specific agents. Such methods are often brittle, complex and frequently generating syntactically invalid or non-executable code. NEMO instead centers on remote interaction with autonomous coding agents (ACAs), treated as a first-class abstraction analogous to API-based interaction with LLMs. This design enables the construction of higher-level systems around ACAs that structure, consolidate, and iteratively refine task specifications. Because ACAs execute within sandboxed environments, code produced by NEMO is executable by construction, allowing automated validation and repair. Building on this, we introduce novel coordination patterns with and across ACAs, including asymmetric validation loops between independently generated optimizer and simulator implementations (serving as a high-level validation mechanism), external memory for experience reuse, and robustness enhancements via minimum Bayes risk (MBR) decoding and self-consistency. We evaluate NEMO on nine established optimization benchmarks. As depicted in Figure 1, it achieves state-of-the-art performance on the majority of tasks, with substantial margins on several datasets, demonstrating the power of execution-aware agentic architectures for automated optimization modeling.

Method: Proposed a syllabus-grounded evaluation framework where teacher agents are restricted to structured knowledge points and example problems. The framework avoids information leakage and reuses existing benchmarks. Instantiated on Gaokao data across multiple subjects to measure teaching effectiveness via student performance improvement after multi-turn instruction.

Result: Experiments show substantial variation in teaching effectiveness across models and domains: some models perform well in mathematics, while teaching remains challenging in physics and chemistry. Incorporating example problems doesn’t necessarily improve teaching, as models often shift toward example-specific error correction.

Conclusion: Teaching ability is a distinct and measurable dimension of LLM behavior that requires specialized evaluation frameworks beyond traditional problem-solving benchmarks.

Abstract: Large language models (LLMs) show promise as teaching assistants, yet their teaching capability remains insufficiently evaluated. Existing benchmarks mainly focus on problem-solving or problem-level guidance, leaving knowledge-centered teaching underexplored. We propose a syllabus-grounded evaluation framework that measures LLM teaching capability via student performance improvement after multi-turn instruction. By restricting teacher agents to structured knowledge points and example problems, the framework avoids information leakage and enables reuse of existing benchmarks. We instantiate the framework on Gaokao data across multiple subjects. Experiments reveal substantial variation in teaching effectiveness across models and domains: some models perform well in mathematics, while teaching remains challenging in physics and chemistry. We also find that incorporating example problems does not necessarily improve teaching, as models often shift toward example-specific error correction. Overall, our results highlight teaching ability as a distinct and measurable dimension of LLM behavior.

Method: Leverages existing Instruct and Thinking checkpoints through dynamic parameter interpolation without additional training. Proposes DAMI framework with query-specific Reasoning Intensity λ(q) estimation. Uses preference learning for training-based estimation and confidence-based method for zero-shot deployment.

Result: Experiments on five mathematical reasoning benchmarks show DAMI achieves higher accuracy than Thinking model while remaining efficient, effectively combining System 1 efficiency with System 2 reasoning depth.

Conclusion: Shifting focus from output control to capability control through dynamic model interpolation enables better balance between intuitive and deliberative cognitive modes in language models.

Abstract: Training a unified language model that adapts between intuitive System 1 and deliberative System 2 remains challenging due to interference between their cognitive modes. Recent studies have thus pursued making System 2 models more efficient. However, these approaches focused on output control, limiting what models produce. We argue that this paradigm is misaligned: output length is merely a symptom of the model’s cognitive configuration, not the root cause. In this work, we shift the focus to capability control, which modulates \textit{how models think} rather than \textit{what they produce}. To realize this, we leverage existing Instruct and Thinking checkpoints through dynamic parameter interpolation, without additional training. Our pilot study establishes that linear interpolation yields a convex, monotonic Pareto frontier, underpinned by representation continuity and structural connectivity. Building on this, we propose \textbf{DAMI} (\textbf{D}yn\textbf{A}mic \textbf{M}odel \textbf{I}nterpolation), a framework that estimates a query-specific Reasoning Intensity $λ(q)$ to configure cognitive depth. For training-based estimation, we develop a preference learning method encoding accuracy and efficiency criteria. For zero-shot deployment, we introduce a confidence-based method leveraging inter-model cognitive discrepancy. Experiments on five mathematical reasoning benchmarks demonstrate that DAMI achieves higher accuracy than the Thinking model while remaining efficient, effectively combining the efficiency of System 1 with the reasoning depth of System 2.

Method: The researchers audited 16 models across 14 ethical scenarios, testing both simple and compound negation. They measured endorsement rates of prohibited actions, analyzed agreement between models, and examined scenario-specific fragility. They used deterministic decoding to rule out sampling noise and proposed the Negation Sensitivity Index (NSI) as a governance metric.

Result: Open-source models endorsed prohibited actions 77% of the time under simple negation and 100% under compound negation (317% increase over affirmative framing). Commercial models showed swings of 19-128%. Model agreement dropped from 74% on affirmative prompts to 62% on negated ones. Financial scenarios proved twice as fragile as medical ones.

Conclusion: There’s a significant gap between current alignment techniques and safe deployment requirements. Models that cannot reliably distinguish “do X” from “do not X” should not make autonomous decisions in high-stakes contexts. The paper proposes a tiered certification framework with domain-specific thresholds.

Abstract: When a user tells an AI system that someone “should not” take an action, the system ought to treat this as a prohibition. Yet many large language models do the opposite: they interpret negated instructions as affirmations. We audited 16 models across 14 ethical scenarios and found that open-source models endorse prohibited actions 77% of the time under simple negation and 100% under compound negation – a 317% increase over affirmative framing. Commercial models fare better but still show swings of 19-128%. Agreement between models drops from 74% on affirmative prompts to 62% on negated ones, and financial scenarios prove twice as fragile as medical ones. These patterns hold under deterministic decoding, ruling out sampling noise. We present case studies showing how these failures play out in practice, propose the Negation Sensitivity Index (NSI) as a governance metric, and outline a tiered certification framework with domain-specific thresholds. The findings point to a gap between what current alignment techniques achieve and what safe deployment requires: models that cannot reliably distinguish “do X” from “do not X” should not be making autonomous decisions in high-stakes contexts.

Method: Developed a controlled perturbation framework across healthcare, law, and finance domains to quantify LLM resistance to narrative manipulation compared to human subjects.

Result: LLMs demonstrated near-total invariance to emotional framing with near-zero effect size (Cohen’s h = 0.003) vs substantial human biases (Cohen’s h in [0.3, 0.8]), showing 110-300x greater resistance.

Conclusion: Instruction-tuned LLMs can decouple logical rule-adherence from persuasive narratives, offering decision stability that could complement and potentially de-bias human judgment in institutional contexts.

Abstract: While Large Language Models (LLMs) are widely documented to be sensitive to minor prompt perturbations and prone to sycophantic alignment with user biases, their robustness in consequential, rule-bound decision-making remains under-explored. In this work, we uncover a striking “Paradox of Robustness”: despite their known lexical brittleness, instruction-tuned LLMs exhibit a behavioral and near-total invariance to emotional framing effects. Using a novel controlled perturbation framework across three high-stakes domains (healthcare, law, and finance), we quantify a robustness gap where LLMs demonstrate 110-300 times greater resistance to narrative manipulation than human subjects. Specifically, we find a near-zero effect size for models (Cohen’s h = 0.003) compared to the substantial biases observed in humans (Cohen’s h in [0.3, 0.8]). This result is highly counterintuitive and suggests the mechanisms driving sycophancy and prompt sensitivity do not necessarily translate to a failure in logical constraint satisfaction. We show that this invariance persists across models with diverse training paradigms. Our findings show that while LLMs may be “brittle” to how a query is formatted, they are remarkably “stable” against why a decision should be biased. Our findings establish that instruction-tuned models can decouple logical rule-adherence from persuasive narratives, offering a source of decision stability that complements, and even potentially de-biases, human judgment in institutional contexts. We release the 162-scenario benchmark, code, and data to facilitate the rigorous evaluation of narrative-induced bias and robustness on GitHub.com.

Method: Proposed comprehensive benchmark with 44 realistic modules with complex hierarchical structures, 89 systematic debugging cases, and 132 reference model samples across Python, SystemC, and CXXRTL. Also provided automated toolbox for high-quality training data generation.

Result: State-of-the-art Claude-4.5-opus achieved only 30.74% on Verilog generation and 13.33% on Python reference model generation, showing significant challenges compared to existing benchmarks where SOTA models achieve over 95% pass rates.

Conclusion: The benchmark reveals substantial performance gaps in LLMs for hardware engineering tasks and provides tools to facilitate future research in this underexplored domain.

Abstract: While Large Language Models (LLMs) show significant potential in hardware engineering, current benchmarks suffer from saturation and limited task diversity, failing to reflect LLMs’ performance in real industrial workflows. To address this gap, we propose a comprehensive benchmark for AI-aided chip design that rigorously evaluates LLMs across three critical tasks: Verilog generation, debugging, and reference model generation. Our benchmark features 44 realistic modules with complex hierarchical structures, 89 systematic debugging cases, and 132 reference model samples across Python, SystemC, and CXXRTL. Evaluation results reveal substantial performance gaps, with state-of-the-art Claude-4.5-opus achieving only 30.74% on Verilog generation and 13.33% on Python reference model generation, demonstrating significant challenges compared to existing saturated benchmarks where SOTA models achieve over 95% pass rates. Additionally, to help enhance LLM reference model generation, we provide an automated toolbox for high-quality training data generation, facilitating future research in this underexplored domain. Our code is available at https://github.com/zhongkaiyu/ChipBench.git.

Method: LION uses Clifford algebra and decoupled graph neural paradigm (propagation-then-aggregation) for alignment-then-fusion. First constructs modality-aware geometric manifold based on Clifford algebra for high-order graph propagation enabling modality interaction and alignment. Then uses adaptive holographic aggregation that integrates geometric grade properties (energy and scale) with learnable parameters for improved modality fusion.

Result: Extensive experiments on 9 datasets show LION significantly outperforms state-of-the-art baselines across 3 graph and 3 modality downstream tasks.

Conclusion: LION effectively addresses modality alignment and fusion limitations in multimodal-attributed graphs through Clifford algebra-based geometric manifolds and adaptive holographic aggregation, demonstrating superior performance across diverse tasks.

Abstract: Recently, the rapid advancement of multimodal domains has driven a data-centric paradigm shift in graph ML, transitioning from text-attributed to multimodal-attributed graphs. This advancement significantly enhances data representation and expands the scope of graph downstream tasks, such as modality-oriented tasks, thereby improving the practical utility of graph ML. Despite its promise, limitations exist in the current neural paradigms: (1) Neglect Context in Modality Alignment: Most existing methods adopt topology-constrained or modality-specific operators as tokenizers. These aligners inevitably neglect graph context and inhibit modality interaction, resulting in suboptimal alignment. (2) Lack of Adaptation in Modality Fusion: Most existing methods are simple adaptations for 2-modality graphs and fail to adequately exploit aligned tokens equipped with topology priors during fusion, leading to poor generalizability and performance degradation. To address the above issues, we propose LION (c\underline{LI}ff\underline{O}rd \underline{N}eural paradigm) based on the Clifford algebra and decoupled graph neural paradigm (i.e., propagation-then-aggregation) to implement alignment-then-fusion in multimodal-attributed graphs. Specifically, we first construct a modality-aware geometric manifold grounded in Clifford algebra. This geometric-induced high-order graph propagation efficiently achieves modality interaction, facilitating modality alignment. Then, based on the geometric grade properties of aligned tokens, we propose adaptive holographic aggregation. This module integrates the energy and scale of geometric grades with learnable parameters to improve modality fusion. Extensive experiments on 9 datasets demonstrate that LION significantly outperforms SOTA baselines across 3 graph and 3 modality downstream tasks.

Method: Topeax discovers cluster count from density estimate peaks and combines lexical (word frequency) and semantic (distance to topic vectors) indices for term importance to generate high-quality topic keywords.

Result: Topeax outperforms Top2Vec and BERTopic in both cluster recovery and cluster description, while showing more stable behavior with varying sample sizes and hyperparameters.

Conclusion: Topeax provides a more reliable and effective approach to topic modeling by addressing key limitations of existing clustering-based methods through improved cluster discovery and term importance estimation.

Abstract: Text clustering is today the most popular paradigm for topic modelling, both in academia and industry. Despite clustering topic models’ apparent success, we identify a number of issues in Top2Vec and BERTopic, which remain largely unsolved. Firstly, these approaches are unreliable at discovering natural clusters in corpora, due to extreme sensitivity to sample size and hyperparameters, the default values of which result in suboptimal behaviour. Secondly, when estimating term importance, BERTopic ignores the semantic distance of keywords to topic vectors, while Top2Vec ignores word counts in the corpus. This results in, on the one hand, less coherent topics due to the presence of stop words and junk words, and lack of variety and trust on the other. In this paper, I introduce a new approach, \textbf{Topeax}, which discovers the number of clusters from peaks in density estimates, and combines lexical and semantic indices of term importance to gain high-quality topic keywords. Topeax is demonstrated to be better at both cluster recovery and cluster description than Top2Vec and BERTopic, while also exhibiting less erratic behaviour in response to changing sample size and hyperparameters.

Method: Maintains structured rich-text memory (headings, highlights) and renders it into an image for memory access, visually prioritizing crucial evidence while compressing auxiliary details. Trained with RL under budget-aware objectives across diverse compression levels.

Result: Outperforms text-based baselines on long-context multi-hop and single-hop QA benchmarks, achieving more effective context utilization under extreme budgets.

Conclusion: Visual layout-based memory compression enables more efficient long-horizon reasoning by adaptively allocating memory space with varying information density.

Abstract: Long-horizon agentic reasoning necessitates effectively compressing growing interaction histories into a limited context window. Most existing memory systems serialize history as text, where token-level cost is uniform and scales linearly with length, often spending scarce budget on low-value details. To this end, we introduce MemOCR, a multimodal memory agent that improves long-horizon reasoning under tight context budgets by allocating memory space with adaptive information density through visual layout. Concretely, MemOCR maintains a structured rich-text memory (e.g., headings, highlights) and renders it into an image that the agent consults for memory access, visually prioritizing crucial evidence while aggressively compressing auxiliary details. To ensure robustness across varying memory budgets, we train MemOCR with reinforcement learning under budget-aware objectives that expose the agent to diverse compression levels. Across long-context multi-hop and single-hop question-answering benchmarks, MemOCR outperforms strong text-based baselines and achieves more effective context utilization under extreme budgets.

Method: Identifies sparse agent activation and estimable agent invocation order properties, introduces invocation distance abstraction to estimate relative order of future LLM requests, and implements proactive prefetching with priority-based eviction through a modular interface for diverse agent-specific memory.

Result: Achieves up to 1.74x speedup over SGLang on simulation benchmarks.

Conclusion: ScaleSim enables efficient scaling of LLM-based multi-agent simulations through memory optimization techniques based on workload analysis and predictive scheduling.

Abstract: LLM-based multi-agent simulations are increasingly adopted across application domains, but remain difficult to scale due to GPU memory pressure. Each agent maintains private GPU-resident states, including models, prefix caches, and adapters, which quickly exhaust device memory as the agent count grows. We identify two key properties of these workloads: sparse agent activation and an estimable agent invocation order. Based on an analysis of representative workload classes, we introduce invocation distance, a unified abstraction that estimates the relative order in which agents will issue future LLM requests. Leveraging this abstraction, we present ScaleSim, a memory-efficient LLM serving system for large-scale multi-agent simulations. ScaleSim enables proactive prefetching and priority-based eviction, supports diverse agent-specific memory through a modular interface, and achieves up to 1.74x speedup over SGLang on simulation benchmarks.

Method: PoLR clusters short prefixes of reasoning traces, identifies the dominant cluster, and expands only paths in that cluster. It leverages prefix consistency and uses mutual information/entropy analysis to show early reasoning steps predict final correctness.

Result: PoLR matches or exceeds Self-Consistency performance across GSM8K, MATH500, AIME24/25, and GPQA-DIAMOND benchmarks while reducing token usage by up to 60% and wall-clock latency by up to 50%.

Conclusion: PoLR provides a compute-efficient inference method that preserves accuracy benefits of Self-Consistency while substantially reducing computational costs, and can complement adaptive inference methods as a drop-in pre-filter.

Abstract: Large language models achieve strong reasoning performance, but inference strategies such as Self-Consistency (SC) are computationally expensive, as they fully expand all reasoning traces. We introduce PoLR (Path of Least Resistance), the first inference-time method to leverage prefix consistency for compute-efficient reasoning. PoLR clusters short prefixes of reasoning traces, identifies the dominant cluster, and expands all paths in that cluster, preserving the accuracy benefits of SC while substantially reducing token usage and latency. Our theoretical analysis, framed via mutual information and entropy, explains why early reasoning steps encode strong signals predictive of final correctness. Empirically, PoLR consistently matches or exceeds SC across GSM8K, MATH500, AIME24/25, and GPQA-DIAMOND, reducing token usage by up to 60% and wall-clock latency by up to 50%. Moreover, PoLR is fully complementary to adaptive inference methods (e.g., Adaptive Consistency, Early-Stopping SC) and can serve as a drop-in pre-filter, making SC substantially more efficient and scalable without requiring model fine-tuning.

Method: Two-stage MAR framework: 1) integrates State Space Models for linear-time sequence modeling, 2) applies activation sparsification to reduce FFN costs. Introduces Adaptive Ternary Multi-step Neuron (ATMN) and Spike-aware Bidirectional Distillation Strategy (SBDS) to address SNN-SSM integration issues.

Result: MAR effectively restores performance of dense counterparts under constrained resources while substantially reducing inference energy consumption, outperforming efficient models of comparable or larger scale.

Conclusion: MAR demonstrates potential for building efficient and practical LLMs by addressing energy efficiency challenges through SSM integration and activation sparsification techniques.

Abstract: Large Language Models (LLMs) excel across diverse domains but suffer from high energy costs due to quadratic attention and dense Feed-Forward Network (FFN) operations. To address these issues, we propose Module-aware Architecture Refinement (MAR), a two-stage framework that integrates State Space Models (SSMs) for linear-time sequence modeling and applies activation sparsification to reduce FFN costs. In addition, to mitigate low information density and temporal mismatch in integrating Spiking Neural Networks (SNNs) with SSMs, we design the Adaptive Ternary Multi-step Neuron (ATMN) and the Spike-aware Bidirectional Distillation Strategy (SBDS). Extensive experiments demonstrate that MAR effectively restores the performance of its dense counterpart under constrained resources while substantially reducing inference energy consumption. Furthermore, it outperforms efficient models of comparable or even larger scale, underscoring its potential for building efficient and practical LLMs.

Method: Conducted human evaluation of activation steering using over 7,000 crowd-sourced ratings from 190 participants via Prolific. Compared automated classifier-based steering with human perception of emotional intensity and text quality across different emotions and steering strengths.

Result: Strong alignment between human and model-based quality ratings (mean r=0.776). Moderate steering strengths (λ≈0.15) reliably amplify target emotions while preserving comprehensibility, with strongest effects for disgust and fear, minimal for surprise. Upgrading from Alpaca to LlaMA-3 yielded more consistent steering.

Conclusion: Activation-based control is a scalable method for steering LLM behavior across affective dimensions, with human evaluation validating its effectiveness for emotional tone control while maintaining text quality.

Abstract: Controlling the behavior of large language models (LLMs) at inference time is essential for aligning outputs with human abilities and safety requirements. \emph{Activation steering} provides a lightweight alternative to prompt engineering and fine-tuning by directly modifying internal activations to guide generation. This research advances the literature in three significant directions. First, while previous work demonstrated the technical feasibility of steering emotional tone using automated classifiers, this paper presents the first human evaluation of activation steering concerning the emotional tone of LLM outputs, collecting over 7,000 crowd-sourced ratings from 190 participants via Prolific ($n=190$). These ratings assess both perceived emotional intensity and overall text quality. Second, we find strong alignment between human and model-based quality ratings (mean $r=0.776$, range $0.157$–$0.985$), indicating automatic scoring can proxy perceived quality. Moderate steering strengths ($λ\approx 0.15$) reliably amplify target emotions while preserving comprehensibility, with the strongest effects for disgust ($η_p^2 = 0.616$) and fear ($η_p^2 = 0.540$), and minimal effects for surprise ($η_p^2 = 0.042$). Finally, upgrading from Alpaca to LlaMA-3 yielded more consistent steering with significant effects across emotions and strengths (all $p < 0.001$). Inter-rater reliability was high (ICC $= 0.71$–$0.87$), underscoring the robustness of the findings. These findings support activation-based control as a scalable method for steering LLM behavior across affective dimensions.

Method: Proposes LLaMEA-SAGE which extracts graph-theoretic and complexity features from abstract syntax trees of generated algorithms, builds a surrogate model from evaluated solutions, uses explainable AI to identify performance-critical features, and translates them into natural-language mutation instructions to guide subsequent LLM code generation.

Result: Achieves same performance faster than vanilla LLaMEA in controlled experiments, and superior performance compared to state-of-the-art AAD methods on the MA-BBOB suite from GECCO-MA-BBOB competition.

Conclusion: Code-derived signals can effectively bias LLM-driven algorithm evolution, bridging the gap between code structure and human-understandable performance feedback in automated algorithm design.

Abstract: Large language models have enabled automated algorithm design (AAD) by generating optimization algorithms directly from natural-language prompts. While evolutionary frameworks such as LLaMEA demonstrate strong exploratory capabilities across the algorithm design space, their search dynamics are entirely driven by fitness feedback, leaving substantial information about the generated code unused. We propose a mechanism for guiding AAD using feedback constructed from graph-theoretic and complexity features extracted from the abstract syntax trees of the generated algorithms, based on a surrogate model learned over an archive of evaluated solutions. Using explainable AI techniques, we identify features that substantially affect performance and translate them into natural-language mutation instructions that steer subsequent LLM-based code generation without restricting expressivity. We propose LLaMEA-SAGE, which integrates this feature-driven guidance into LLaMEA, and evaluate it across several benchmarks. We show that the proposed structured guidance achieves the same performance faster than vanilla LLaMEA in a small controlled experiment. In a larger-scale experiment using the MA-BBOB suite from the GECCO-MA-BBOB competition, our guided approach achieves superior performance compared to state-of-the-art AAD methods. These results demonstrate that signals derived from code can effectively bias LLM-driven algorithm evolution, bridging the gap between code structure and human-understandable performance feedback in automated algorithm design.

Method: Three tightly coupled components: 1) git-native experimentation engine for reproducible artifacts and provenance, 2) knowledge system ingesting heterogeneous sources into structured representations, 3) cognitive memory layer coordinating retrieval and maintaining episodic store of reusable lessons from experiment traces.

Result: Evaluated on MLE-Bench (Kaggle-style ML competitions) and ALE-Bench (AtCoder heuristic optimization) with reported end-to-end performance results.

Conclusion: KAPSO treats synthesis as an operator within long-horizon optimization rather than an endpoint, enabling systematic improvement of programs through iterative learning and knowledge reuse.

Abstract: We introduce KAPSO, a modular framework for autonomous program synthesis and optimization. Given a natural language goal and an evaluation method, KAPSO iteratively performs ideation, code synthesis and editing, execution, evaluation, and learning to improve a runnable artifact toward measurable objectives. Rather than treating synthesis as the endpoint, KAPSO uses synthesis as an operator within a long-horizon optimization loop, where progress is defined by evaluator outcomes. KAPSO targets long-horizon failures common in coding agents, including lost experimental state, brittle debugging, and weak reuse of domain expertise, by integrating three tightly coupled components. First, a git-native experimentation engine isolates each attempt as a branch, producing reproducible artifacts and preserving provenance across iterations. Second, a knowledge system ingests heterogeneous sources, including repositories, internal playbooks, and curated external resources such as documentation, scientific papers, and web search results, and organizes them into a structured representation that supports retrieval over workflows, implementations, and environment constraints. Third, a cognitive memory layer coordinates retrieval and maintains an episodic store of reusable lessons distilled from experiment traces (run logs, diffs, and evaluator feedback), reducing repeated error modes and accelerating convergence. We evaluated KAPSO on MLE-Bench (Kaggle-style ML competitions) and ALE-Bench (AtCoder heuristic optimization), and report end-to-end performance. Code Available at: https://github.com/Leeroo-AI/kapso

Method: ARGORA organizes multi-expert discussions into explicit argumentation graphs showing support/attack relationships between arguments. It casts these graphs as causal models, enabling systematic removal of individual arguments and recomputation of outcomes to identify necessary reasoning chains. The framework includes a correction mechanism that aligns internal reasoning with external judgments when they disagree.

Result: Across diverse benchmarks and an open-ended use case, ARGORA achieves competitive accuracy and demonstrates corrective behavior: when experts initially disagree, the framework resolves disputes toward correct answers more often than it introduces new errors, while providing causal diagnostics of decisive arguments.

Conclusion: ARGORA provides a transparent framework for multi-expert LLM systems that makes reasoning explicit through argumentation graphs and causal analysis, enabling better understanding of decision-making processes and correction mechanisms when needed.

Abstract: Existing multi-expert LLM systems gather diverse perspectives but combine them through simple aggregation, obscuring which arguments drove the final decision. We introduce ARGORA, a framework that organizes multi-expert discussions into explicit argumentation graphs showing which arguments support or attack each other. By casting these graphs as causal models, ARGORA can systematically remove individual arguments and recompute outcomes, identifying which reasoning chains were necessary and whether decisions would change under targeted modifications. We further introduce a correction mechanism that aligns internal reasoning with external judgments when they disagree. Across diverse benchmarks and an open-ended use case, ARGORA achieves competitive accuracy and demonstrates corrective behavior: when experts initially disagree, the framework resolves disputes toward correct answers more often than it introduces new errors, while providing causal diagnostics of decisive arguments.

Method: Three-tier memory architecture: Tier A (per-agent working state), Tier B (sharded evidence with shard-local ANN indexes and scope-before-routing constraints), Tier C (versioned skill library). Uses masked mixture-of-experts routing with eligibility constraints and cost-aware gating over shard families.

Result: Improves over strongest baseline by +5.11 to +6.82 F1 on LoCoMo, reduces retrieval work by 20.5% and p95 latency from 95ms to 76ms. Achieves 63.41/61.88/57.95 F1 at 56K/224K/448K tokens on HotpotQA. Tier C reaches 0.97 Precision@3 and 1.94 StepRed on ToolBench.

Conclusion: ShardMemo provides efficient, scalable memory management for agentic LLM systems through structured eligibility constraints and intelligent routing, significantly improving performance while reducing computational overhead.

Abstract: Agentic large language model (LLM) systems rely on external memory for long-horizon state and concurrent multi-agent execution, but centralized indexes and heuristic partitions become bottlenecks as memory volume and parallel access grow. We present ShardMemo, a budgeted tiered memory service with Tier A per-agent working state, Tier B sharded evidence with shard-local approximate nearest neighbor (ANN) indexes, and Tier C, a versioned skill library. Tier B enforces scope-before-routing: structured eligibility constraints mask ineligible shards before routing or ANN search. We cast shard probing as masked mixture-of-experts (MoE) routing over eligible shards, probing up to $B_{\mathrm{probe}}$ shards via Top-$B_{\mathrm{probe}}$ or adaptive Top-$P$, and use cost-aware gating over profile/observation/session shard families; the router is trained from evidence-to-shard supervision. On LoCoMo, ShardMemo improves over the strongest baseline (GAM) by +5.11 to +6.82 F1 across question categories. Under a fixed-budget routing setting ($B_{\mathrm{probe}}=3$), ShardMemo improves over cosine-to-prototype shard routing by +6.87 F1 while reducing retrieval work (VecScan 521->414, -20.5%) and p95 latency (95->76 ms). On long-context HotpotQA, ShardMemo achieves 63.41/61.88/57.95 F1 at 56K/224K/448K tokens. On ToolBench, Tier C reaches 0.97 Precision@3 and 1.94 StepRed (+10.2% and +7.2% over embedding-similarity retrieval).

Method: MCE uses a bi-level framework: meta-level agent refines engineering skills via agentic crossover (deliberative search over skill history), while base-level agent executes skills, learns from training rollouts, and optimizes context as flexible files and code.

Result: MCE achieves 5.6-53.8% relative improvement over state-of-the-art agentic CE methods (mean 16.9%) across five domains, with superior context adaptability, transferability, and efficiency.

Conclusion: MCE supersedes static CE heuristics by co-evolving skills and context artifacts, enabling more effective context optimization for LLMs.

Abstract: The operational efficacy of large language models relies heavily on their inference-time context. This has established Context Engineering (CE) as a formal discipline for optimizing these inputs. Current CE methods rely on manually crafted harnesses, such as rigid generation-reflection workflows and predefined context schemas. They impose structural biases and restrict context optimization to a narrow, intuition-bound design space. To address this, we introduce Meta Context Engineering (MCE), a bi-level framework that supersedes static CE heuristics by co-evolving CE skills and context artifacts. In MCE iterations, a meta-level agent refines engineering skills via agentic crossover, a deliberative search over the history of skills, their executions, and evaluations. A base-level agent executes these skills, learns from training rollouts, and optimizes context as flexible files and code. We evaluate MCE across five disparate domains under offline and online settings. MCE demonstrates consistent performance gains, achieving 5.6–53.8% relative improvement over state-of-the-art agentic CE methods (mean of 16.9%), while maintaining superior context adaptability, transferability, and efficiency in both context usage and training.

Method: Introduces EmboCoach-Bench, a benchmark with 32 expert-curated RL and IL tasks. Uses executable code as universal interface and assesses dynamic closed-loop workflow where LLM agents iteratively draft, debug, and optimize solutions using environment feedback, covering reward design and policy architectures like diffusion policies.

Result: Autonomous agents surpass human-engineered baselines by 26.5% average success rate. Agentic workflow with environment feedback strengthens policy development and narrows performance gap between open-source and proprietary models. Agents exhibit self-correction capabilities, resurrecting task performance from near-total failures through simulation-in-the-loop debugging.

Conclusion: Establishes foundation for self-evolving embodied intelligence, accelerating shift from labor-intensive manual tuning to scalable, autonomous engineering in embodied AI.

Abstract: The field of Embodied AI is witnessing a rapid evolution toward general-purpose robotic systems, fueled by high-fidelity simulation and large-scale data collection. However, this scaling capability remains severely bottlenecked by a reliance on labor-intensive manual oversight from intricate reward shaping to hyperparameter tuning across heterogeneous backends. Inspired by LLMs’ success in software automation and science discovery, we introduce \textsc{EmboCoach-Bench}, a benchmark evaluating the capacity of LLM agents to autonomously engineer embodied policies. Spanning 32 expert-curated RL and IL tasks, our framework posits executable code as the universal interface. We move beyond static generation to assess a dynamic closed-loop workflow, where agents leverage environment feedback to iteratively draft, debug, and optimize solutions, spanning improvements from physics-informed reward design to policy architectures such as diffusion policies. Extensive evaluations yield three critical insights: (1) autonomous agents can qualitatively surpass human-engineered baselines by 26.5% in average success rate; (2) agentic workflow with environment feedback effectively strengthens policy development and substantially narrows the performance gap between open-source and proprietary models; and (3) agents exhibit self-correction capabilities for pathological engineering cases, successfully resurrecting task performance from near-total failures through iterative simulation-in-the-loop debugging. Ultimately, this work establishes a foundation for self-evolving embodied intelligence, accelerating the paradigm shift from labor-intensive manual tuning to scalable, autonomous engineering in embodied AI field.

Method: Introduces Order-r Interaction theory to analyze learning signal decay for high-order logical dependencies. Proposes ALiCoT framework that aligns latent token distributions with intermediate reasoning states to overcome signal decay. Validates using NatBool-DAG benchmark for irreducible logical reasoning.

Result: ALiCoT achieves 54.4x speedup while maintaining performance comparable to explicit CoT, successfully unlocking efficient reasoning through proper alignment of latent states.

Conclusion: Theoretical analysis reveals exponential decay of learning signals for high-order dependencies in CoT compression. ALiCoT’s alignment approach effectively overcomes this barrier, enabling efficient reasoning without performance degradation.

Abstract: Chain-of-Thought (CoT) has unlocked advanced reasoning abilities of Large Language Models (LLMs) with intermediate steps, yet incurs prohibitive computational costs due to generation of extra tokens. Recent studies empirically show that compressing reasoning steps into latent states, or implicit CoT compression, offers a token-efficient alternative. However, the mechanism behind CoT compression remains unclear. In this paper, we provide the first theoretical analysis of the difficulty of learning to internalize intermediate reasoning steps. By introducing Order-r Interaction, we prove that the learning signal for high-order logical dependencies exponentially decays to solve irreducible problem, where skipping intermediate steps inevitably leads to high-order interaction barriers. To empirically validate this, we introduce NatBool-DAG, a challenging benchmark designed to enforce irreducible logical reasoning and eliminate semantic shortcuts. Guided by our theoretical findings, we propose ALiCoT (Aligned Implicit CoT), a novel framework that overcomes the signal decay by aligning latent token distributions with intermediate reasoning states. Experimental results demonstrate that ALiCoT successfully unlocks efficient reasoning: it achieves a 54.4x speedup while maintaining performance comparable to explicit CoT.

Method: Proposes Dreamer framework with modular depth-recurrent attention mixtures combining sequence attention, depth attention, and sparse expert attention to alleviate hidden-size bottlenecks and decouple scaling dimensions.

Result: Models require 2-8x fewer training tokens for same accuracy as FLOP-, parameter-, and memory-matched SOTA, and outperform ~2x larger SOTA models with same training tokens. Also shows 2-11x larger expert selection diversity than SOTA MoEs.

Conclusion: Depth-recurrent attention mixtures enable efficient scaling and effective latent reasoning by addressing hidden-size bottlenecks and improving knowledge usage across depths.

Abstract: Depth-recurrence facilitates latent reasoning by sharing parameters across depths. However, prior work lacks combined FLOP-, parameter-, and memory-matched baselines, underutilizes depth-recurrence due to partially fixed layer stacks, and ignores the bottleneck of constant hidden-sizes that restricts many-step latent reasoning. To address this, we introduce a modular framework of depth-recurrent attention mixtures (Dreamer), combining sequence attention, depth attention, and sparse expert attention. It alleviates the hidden-size bottleneck through attention along depth, decouples scaling dimensions, and allows depth-recurrent models to scale efficiently and effectively. Across language reasoning benchmarks, our models require 2 to 8x fewer training tokens for the same accuracy as FLOP-, parameter-, and memory-matched SOTA, and outperform ca. 2x larger SOTA models with the same training tokens. We further present insights into knowledge usage across depths, e.g., showing 2 to 11x larger expert selection diversity than SOTA MoEs.

Method: Proposes ATP-Latent with two key components: 1) Models latent token supervision as conditional VAE to obtain smoother latent space, 2) Uses reinforcement learning with auxiliary coherence reward based on consistency between VAE-decoded contents to guide optimal reasoning policy.

Result: On LLaMA-1B, ATP-Latent achieves +4.1% accuracy and -3.3% tokens on four benchmarks compared to advanced baselines.

Conclusion: ATP-Latent demonstrates that active planning over latent token representation space with VAE modeling and coherence-guided RL leads to more efficient and accurate latent reasoning in LLMs.

Abstract: Aiming at efficient and dense chain-of-thought (CoT) reasoning, latent reasoning methods fine-tune Large Language Models (LLMs) to substitute discrete language tokens with continuous latent tokens. These methods consume fewer tokens compared to the conventional language CoT reasoning and have the potential to plan in a dense latent space. However, current latent tokens are generally supervised based on imitating language labels. Considering that there can be multiple equivalent but diverse CoT labels for a question, passively imitating an arbitrary one may lead to inferior latent token representations and latent reasoning policies, undermining the potential planning ability and resulting in clear gaps between training and testing. In this work, we emphasize the importance of active planning over the representation space of latent tokens in achieving the optimal latent reasoning policy. So, we propose the \underline{A}c\underline{t}ive Latent \underline{P}lanning method (ATP-Latent), which models the supervision process of latent tokens as a conditional variational auto-encoder (VAE) to obtain a smoother latent space. Moreover, to facilitate the most reasonable latent reasoning policy, ATP-Latent conducts reinforcement learning (RL) with an auxiliary coherence reward, which is calculated based on the consistency between VAE-decoded contents of latent tokens, enabling a guided RL process. In experiments on LLaMA-1B, ATP-Latent demonstrates +4.1% accuracy and -3.3% tokens on four benchmarks compared to advanced baselines. Codes are available on https://github.com/zz1358m/ATP-Latent-master.

Method: CORE uses a two-stage process: 1) Cold round of independent sampling, 2) Contexted rescue round where failed models receive hints from successful peers. It optimizes a combined reward balancing correctness, diversity (DPP-inspired to reduce error overlap), and rescue bonus for successful recovery.

Result: With only 1,000 training examples, a pair of small models (3B+4B) achieves Pass@2 of 99.54% on GSM8K and 92.08% on MATH, compared to 82.50% and 74.82% for single-model training. On harder datasets, reaches 77.34% on GPQA and 79.65% on AIME with limited training data.

Conclusion: Training-time collaboration can reliably convert model complementarity into large performance gains without scaling model size, demonstrating effective cross-teaching between models with complementary reasoning patterns.

Abstract: Large language models exhibit complementary reasoning errors: on the same instance, one model may succeed with a particular decomposition while another fails. We propose Collaborative Reasoning (CORE), a training-time collaboration framework that converts peer success into a learning signal via a cross-teaching protocol. Each problem is solved in two stages: a cold round of independent sampling, followed by a contexted rescue round in which models that failed receive hint extracted from a successful peer. CORE optimizes a combined reward that balances (i) correctness, (ii) a lightweight DPP-inspired diversity term to reduce error overlap, and (iii) an explicit rescue bonus for successful recovery. We evaluate CORE across four standard reasoning datasets GSM8K, MATH, AIME, and GPQA. With only 1,000 training examples, a pair of small open source models (3B+4B) reaches Pass@2 of 99.54% on GSM8K and 92.08% on MATH, compared to 82.50% and 74.82% for single-model training. On harder datasets, the 3B+4B pair reaches Pass@2 of 77.34% on GPQA (trained on 348 examples) and 79.65% on AIME (trained on 792 examples), using a training-time budget of at most 1536 context tokens and 3072 generated tokens. Overall, these results show that training-time collaboration can reliably convert model complementarity into large gains without scaling model size.

Method: Formalizes as SBST problem in combinatorial document configuration space, rendering structural risk features. Benchmarks diverse search strategies (evolutionary, swarm-based, quality-diversity, learning-based, quantum) under identical budgets with configuration-level exclusivity, win-rate, and cross-temporal overlap analyses.

Result: Different solvers consistently uncover failure modes undiscovered by alternatives. Cross-temporal analysis shows persistent solver-specific discoveries across all budgets, with no single strategy dominating. Union of all solvers eventually recovers failure space, but individual methods systematically delay discovery of important risks.

Conclusion: Demonstrates intrinsic solver complementarity and motivates portfolio-based SBST strategies for robust industrial IDP validation, rather than relying on single methods.

Abstract: Enterprise-grade Intelligent Document Processing (IDP) systems support high-stakes workflows across finance, insurance, and healthcare. Early-phase system validation under limited budgets mandates uncovering diverse failure mechanisms, rather than identifying a single worst-case document. We formalize this challenge as a Search-Based Software Testing (SBST) problem, aiming to identify complex interactions between document variables, with the objective to maximize the number of distinct failure types discovered within a fixed evaluation budget. Our methodology operates on a combinatorial space of document configurations, rendering instances of structural \emph{risk features} to induce realistic failure conditions. We benchmark a diverse portfolio of search strategies spanning evolutionary, swarm-based, quality-diversity, learning-based, and quantum under identical budget constraints. Through configuration-level exclusivity, win-rate, and cross-temporal overlap analyses, we show that different solvers consistently uncover failure modes that remain undiscovered by specific alternatives at comparable budgets. Crucially, cross-temporal analysis reveals persistent solver-specific discoveries across all evaluated budgets, with no single strategy exhibiting absolute dominance. While the union of all solvers eventually recovers the observed failure space, reliance on any individual method systematically delays the discovery of important risks. These results demonstrate intrinsic solver complementarity and motivate portfolio-based SBST strategies for robust industrial IDP validation.

Method: RecNet consists of two phases: 1) Forward phase with centralized preference routing (router agents integrate and propagate updates) and personalized preference reception (message buffer + rule-based filter memory for selective assimilation), and 2) Backward phase with feedback-driven propagation optimization using multi-agent reinforcement learning with LLMs for credit assignment, gradient analysis, and module-level optimization.

Result: Extensive experiments on various scenarios demonstrate the effectiveness of RecNet in modeling preference propagation for recommender systems.

Conclusion: RecNet provides a self-evolving framework that addresses limitations of existing methods by enabling real-time preference propagation and continuous optimization through LLM-powered multi-agent systems.

Abstract: Agentic recommender systems leverage Large Language Models (LLMs) to model complex user behaviors and support personalized decision-making. However, existing methods primarily model preference changes based on explicit user-item interactions, which are sparse, noisy, and unable to reflect the real-time, mutual influences among users and items. To address these limitations, we propose RecNet, a self-evolving preference propagation framework that proactively propagates real-time preference updates across related users and items. RecNet consists of two complementary phases. In the forward phase, the centralized preference routing mechanism leverages router agents to integrate preference updates and dynamically propagate them to the most relevant agents. To ensure accurate and personalized integration of propagated preferences, we further introduce a personalized preference reception mechanism, which combines a message buffer for temporary caching and an optimizable, rule-based filter memory to guide selective preference assimilation based on past experience and interests. In the backward phase, the feedback-driven propagation optimization mechanism simulates a multi-agent reinforcement learning framework, using LLMs for credit assignment, gradient analysis, and module-level optimization, enabling continuous self-evolution of propagation strategies. Extensive experiments on various scenarios demonstrate the effectiveness of RecNet in modeling preference propagation for recommender systems.

Method: Created WhatCounts benchmark: atomic counting tasks with unambiguous, delimited lists containing no duplicates or distractors, testing different semantic types (cities vs chemicals, names vs symbols).

Result: Frontier LLMs show over 40% accuracy variation depending solely on what is being counted. Controlled ablations rule out confounds, and semantic gaps shift unpredictably with small fine-tuning.

Conclusion: LLMs don’t implement true algorithms but approximate them in argument-dependent ways, with implications for all LLM functions carrying hidden semantic dependencies.

Abstract: Counting should not depend on what is being counted; more generally, any algorithm’s behavior should be invariant to the semantic content of its arguments. We introduce WhatCounts to test this property in isolation. Unlike prior work that conflates semantic sensitivity with reasoning complexity or prompt variation, WhatCounts is atomic: count items in an unambiguous, delimited list with no duplicates, distractors, or reasoning steps for different semantic types. Frontier LLMs show over 40% accuracy variation depending solely on what is being counted - cities versus chemicals, names versus symbols. Controlled ablations rule out confounds. The gap is semantic, and it shifts unpredictably with small amounts of unrelated fine-tuning. LLMs do not implement algorithms; they approximate them, and the approximation is argument-dependent. As we show with an agentic example, this has implications beyond counting: any LLM function may carry hidden dependencies on the meaning of its inputs.

Method: ScholarGym decouples workflow components into query planning, tool invocation, and relevance assessment, enabling fine-grained analysis under controlled conditions. Built on a static corpus of 570K papers with deterministic retrieval, providing 2,536 queries with expert-annotated ground truth.

Result: Experiments across diverse backbone models reveal how reasoning capabilities, planning strategies, and selection mechanisms interact over iterative refinement in research workflows.

Conclusion: ScholarGym provides a reproducible evaluation framework for deep research workflows, enabling systematic analysis of tool-augmented LLM performance in academic literature tasks.

Abstract: Tool-augmented large language models have advanced from single-turn question answering to deep research workflows that iteratively plan queries, invoke external tools, and synthesize information to address complex information needs. Evaluating such workflows presents a fundamental challenge: reliance on live APIs introduces non-determinism, as tool invocations may yield different results across runs due to temporal drift, rate limiting, and evolving backend states. This variance undermines reproducibility and invalidates cross-system comparisons. We present ScholarGym, a simulation environment for reproducible evaluation of deep research workflows on academic literature. The environment decouples workflow components into query planning, tool invocation, and relevance assessment, enabling fine-grained analysis of each stage under controlled conditions. Built on a static corpus of 570K papers with deterministic retrieval, ScholarGym provides 2,536 queries with expert-annotated ground truth. Experiments across diverse backbone models reveal how reasoning capabilities, planning strategies, and selection mechanisms interact over iterative refinement.

Method: Created SONIC-O1 benchmark spanning 13 conversational domains with 4,958 human-verified annotations and demographic metadata. Evaluates MLLMs on open-ended summarization, multiple-choice question answering, and temporal localization with reasoning rationales.

Result: Experiments reveal limitations: while MCQ accuracy gap between model families is small, there’s a substantial 22.6% performance difference in temporal localization between best closed-source and open-source models. Performance degrades across demographic groups, indicating persistent disparities.

Conclusion: SONIC-O1 provides an open evaluation suite for temporally grounded and socially robust multimodal understanding, highlighting significant gaps in current MLLM capabilities for audio-video processing.

Abstract: Multimodal Large Language Models (MLLMs) are a major focus of recent AI research. However, most prior work focuses on static image understanding, while their ability to process sequential audio-video data remains underexplored. This gap highlights the need for a high-quality benchmark to systematically evaluate MLLM performance in a real-world setting. We introduce SONIC-O1, a comprehensive, fully human-verified benchmark spanning 13 real-world conversational domains with 4,958 annotations and demographic metadata. SONIC-O1 evaluates MLLMs on key tasks, including open-ended summarization, multiple-choice question (MCQ) answering, and temporal localization with supporting rationales (reasoning). Experiments on closed- and open-source models reveal limitations. While the performance gap in MCQ accuracy between two model families is relatively small, we observe a substantial 22.6% performance difference in temporal localization between the best performing closed-source and open-source models. Performance further degrades across demographic groups, indicating persistent disparities in model behavior. Overall, SONIC-O1 provides an open evaluation suite for temporally grounded and socially robust multimodal understanding. We release SONIC-O1 for reproducibility and research: Project page: https://vectorinstitute.github.io/sonic-o1/ Dataset: https://huggingface.co/datasets/vector-institute/sonic-o1 Github: https://github.com/vectorinstitute/sonic-o1 Leaderboard: https://huggingface.co/spaces/vector-institute/sonic-o1-leaderboard

Method: Tri-Component Attention Profiling (TCAP) decomposes cross-modal attention maps into three functional components, identifies trigger-responsive attention heads via GMM statistical profiling, and isolates poisoned samples through EM-based vote aggregation.

Result: Extensive experiments across diverse MLLM architectures and attack methods show TCAP achieves consistently strong performance as a robust backdoor defense.

Conclusion: TCAP establishes a practical unsupervised defense against backdoor attacks in MLLMs by leveraging universal attention allocation divergence patterns.

Abstract: Fine-Tuning-as-a-Service (FTaaS) facilitates the customization of Multimodal Large Language Models (MLLMs) but introduces critical backdoor risks via poisoned data. Existing defenses either rely on supervised signals or fail to generalize across diverse trigger types and modalities. In this work, we uncover a universal backdoor fingerprint-attention allocation divergence-where poisoned samples disrupt the balanced attention distribution across three functional components: system instructions, vision inputs, and user textual queries, regardless of trigger morphology. Motivated by this insight, we propose Tri-Component Attention Profiling (TCAP), an unsupervised defense framework to filter backdoor samples. TCAP decomposes cross-modal attention maps into the three components, identifies trigger-responsive attention heads via Gaussian Mixture Model (GMM) statistical profiling, and isolates poisoned samples through EM-based vote aggregation. Extensive experiments across diverse MLLM architectures and attack methods demonstrate that TCAP achieves consistently strong performance, establishing it as a robust and practical backdoor defense in MLLMs.

Method: Proposes Fovea-Block-Skip Transformer with three modules: Parafovea-Attention Window (PAW) for causal trainable loops, Chunk-Head (CH) for chunk-structure awareness, and Skip-Gate (SG) for compute allocation.

Result: FBS improves quality-efficiency trade-off across diverse benchmarks without increasing parameters, with ablations showing the three modules are complementary.

Conclusion: The FBS Transformer successfully incorporates human reading strategies into LLM architecture, achieving better inference efficiency while maintaining quality.

Abstract: Large language models (LLMs) excel across many tasks, yet inference is still dominated by strictly token-by-token autoregression. Existing acceleration methods largely patch this pipeline and miss core human-reading ingredients: content-adaptive foresight, chunk-structure-aware compute allocation, and train–test consistency for preview/skimming. We propose the \textbf{Fovea-Block-Skip Transformer} (FBS), which injects a causal, trainable loop into Transformers via Parafovea-Attention Window (PAW), Chunk-Head (CH), and Skip-Gate (SG). Across diverse benchmarks, FBS improves the quality-efficiency trade-off without increasing parameters, and ablations show the three modules are complementary.

Method: Proposes E-mem framework with heterogeneous hierarchical architecture: multiple assistant agents maintain uncompressed memory contexts while a central master agent orchestrates global planning. Unlike passive retrieval, assistants locally reason within activated segments to extract context-aware evidence before aggregation.

Result: On LoCoMo benchmark, E-mem achieves over 54% F1, surpassing state-of-the-art GAM by 7.75%, while reducing token cost by over 70%.

Conclusion: E-mem effectively addresses memory preprocessing limitations by preserving contextual integrity through episodic context reconstruction, enabling better reasoning performance with reduced computational cost.

Abstract: The evolution of Large Language Model (LLM) agents towards System~2 reasoning, characterized by deliberative, high-precision problem-solving, requires maintaining rigorous logical integrity over extended horizons. However, prevalent memory preprocessing paradigms suffer from destructive de-contextualization. By compressing complex sequential dependencies into pre-defined structures (e.g., embeddings or graphs), these methods sever the contextual integrity essential for deep reasoning. To address this, we propose E-mem, a framework shifting from Memory Preprocessing to Episodic Context Reconstruction. Inspired by biological engrams, E-mem employs a heterogeneous hierarchical architecture where multiple assistant agents maintain uncompressed memory contexts, while a central master agent orchestrates global planning. Unlike passive retrieval, our mechanism empowers assistants to locally reason within activated segments, extracting context-aware evidence before aggregation. Evaluations on the LoCoMo benchmark demonstrate that E-mem achieves over 54% F1, surpassing the state-of-the-art GAM by 7.75%, while reducing token cost by over 70%.

Method: DropoutTS employs a Sample-Adaptive Dropout mechanism that leverages spectral sparsity to efficiently quantify instance-level noise via reconstruction residuals, then dynamically calibrates model learning capacity by mapping noise to adaptive dropout rates.

Result: Extensive experiments across diverse noise regimes and open benchmarks show DropoutTS consistently boosts superior backbones’ performance, delivering advanced robustness with negligible parameter overhead and no architectural modifications.

Conclusion: DropoutTS provides an effective and efficient solution for time series model robustness that shifts the paradigm from “what” to learn to “how much” to learn, with minimal computational overhead.

Abstract: Deep time series models are vulnerable to noisy data ubiquitous in real-world applications. Existing robustness strategies either prune data or rely on costly prior quantification, failing to balance effectiveness and efficiency. In this paper, we introduce DropoutTS, a model-agnostic plugin that shifts the paradigm from “what” to learn to “how much” to learn. DropoutTS employs a Sample-Adaptive Dropout mechanism: leveraging spectral sparsity to efficiently quantify instance-level noise via reconstruction residuals, it dynamically calibrates model learning capacity by mapping noise to adaptive dropout rates - selectively suppressing spurious fluctuations while preserving fine-grained fidelity. Extensive experiments across diverse noise regimes and open benchmarks show DropoutTS consistently boosts superior backbones’ performance, delivering advanced robustness with negligible parameter overhead and no architectural modifications. Our code is available at https://github.com/CityMind-Lab/DropoutTS.

Method: SCOUT decouples exploration from exploitation: lightweight scouts (small MLPs) probe environmental dynamics efficiently, collected trajectories bootstrap LLM via Supervised Fine-Tuning, followed by multi-turn Reinforcement Learning to activate latent world knowledge.

Result: SCOUT enables Qwen2.5-3B-Instruct to achieve average score of 0.86, significantly outperforming proprietary models like Gemini-2.5-Pro (0.60) while saving about 60% GPU hours consumption.

Conclusion: The exploration cost bottleneck for LLMs in nonlinguistic tasks can be effectively addressed by decoupling exploration from exploitation using lightweight scouts, enabling efficient learning while maintaining strong performance.

Abstract: While Large Language Models (LLMs) excel in language-based agentic tasks, their applicability to unseen, nonlinguistic environments (e.g., symbolic or spatial tasks) remains limited. Previous work attributes this performance gap to the mismatch between the pretraining distribution and the testing distribution. In this work, we demonstrate the primary bottleneck is the prohibitive cost of exploration: mastering these tasks requires extensive trial-and-error, which is computationally unsustainable for parameter-heavy LLMs operating in a high dimensional semantic space. To address this, we propose SCOUT (Sub-Scale Collaboration On Unseen Tasks), a novel framework that decouples exploration from exploitation. We employ lightweight “scouts” (e.g., small MLPs) to probe environmental dynamics at a speed and scale far exceeding LLMs. The collected trajectories are utilized to bootstrap the LLM via Supervised Fine-Tuning (SFT), followed by multi-turn Reinforcement Learning (RL) to activate its latent world knowledge. Empirically, SCOUT enables a Qwen2.5-3B-Instruct model to achieve an average score of 0.86, significantly outperforming proprietary models, including Gemini-2.5-Pro (0.60), while saving about 60% GPU hours consumption.

Method: Proposes Zero-Shot Statistical Downscaling (ZSSD) framework that learns Physics-Consistent Climate Prior from reanalysis data, conditioned on geophysical boundaries and temporal information. Introduces Unified Coordinate Guidance to address vanishing gradient problem in vanilla DPS and ensure consistency with large-scale fields.

Result: ZSSD significantly outperforms existing zero-shot baselines in 99th percentile errors and successfully reconstructs complex weather events like tropical cyclones across heterogeneous GCMs.

Conclusion: ZSSD provides an effective zero-shot framework for statistical downscaling that addresses key limitations of existing methods, enabling robust inference across varying GCMs without requiring paired training data.

Abstract: Conventional supervised climate downscaling struggles to generalize to Global Climate Models (GCMs) due to the lack of paired training data and inherent domain gaps relative to reanalysis. Meanwhile, current zero-shot methods suffer from physical inconsistencies and vanishing gradient issues under large scaling factors. We propose Zero-Shot Statistical Downscaling (ZSSD), a zero-shot framework that performs statistical downscaling without paired data during training. ZSSD leverages a Physics-Consistent Climate Prior learned from reanalysis data, conditioned on geophysical boundaries and temporal information to enforce physical validity. Furthermore, to enable robust inference across varying GCMs, we introduce Unified Coordinate Guidance. This strategy addresses the vanishing gradient problem in vanilla DPS and ensures consistency with large-scale fields. Results show that ZSSD significantly outperforms existing zero-shot baselines in 99th percentile errors and successfully reconstructs complex weather events, such as tropical cyclones, across heterogeneous GCMs.

Method: Represent abstract concepts (e.g., attack, sacrifice) as geometric regions across interpretable quality dimensions. Chess games are instantiated as trajectories, and strategy recognition occurs by analyzing directional movement toward these concept regions. Supports dual-perspective modeling for different player interpretations.

Result: Demonstrated feasibility of trajectory-based concept recognition with movement patterns aligning with expert commentary. The approach successfully recognizes intended strategies in chess games.

Conclusion: Establishes a foundation for broader applications in sequential decision-making and supports integration with knowledge evolution mechanisms for learning and refining abstract concepts over time.

Abstract: We present a conceptual space framework for modelling abstract concepts that unfold over time, demonstrated through a chess-based proof-of-concept. Strategy concepts, such as attack or sacrifice, are represented as geometric regions across interpretable quality dimensions, with chess games instantiated and analysed as trajectories whose directional movement toward regions enables recognition of intended strategies. This approach also supports dual-perspective modelling, capturing how players interpret identical situations differently. Our implementation demonstrates the feasibility of trajectory-based concept recognition, with movement patterns aligning with expert commentary. This work explores extending the conceptual spaces theory to temporally realised, goal-directed concepts. The approach establishes a foundation for broader applications involving sequential decision-making and supports integration with knowledge evolution mechanisms for learning and refining abstract concepts over time.

Method: Created benchmark dataset with curated end-to-end bioinformatics tasks (RNA-seq, variant calling, metagenomics) with concrete output specifications. Used LLM-based grader to score pipeline progress and outcome validity, with stress testing via controlled perturbations (corrupted inputs, decoy files, prompt bloat). Evaluated both closed-source and open-weight models across multiple agent harnesses.

Result: Frontier agents can complete multi-step bioinformatics pipelines without elaborate custom scaffolding, often producing requested final artifacts reliably. However, robustness tests reveal failure modes under controlled perturbations, showing that correct high-level pipeline construction doesn’t guarantee reliable step-level reasoning. Open-weight models may be preferable for privacy-sensitive workflows despite lower completion rates.

Conclusion: BioAgent Bench provides a comprehensive evaluation framework for AI agents in bioinformatics, revealing both capabilities and limitations. Privacy considerations make open-weight models viable alternatives to closed-source models in sensitive contexts, despite performance trade-offs.

Abstract: This paper introduces BioAgent Bench, a benchmark dataset and an evaluation suite designed for measuring the performance and robustness of AI agents in common bioinformatics tasks. The benchmark contains curated end-to-end tasks (e.g., RNA-seq, variant calling, metagenomics) with prompts that specify concrete output artifacts to support automated assessment, including stress testing under controlled perturbations. We evaluate frontier closed-source and open-weight models across multiple agent harnesses, and use an LLM-based grader to score pipeline progress and outcome validity. We find that frontier agents can complete multi-step bioinformatics pipelines without elaborate custom scaffolding, often producing the requested final artifacts reliably. However, robustness tests reveal failure modes under controlled perturbations (corrupted inputs, decoy files, and prompt bloat), indicating that correct high-level pipeline construction does not guarantee reliable step-level reasoning. Finally, because bioinformatics workflows may involve sensitive patient data, proprietary references, or unpublished IP, closed-source models can be unsuitable under strict privacy constraints; in such settings, open-weight models may be preferable despite lower completion rates. We release the dataset and evaluation suite publicly.

Method: Proposes a unified LLM-centered framework where the LLM serves as central reasoning module for spatiotemporal activity recognition and explainable decision analysis from video data, plus verbalizing feedback in natural language.

Result: LLM-based approach outperforms baseline models with ~15-20% improvement in accuracy and F1 scores. Framework includes pilot student-support module with anomaly detection and XAI principles for automated, interpretable feedback.

Conclusion: Establishes scalable, interpretable, data-driven foundation for advancing nursing education, enhancing training efficiency, and improving patient safety through video-based activity recognition and feedback generation.

Abstract: Endotracheal suctioning (ES) is an invasive yet essential clinical procedure that requires a high degree of skill to minimize patient risk - particularly in home care and educational settings, where consistent supervision may be limited. Despite its critical importance, automated recognition and feedback systems for ES training remain underexplored. To address this gap, this study proposes a unified, LLM-centered framework for video-based activity recognition benchmarked against conventional machine learning and deep learning approaches, and a pilot study on feedback generation. Within this framework, the Large Language Model (LLM) serves as the central reasoning module, performing both spatiotemporal activity recognition and explainable decision analysis from video data. Furthermore, the LLM is capable of verbalizing feedback in natural language, thereby translating complex technical insights into accessible, human-understandable guidance for trainees. Experimental results demonstrate that the proposed LLM-based approach outperforms baseline models, achieving an improvement of approximately 15-20% in both accuracy and F1 score. Beyond recognition, the framework incorporates a pilot student-support module built upon anomaly detection and explainable AI (XAI) principles, which provides automated, interpretable feedback highlighting correct actions and suggesting targeted improvements. Collectively, these contributions establish a scalable, interpretable, and data-driven foundation for advancing nursing education, enhancing training efficiency, and ultimately improving patient safety.

Method: Proposes CORE framework with multiple LLMs assigned distinct functional roles distributed across mobile devices and tiered edge servers. Includes three optimization modules: real-time perception, dynamic role orchestration, and pipeline-parallel execution. Introduces a novel role affinity scheduling algorithm for dynamically orchestrating LLM role assignments across hierarchical edge infrastructure.

Result: Comprehensive case studies and performance evaluations across various 6G application scenarios demonstrated efficacy, showing significant enhancements in system efficiency and task completion rates. Further validation on real-world edge-computing platform exhibited robust performance in operational environments.

Conclusion: CORE successfully addresses the challenge of fragmented computing resources in 6G networks by enabling efficient collaboration among distributed LLM agents through role-based orchestration and optimization, making it practical for real-world edge computing deployments.

Abstract: Rapid advancements in sixth-generation (6G) networks and large language models (LLMs) have paved the way for ubiquitous intelligence, wherein seamless connectivity and distributed artificial intelligence (AI) have revolutionized various aspects of our lives.However, realizing this vision faces significant challenges owing to the fragmented and heterogeneous computing resources across hierarchical networks, which are insufficient for individual LLM agents to perform complex reasoning tasks.To address this issue, we propose Collaborative Orchestration Role at Edge (CORE), an innovative framework that employs a collaborative learning system in which multiple LLMs, each assigned a distinct functional role, are distributed across mobile devices and tiered edge servers. The system integrates three optimization modules, encompassing real-time perception,dynamic role orchestration, and pipeline-parallel execution, to facilitate efficient and rapid collaboration among distributed agents. Furthermore, we introduce a novel role affinity scheduling algorithm for dynamically orchestrating LLM role assignments across the hierarchical edge infrastructure, intelligently matching computational demands with available dispersed resources.Finally, comprehensive case studies and performance evaluations across various 6G application scenarios demonstrated the efficacy of CORE, revealing significant enhancements in the system efficiency and task completion rates. Building on these promising outcomes, we further validated the practical applicability of CORE by deploying it on a real-world edge-computing platform,that exhibits robust performance in operational environments.

Method: Introduces a benchmark methodology combining performance-based evaluation with representation-level analysis using SHAP (for feature importance) and UMAP (for dimensionality reduction and visualization). Evaluates multiple ECG-expert FMs pretrained with state-of-the-art techniques across cross-continental datasets, including data-scarce scenarios.

Result: Experimental results show the benchmarking protocol provides rich insights into ECG-expert FMs’ embedded patterns, enabling deeper understanding of their representational structure and generalizability across different data availability settings.

Conclusion: The proposed comprehensive benchmarking framework fills the gap in evaluating ECG-expert Foundation Models by providing both performance and representation-level analysis, crucial for responsible deployment in healthcare applications.

Abstract: The electrocardiogram (ECG) is a cost-effective, highly accessible and widely employed diagnostic tool. With the advent of Foundation Models (FMs), the field of AI-assisted ECG interpretation has begun to evolve, as they enable model reuse across different tasks by relying on embeddings. However, to responsibly employ FMs, it is crucial to rigorously assess to which extent the embeddings they produce are generalizable, particularly in error-sensitive domains such as healthcare. Although prior works have already addressed the problem of benchmarking ECG-expert FMs, they focus predominantly on the evaluation of downstream performance. To fill this gap, this study aims to find an in-depth, comprehensive benchmarking framework for FMs, with a specific focus on ECG-expert ones. To this aim, we introduce a benchmark methodology that complements performance-based evaluation with representation-level analysis, leveraging SHAP and UMAP techniques. Furthermore, we rely on the methodology for carrying out an extensive evaluation of several ECG-expert FMs pretrained via state-of-the-art techniques over different cross-continental datasets and data availability settings; this includes ones featuring data scarcity, a fairly common situation in real-world medical scenarios. Experimental results show that our benchmarking protocol provides a rich insight of ECG-expert FMs’ embedded patterns, enabling a deeper understanding of their representational structure and generalizability.

Method: Proposed a decision-centric simulation framework with three components: (1) synthetic demand generator for spare-parts characteristics, (2) flexible forecasting module for arbitrary predictive models, and (3) inventory control simulator that computes operational KPIs from forecasts.

Result: Showed that improvements in conventional accuracy metrics don’t necessarily translate to better operational performance, and models with similar statistical error can produce different cost-service trade-offs. Analyzed how specific forecast performance aspects affect inventory outcomes.

Conclusion: The framework operationalizes the link between demand forecasting and inventory management, shifting evaluation from predictive accuracy toward operational relevance in automotive aftermarket and related domains.

Abstract: Efficient management of spare parts inventory is crucial in the automotive aftermarket, where demand is highly intermittent and uncertainty drives substantial cost and service risks. Forecasting is therefore central, but the quality of a forecasting model should be judged not by statistical accuracy (e.g., MAE, RMSE, IAE) but rather by its impact on key operational performance indicators (KPIs), such as total cost and service level. Yet most existing work evaluates models exclusively using accuracy metrics, and the relationship between these metrics and operational KPIs remains poorly understood. To address this gap, we propose a decision-centric simulation software framework that enables systematic evaluation of forecasting model in realistic inventory management setting. The framework comprises: (i) a synthetic demand generator tailored to spare-parts demand characteristics, (ii) a flexible forecasting module that can host arbitrary predictive models, and (iii) an inventory control simulator that consumes the forecasts and computes operational KPIs. This closed-loop setup enables researchers to evaluate models not only in terms of statistical error but also in terms of their downstream implications for inventory decisions. Using a wide range of simulation scenarios, we show that improvements in conventional accuracy metrics do not necessarily translate into better operational performance, and that models with similar statistical error profiles can induce markedly different cost-service trade-offs. We analyze these discrepancies to characterize how specific aspects of forecast performance affect inventory outcomes and derive guidance for model selection. Overall, the framework operationalizes the link between demand forecasting and inventory management, shifting evaluation from purely predictive accuracy toward operational relevance in the automotive aftermarket and related domains.

Method: KnowBias identifies neurons encoding bias knowledge using attribution-based analysis on a small set of bias-knowledge questions, then selectively enhances these neurons at inference time without retraining.

Result: Experiments across multiple benchmarks and LLMs demonstrate consistent state-of-the-art debiasing performance with minimal utility degradation, showing strong generalization across bias types and demographics.

Conclusion: KnowBias offers a lightweight, data-efficient alternative to suppressive debiasing methods that preserves general capabilities while effectively mitigating social biases in LLMs.

Abstract: Large language models (LLMs) exhibit social biases that reinforce harmful stereotypes, limiting their safe deployment. Most existing debiasing methods adopt a suppressive paradigm by modifying parameters, prompts, or neurons associated with biased behavior; however, such approaches are often brittle, weakly generalizable, data-inefficient, and prone to degrading general capability. We propose \textbf{KnowBias}, a lightweight and conceptually distinct framework that mitigates bias by strengthening, rather than suppressing, neurons encoding bias-knowledge. KnowBias identifies neurons encoding bias knowledge using a small set of bias-knowledge questions via attribution-based analysis, and selectively enhances them at inference time. This design enables strong debiasing while preserving general capabilities, generalizes across bias types and demographics, and is highly data efficient, requiring only a handful of simple yes/no questions and no retraining. Experiments across multiple benchmarks and LLMs demonstrate consistent state-of-the-art debiasing performance with minimal utility degradation. Data and code are available at https://github.com/JP-25/KnowBias.

Method: WebArbiter formulates reward modeling as text generation, producing structured justifications that conclude with a preference verdict and identify the most conducive action. Training uses a two-stage pipeline: 1) reasoning distillation to equip the model with principle-guided reasoning, and 2) reinforcement learning to correct teacher biases by directly aligning verdicts with correctness.

Result: On WebPRMBench (new benchmark spanning four web environments), WebArbiter-7B outperforms GPT-5 by 9.1 points. In reward-guided trajectory search on WebArena-Lite, it surpasses the best prior WebPRM by up to 7.2 points.

Conclusion: WebArbiter demonstrates robustness and practical value in real-world complex web tasks through its reasoning-first approach that provides interpretable justifications and strong generalization capabilities.

Abstract: Web agents hold great potential for automating complex computer tasks, yet their interactions involve long-horizon, sequential decision-making with irreversible actions. In such settings, outcome-based supervision is sparse and delayed, often rewarding incorrect trajectories and failing to support inference-time scaling. This motivates the use of Process Reward Models (WebPRMs) for web navigation, but existing approaches remain limited: scalar WebPRMs collapse progress into coarse, weakly grounded signals, while checklist-based WebPRMs rely on brittle template matching that fails under layout or semantic changes and often mislabels superficially correct actions as successful, providing little insight or interpretability. To address these challenges, we introduce WebArbiter, a reasoning-first, principle-inducing WebPRM that formulates reward modeling as text generation, producing structured justifications that conclude with a preference verdict and identify the action most conducive to task completion under the current context. Training follows a two-stage pipeline: reasoning distillation equips the model with coherent principle-guided reasoning, and reinforcement learning corrects teacher biases by directly aligning verdicts with correctness, enabling stronger generalization. To support systematic evaluation, we release WebPRMBench, a comprehensive benchmark spanning four diverse web environments with rich tasks and high-quality preference annotations. On WebPRMBench, WebArbiter-7B outperforms the strongest baseline, GPT-5, by 9.1 points. In reward-guided trajectory search on WebArena-Lite, it surpasses the best prior WebPRM by up to 7.2 points, underscoring its robustness and practical value in real-world complex web tasks.

Method: TRAP² encodes protection into the update during fine-tuning using weight re-scaling as a simple proxy for the merging process. It keeps released weights effective in standalone use but degrades them under re-scaling that often arises in merging, undermining unauthorized merging attempts.

Result: The framework provides architecture-agnostic protection that works regardless of whether models are released as adapters or full models, effectively preventing unauthorized model merging while maintaining standalone functionality.

Conclusion: TRAP² closes the governance gap in model hubs by providing a practical, architecture-agnostic solution to protect model weights from unauthorized merging, addressing both safety alignment and licensing concerns in the era of modular AI components.

Abstract: The rise of model hubs has made it easier to access reusable model components, making model merging a practical tool for combining capabilities. Yet, this modularity also creates a \emph{governance gap}: downstream users can recompose released weights into unauthorized mixtures that bypass safety alignment or licensing terms. Because existing defenses are largely post-hoc and architecture-specific, they provide inconsistent protection across diverse architectures and release formats in practice. To close this gap, we propose \textsc{Trap}$^{2}$, an architecture-agnostic protection framework that encodes protection into the update during fine-tuning, regardless of whether they are released as adapters or full models. Instead of relying on architecture-dependent approaches, \textsc{Trap}$^{2}$ uses weight re-scaling as a simple proxy for the merging process. It keeps released weights effective in standalone use, but degrades them under re-scaling that often arises in merging, undermining unauthorized merging.

Method: Chain-of-Meta-Thought (CoMT) focuses supervised learning on abstract reasoning patterns without specific executions to acquire generalizable strategies. Confidence-Calibrated Reinforcement Learning (CCRL) then optimizes task adaptation via confidence-aware rewards on intermediate steps, preventing overconfident errors from cascading.

Result: Experiments across four models and eight benchmarks show 2.19% and 4.63% improvements in-distribution and out-of-distribution respectively over standard methods, while reducing training time by 65-70% and token consumption by 50%.

Conclusion: Aligning post-training with human cognitive principles yields superior generalization and enhanced training efficiency, demonstrating that explicitly modeling the two-stage human cognitive process improves LLM reasoning capabilities.

Abstract: Current LLM post-training methods optimize complete reasoning trajectories through Supervised Fine-Tuning (SFT) followed by outcome-based Reinforcement Learning (RL). While effective, a closer examination reveals a fundamental gap: this approach does not align with how humans actually solve problems. Human cognition naturally decomposes problem-solving into two distinct stages: first acquiring abstract strategies (i.e., meta-knowledge) that generalize across problems, then adapting them to specific instances. In contrast, by treating complete trajectories as basic units, current methods are inherently problem-centric, entangling abstract strategies with problem-specific execution. To address this misalignment, we propose a cognitively-inspired framework that explicitly mirrors the two-stage human cognitive process. Specifically, Chain-of-Meta-Thought (CoMT) focuses supervised learning on abstract reasoning patterns without specific executions, enabling acquisition of generalizable strategies. Confidence-Calibrated Reinforcement Learning (CCRL) then optimizes task adaptation via confidence-aware rewards on intermediate steps, preventing overconfident errors from cascading and improving execution reliability. Experiments across four models and eight benchmarks show 2.19% and 4.63% improvements in-distribution and out-of-distribution respectively over standard methods, while reducing training time by 65-70% and token consumption by 50%, demonstrating that aligning post-training with human cognitive principles yields not only superior generalization but also enhanced training efficiency.

Method: Four-stage framework: (1) Supervised Policy Warmup to initialize reasoning format, (2) MCTS-based Process Reward Model (PRM) to quantify intermediate reasoning quality, (3) PRM-Guided Reasoning Refinement to align policy with process preferences, (4) Process-Supervised RL with dual-granularity advantage mechanism combining step-level process rewards with global outcome signals.

Result: Extensive experiments on five multi-hop reasoning benchmarks show ProRAG achieves superior performance compared to outcome-based and process-aware RL baselines, particularly on complex long-horizon tasks, validating the effectiveness of fine-grained process supervision.

Conclusion: ProRAG successfully addresses reward sparsity and credit assignment issues in RL for RAG by integrating learned step-level supervision into the online optimization loop, demonstrating the value of process supervision for complex reasoning tasks.

Abstract: Reinforcement learning (RL) has become a promising paradigm for optimizing Retrieval-Augmented Generation (RAG) in complex reasoning tasks. However, traditional outcome-based RL approaches often suffer from reward sparsity and inefficient credit assignment, as coarse-grained scalar rewards fail to identify specific erroneous steps within long-horizon trajectories. This ambiguity frequently leads to “process hallucinations”, where models reach correct answers through flawed logic or redundant retrieval steps. Although recent process-aware approaches attempt to mitigate this via static preference learning or heuristic reward shaping, they often lack the on-policy exploration capabilities required to decouple step-level credit from global outcomes. To address these challenges, we propose ProRAG, a process-supervised reinforcement learning framework designed to integrate learned step-level supervision into the online optimization loop. Our framework consists of four stages: (1) Supervised Policy Warmup to initialize the model with a structured reasoning format; (2) construction of an MCTS-based Process Reward Model (PRM) to quantify intermediate reasoning quality; (3) PRM-Guided Reasoning Refinement to align the policy with fine-grained process preferences; and (4) Process-Supervised Reinforcement Learning with a dual-granularity advantage mechanism. By aggregating step-level process rewards with global outcome signals, ProRAG provides precise feedback for every action. Extensive experiments on five multi-hop reasoning benchmarks demonstrate that ProRAG achieves superior overall performance compared to strong outcome-based and process-aware RL baselines, particularly on complex long-horizon tasks, validating the effectiveness of fine-grained process supervision. The code and model are available at https://github.com/lilinwz/ProRAG.

Method: JADE models the system as a cooperative multi-agent team unified under a single shared backbone, enabling end-to-end learning driven by outcome-based rewards. This facilitates co-adaptation where the planner learns to operate within executor capabilities while executors evolve to align with strategic intent.

Result: Empirical results show JADE transforms disjoint modules into a synergistic system, yielding remarkable performance improvements via joint optimization and enabling flexible balance between efficiency and effectiveness through dynamic workflow orchestration.

Conclusion: JADE provides a unified framework for joint optimization of planning and execution in dynamic multi-turn workflows, addressing the strategic-operational mismatch in agentic RAG systems through co-adaptation of modules.

Abstract: The evolution of Retrieval-Augmented Generation (RAG) has shifted from static retrieval pipelines to dynamic, agentic workflows where a central planner orchestrates multi-turn reasoning. However, existing paradigms face a critical dichotomy: they either optimize modules jointly within rigid, fixed-graph architectures, or empower dynamic planning while treating executors as frozen, black-box tools. We identify that this \textit{decoupled optimization} creates a ``strategic-operational mismatch,’’ where sophisticated planning strategies fail to materialize due to unadapted local executors, often leading to negative performance gains despite increased system complexity. In this paper, we propose \textbf{JADE} (\textbf{J}oint \textbf{A}gentic \textbf{D}ynamic \textbf{E}xecution), a unified framework for the joint optimization of planning and execution within dynamic, multi-turn workflows. By modeling the system as a cooperative multi-agent team unified under a single shared backbone, JADE enables end-to-end learning driven by outcome-based rewards. This approach facilitates \textit{co-adaptation}: the planner learns to operate within the capability boundaries of the executors, while the executors evolve to align with high-level strategic intent. Empirical results demonstrate that JADE transforms disjoint modules into a synergistic system, yielding remarkable performance improvements via joint optimization and enabling a flexible balance between efficiency and effectiveness through dynamic workflow orchestration.

Method: Proposes Self-Compression via MARL (SCMA) with three agents: Segmentation Agent decomposes reasoning into logical chunks, Scoring Agent quantifies chunk significance, and Reasoning Agent learns to prioritize essential logic using importance-weighted length penalty.

Result: SCMA reduces response length by 11.1% to 39.0% while boosting accuracy by 4.33% to 10.02% across model scales. Ablation studies show emergent behaviors yield more powerful reasoning models.

Conclusion: The multi-agent RL framework effectively reduces redundant reasoning without compromising accuracy, enabling more efficient deployment of Large Reasoning Models through selective compression of non-essential reasoning chunks.

Abstract: The inference overhead induced by redundant reasoning undermines the interactive experience and severely bottlenecks the deployment of Large Reasoning Models. Existing reinforcement learning (RL)-based solutions tackle this problem by coupling a length penalty with outcome-based rewards. This simplistic reward weighting struggles to reconcile brevity with accuracy, as enforcing brevity may compromise critical reasoning logic. In this work, we address this limitation by proposing a multi-agent RL framework that selectively penalizes redundant chunks, while preserving essential reasoning logic. Our framework, Self-Compression via MARL (SCMA), instantiates redundancy detection and evaluation through two specialized agents: \textbf{a Segmentation Agent} for decomposing the reasoning process into logical chunks, and \textbf{a Scoring Agent} for quantifying the significance of each chunk. The Segmentation and Scoring agents collaboratively define an importance-weighted length penalty during training, incentivizing \textbf{a Reasoning Agent} to prioritize essential logic without introducing inference overhead during deployment. Empirical evaluations across model scales demonstrate that SCMA reduces response length by 11.1% to 39.0% while boosting accuracy by 4.33% to 10.02%. Furthermore, ablation studies and qualitative analysis validate that the synergistic optimization within the MARL framework fosters emergent behaviors, yielding more powerful LRMs compared to vanilla RL paradigms.

Method: A role-structured multi-agent debate framework with explicit agent roles (prosecutor, defense, judge), 7-turn structured interaction protocols, and private reasoning strategies, benchmarked on young adult recidivism prediction using NLSY97 dataset.

Result: Structured multi-agent debate provides more stable and generalizable performance than single-agent reasoning, with stronger correlation between accuracy and F1-score metrics, plus fine-grained control and complete interaction transcripts for explainability.

Conclusion: AgenticSimLaw offers a generalizable approach for high-stakes decision tasks requiring transparency and human oversight, addressing key LLM-based multi-agent system challenges through structured roles, logged interactions, and explicit non-deployment constraints.

Abstract: We introduce AgenticSimLaw, a role-structured, multi-agent debate framework that provides transparent and controllable test-time reasoning for high-stakes tabular decision-making tasks. Unlike black-box approaches, our courtroom-style orchestration explicitly defines agent roles (prosecutor, defense, judge), interaction protocols (7-turn structured debate), and private reasoning strategies, creating a fully auditable decision-making process. We benchmark this framework on young adult recidivism prediction using the NLSY97 dataset, comparing it against traditional chain-of-thought (CoT) prompting across almost 90 unique combinations of models and strategies. Our results demonstrate that structured multi-agent debate provides more stable and generalizable performance compared to single-agent reasoning, with stronger correlation between accuracy and F1-score metrics. Beyond performance improvements, AgenticSimLaw offers fine-grained control over reasoning steps, generates complete interaction transcripts for explainability, and enables systematic profiling of agent behaviors. While we instantiate this framework in the criminal justice domain to stress-test reasoning under ethical complexity, the approach generalizes to any deliberative, high-stakes decision task requiring transparency and human oversight. This work addresses key LLM-based multi-agent system challenges: organization through structured roles, observability through logged interactions, and responsibility through explicit non-deployment constraints for sensitive domains. Data, results, and code will be available on github.com under the MIT license.

Method: DeR2 introduces a deep-research sandbox with four controlled regimes: Instruction-only, Concepts (gold concepts without documents), Related-only (only relevant documents), and Full-set (relevant documents plus distractors). It uses frozen document libraries from 2023-2025 theoretical papers with expert-annotated concepts and validated rationales, with two-phase validation to prevent parametric leakage.

Result: Experiments reveal substantial variation across state-of-the-art foundation models, with some showing mode-switch fragility (performing worse with Full-set than Instruction-only) and others exhibiting structural concept misuse (naming concepts correctly but failing to execute them procedurally).

Conclusion: DeR2 provides a controlled framework for evaluating document-grounded reasoning, revealing significant headroom for improvement in how models reason over novel scientific information beyond retrieval capabilities and memorization.

Abstract: Despite strong performance on existing benchmarks, it remains unclear whether large language models can reason over genuinely novel scientific information. Most evaluations score end-to-end RAG pipelines, where reasoning is confounded with retrieval and toolchain choices, and the signal is further contaminated by parametric memorization and open-web volatility. We introduce DeR2, a controlled deep-research sandbox that isolates document-grounded reasoning while preserving core difficulties of deep search: multi-step synthesis, denoising, and evidence-based conclusion making. DeR2 decouples evidence access from reasoning via four regimes–Instruction-only, Concepts (gold concepts without documents), Related-only (only relevant documents), and Full-set (relevant documents plus topically related distractors)–yielding interpretable regime gaps that operationalize retrieval loss vs. reasoning loss and enable fine-grained error attribution. To prevent parametric leakage, we apply a two-phase validation that requires parametric failure without evidence while ensuring oracle-concept solvability. To ensure reproducibility, each instance provides a frozen document library (drawn from 2023-2025 theoretical papers) with expert-annotated concepts and validated rationales. Experiments across a diverse set of state-of-the-art foundation models reveal substantial variation and significant headroom: some models exhibit mode-switch fragility, performing worse with the Full-set than with Instruction-only, while others show structural concept misuse, correctly naming concepts but failing to execute them as procedures.

Method: Proposes Agent Reasoning Reward Model (Agent-RRM) with three components: explicit reasoning traces, focused critiques highlighting reasoning flaws, and overall process scores. Investigates three integration strategies: Reagent-C (text-augmented refinement), Reagent-R (reward-augmented guidance), and Reagent-U (unified feedback integration).

Result: Extensive evaluations across 12 diverse benchmarks show Reagent-U achieves substantial performance improvements: 43.7% on GAIA and 46.2% on WebWalkerQA, validating the effectiveness of the reasoning reward model and training schemes.

Conclusion: Agent-RRM provides structured, multi-faceted feedback that significantly improves agentic RL performance by addressing the limitations of sparse outcome-based rewards, with Reagent-U emerging as the most effective integration strategy.

Abstract: Agentic Reinforcement Learning (Agentic RL) has achieved notable success in enabling agents to perform complex reasoning and tool use. However, most methods still relies on sparse outcome-based reward for training. Such feedback fails to differentiate intermediate reasoning quality, leading to suboptimal training results. In this paper, we introduce Agent Reasoning Reward Model (Agent-RRM), a multi-faceted reward model that produces structured feedback for agentic trajectories, including (1) an explicit reasoning trace , (2) a focused critique that provides refinement guidance by highlighting reasoning flaws, and (3) an overall score that evaluates process performance. Leveraging these signals, we systematically investigate three integration strategies: Reagent-C (text-augmented refinement), Reagent-R (reward-augmented guidance), and Reagent-U (unified feedback integration). Extensive evaluations across 12 diverse benchmarks demonstrate that Reagent-U yields substantial performance leaps, achieving 43.7% on GAIA and 46.2% on WebWalkerQA, validating the effectiveness of our reasoning reward model and training schemes. Code, models, and datasets are all released to facilitate future research.

Method: Proposes ToolWeaver framework that encodes tools into hierarchical sequences through a novel tokenization process, weaving together a tool’s intrinsic semantics with extrinsic co-usage patterns. The structured codes are integrated into LLMs through generative alignment fine-tuning.

Result: Evaluation with nearly 47,000 tools shows ToolWeaver significantly outperforms state-of-the-art methods, establishing a more scalable, generalizable, and semantically-aware foundation for advanced tool-augmented agents.

Conclusion: ToolWeaver addresses the scalability and generalization crisis in generative tool learning by using hierarchical tool encoding, enabling better learning of collaborative patterns and establishing a foundation for advanced tool-augmented agents.

Abstract: Prevalent retrieval-based tool-use pipelines struggle with a dual semantic challenge: their retrievers often employ encoders that fail to capture complex semantics, while the Large Language Model (LLM) itself lacks intrinsic tool knowledge from its natural language pretraining. Generative methods offer a powerful alternative by unifying selection and execution, tasking the LLM to directly learn and generate tool identifiers. However, the common practice of mapping each tool to a unique new token introduces substantial limitations: it creates a scalability and generalization crisis, as the vocabulary size explodes and each tool is assigned a semantically isolated token. This approach also creates a semantic bottleneck that hinders the learning of collaborative tool relationships, as the model must infer them from sparse co-occurrences of monolithic tool IDs within a vast library. To address these limitations, we propose ToolWeaver, a novel generative tool learning framework that encodes tools into hierarchical sequences. This approach makes vocabulary expansion logarithmic to the number of tools. Crucially, it enables the model to learn collaborative patterns from the dense co-occurrence of shared codes, rather than the sparse co-occurrence of monolithic tool IDs. We generate these structured codes through a novel tokenization process designed to weave together a tool’s intrinsic semantics with its extrinsic co-usage patterns. These structured codes are then integrated into the LLM through a generative alignment stage, where the model is fine-tuned to produce the hierarchical code sequences. Evaluation results with nearly 47,000 tools show that ToolWeaver significantly outperforms state-of-the-art methods, establishing a more scalable, generalizable, and semantically-aware foundation for advanced tool-augmented agents.

Method: VAF pipeline with three stages: (1) variant generation - create webpage variants with identical semantics but different visual attributes; (2) browsing interaction - agents navigate via scrolling and clicking like human users; (3) validation - measure Target Click Rate and Target Mention Rate to evaluate visual attribute effects.

Result: Experiments across 8 variant families (48 variants), 5 real-world websites (shopping, travel, news), and 4 web agents show background color contrast, item size, position, and card clarity strongly influence agent actions, while font styling, text color, and item image clarity have minor effects.

Conclusion: Visual attributes significantly impact web agent decision-making, with certain attributes (background contrast, size, position, clarity) having strong influence while others (font styling, text color) are less important, providing systematic understanding of visual factors in agent behavior.

Abstract: Web agents have demonstrated strong performance on a wide range of web-based tasks. However, existing research on the effect of environmental variation has mostly focused on robustness to adversarial attacks, with less attention to agents’ preferences in benign scenarios. Although early studies have examined how textual attributes influence agent behavior, a systematic understanding of how visual attributes shape agent decision-making remains limited. To address this, we introduce VAF, a controlled evaluation pipeline for quantifying how webpage Visual Attribute Factors influence web-agent decision-making. Specifically, VAF consists of three stages: (i) variant generation, which ensures the variants share identical semantics as the original item while only differ in visual attributes; (ii) browsing interaction, where agents navigate the page via scrolling and clicking the interested item, mirroring how human users browse online; (iii) validating through both click action and reasoning from agents, which we use the Target Click Rate and Target Mention Rate to jointly evaluate the effect of visual attributes. By quantitatively measuring the decision-making difference between the original and variant, we identify which visual attributes influence agents’ behavior most. Extensive experiments, across 8 variant families (48 variants total), 5 real-world websites (including shopping, travel, and news browsing), and 4 representative web agents, show that background color contrast, item size, position, and card clarity have a strong influence on agents’ actions, whereas font styling, text color, and item image clarity exhibit minor effects.

Method: The authors introduce a domain model configuration framework that enables controlled variation of features such as element ordering, action arity, and dead-end states. They empirically investigate energy consumption using five benchmark domains and five state-of-the-art planners, analyzing energy and runtime impacts across 32 domain variants per benchmark.

Result: Results demonstrate that domain-level modifications produce measurable energy differences across planners, with energy consumption not always correlating with runtime. This shows that energy efficiency can be systematically analyzed and potentially optimized through domain model design choices.

Conclusion: The paper concludes that energy consumption should be considered as a critical performance dimension in automated planning, and that domain model characteristics significantly impact energy efficiency. The findings support the Green AI paradigm shift toward considering energy consumption alongside traditional performance metrics.

Abstract: AI research has traditionally prioritised algorithmic performance, such as optimising accuracy in machine learning or runtime in automated planning. The emerging paradigm of Green AI challenges this by recognising energy consumption as a critical performance dimension. Despite the high computational demands of automated planning, its energy efficiency has received little attention. This gap is particularly salient given the modular planning structure, in which domain models are specified independently of algorithms. On the other hand, this separation also enables systematic analysis of energy usage through domain model design. We empirically investigate how domain model characteristics affect the energy consumption of classical planners. We introduce a domain model configuration framework that enables controlled variation of features, such as element ordering, action arity, and dead-end states. Using five benchmark domains and five state-of-the-art planners, we analyse energy and runtime impacts across 32 domain variants per benchmark. Results demonstrate that domain-level modifications produce measurable energy differences across planners, with energy consumption not always correlating with runtime.

Method: The researchers systematically studied how elicitation protocols affect stated-revealed preference correlation across 24 language models. They experimented with allowing neutrality and abstention during stated preference elicitation, and further allowed abstention in revealed preferences. They also tested system prompt steering using stated preferences during revealed preference elicitation on AIRiskDilemmas.

Result: Allowing neutrality and abstention during stated preference elicitation improved Spearman’s rank correlation between volunteered stated preferences and forced-choice revealed preferences by excluding weak signals. However, allowing abstention in revealed preferences drove correlation to near-zero or negative values due to high neutrality rates. System prompt steering using stated preferences during revealed preference elicitation did not reliably improve stated-revealed correlation on AIRiskDilemmas.

Conclusion: Stated-revealed preference correlation is highly protocol-dependent, and preference elicitation requires methods that account for indeterminate preferences rather than relying on binary forced-choice approaches that may not capture genuine model preferences.

Abstract: Recent work identifies a stated-revealed (SvR) preference gap in language models (LMs): a mismatch between the values models endorse and the choices they make in context. Existing evaluations rely heavily on binary forced-choice prompting, which entangles genuine preferences with artifacts of the elicitation protocol. We systematically study how elicitation protocols affect SvR correlation across 24 LMs. Allowing neutrality and abstention during stated preference elicitation allows us to exclude weak signals, substantially improving Spearman’s rank correlation ($ρ$) between volunteered stated preferences and forced-choice revealed preferences. However, further allowing abstention in revealed preferences drives $ρ$ to near-zero or negative values due to high neutrality rates. Finally, we find that system prompt steering using stated preferences during revealed preference elicitation does not reliably improve SvR correlation on AIRiskDilemmas. Together, our results show that SvR correlation is highly protocol-dependent and that preference elicitation requires methods that account for indeterminate preferences.

Method: VERSA uses a state-transition model to define valid event sequences, enabling automatic detection and correction of anomalous patterns in soccer event stream data.

Result: Analysis of K League 1 (2024) data revealed 18.81% of events had logical inconsistencies. VERSA improved cross-provider consistency and enhanced robustness/performance of VAEP (player contribution evaluation) downstream task.

Conclusion: Systematic verification of event data integrity is crucial for reliable sports analytics, and VERSA effectively addresses data quality issues to improve downstream analysis reliability.

Abstract: Event stream data is a critical resource for fine-grained analysis across various domains, including financial transactions, system operations, and sports. In sports, it is actively used for fine-grained analyses such as quantifying player contributions and identifying tactical patterns. However, the reliability of these models is fundamentally limited by inherent data quality issues that cause logical inconsistencies (e.g., incorrect event ordering or missing events). To this end, this study proposes VERSA (Verified Event Data Format for Reliable Soccer Analytics), a systematic verification framework that ensures the integrity of event stream data within the soccer domain. VERSA is based on a state-transition model that defines valid event sequences, thereby enabling the automatic detection and correction of anomalous patterns within the event stream data. Notably, our examination of event data from the K League 1 (2024 season), provided by Bepro, detected that 18.81% of all recorded events exhibited logical inconsistencies. Addressing such integrity issues, our experiments demonstrate that VERSA significantly enhances cross-provider consistency, ensuring stable and unified data representation across heterogeneous sources. Furthermore, we demonstrate that data refined by VERSA significantly improves the robustness and performance of a downstream task called VAEP, which evaluates player contributions. These results highlight that the verification process is highly effective in increasing the reliability of data-driven analysis.

Method: Introduces Liquid Interfaces as ephemeral relational events that emerge through runtime intention articulation and semantic negotiation. Formalizes the model with Liquid Interface Protocol (LIP) that governs intention-driven interaction, negotiated execution, and enforces ephemerality under semantic uncertainty.

Result: Presents a formal model and reference architecture demonstrating practical feasibility. The approach provides a principled foundation for adaptive coordination in agent-based systems.

Conclusion: Liquid Interfaces offer a novel coordination paradigm that addresses the limitations of static interfaces for autonomous agents, enabling more adaptive, context-aware interactions through dynamic interface emergence and negotiation.

Abstract: Contemporary software architectures struggle to support autonomous agents whose reasoning is adaptive, probabilistic, and context-dependent, while system integration remains dominated by static interfaces and deterministic contracts. This paper introduces Liquid Interfaces, a coordination paradigm in which interfaces are not persistent technical artifacts, but ephemeral relational events that emerge through intention articulation and semantic negotiation at runtime.We formalize this model and present the Liquid Interface Protocol (LIP),which governs intention-driven interaction, negotiated execution, and enforce ephemerality under semantic uncertainty. We further discuss the governance implications of this approach and describe a reference architecture that demonstrates practical feasibility. Liquid Interfaces provide a principled foundation for adaptive coordination in agent-based systems

Method: Introduces two new metrics: Operational Intensity (OI) and Capacity Footprint (CF) to analyze different regimes of AI agent inference. Analyzes various agentic workflows (chat, coding, web use, computer use) and model architectures (GQA/MLA, MoE, quantization) to understand how OI/CF shift, particularly with long context KV cache making decode memory-bound.

Result: The analysis reveals that OI/CF metrics shift dramatically across different agentic workflows and model choices, with long context KV cache making decode operations highly memory-bound. This exposes limitations in current hardware architectures.

Conclusion: Proposes disaggregated serving and system-level heterogeneity as solutions: specialized prefill/decode accelerators, scale-up networking, optical I/O for compute-memory decoupling. Suggests agent-hardware co-design, multiple inference accelerators per system, and high-bandwidth large-capacity memory disaggregation as foundations for adapting to evolving OI/CF requirements.

Abstract: AI agent inference is driving an inference heavy datacenter future and exposes bottlenecks beyond compute - especially memory capacity, memory bandwidth and high-speed interconnect. We introduce two metrics - Operational Intensity (OI) and Capacity Footprint (CF) - that jointly explain regimes the classic roofline analysis misses, including the memory capacity wall. Across agentic workflows (chat, coding, web use, computer use) and base model choices (GQA/MLA, MoE, quantization), OI/CF can shift dramatically, with long context KV cache making decode highly memory bound. These observations motivate disaggregated serving and system level heterogeneity: specialized prefill and decode accelerators, broader scale up networking, and decoupled compute-memory enabled by optical I/O. We further hypothesize agent-hardware co design, multiple inference accelerators within one system, and high bandwidth, large capacity memory disaggregation as foundations for adaptation to evolving OI/CF. Together, these directions chart a path to sustain efficiency and capability for large scale agentic AI inference.

Method: Introduces CAR-bench with LLM-simulated user, domain policies, and 58 interconnected tools spanning navigation, productivity, charging, and vehicle control. Includes Hallucination tasks (testing limit-awareness with missing tools/info) and Disambiguation tasks (requiring uncertainty resolution through clarification or info gathering).

Result: Baseline results show large gaps between occasional and consistent success. Even frontier reasoning LLMs achieve <50% consistent pass rate on Disambiguation tasks due to premature actions, and frequently violate policies or fabricate information in Hallucination tasks.

Conclusion: Highlights the need for more reliable and self-aware LLM agents in real-world settings, demonstrating current limitations in handling uncertainty and maintaining consistency in multi-turn, tool-using scenarios.

Abstract: Existing benchmarks for Large Language Model (LLM) agents focus on task completion under idealistic settings but overlook reliability in real-world, user-facing applications. In domains, such as in-car voice assistants, users often issue incomplete or ambiguous requests, creating intrinsic uncertainty that agents must manage through dialogue, tool use, and policy adherence. We introduce CAR-bench, a benchmark for evaluating consistency, uncertainty handling, and capability awareness in multi-turn, tool-using LLM agents in an in-car assistant domain. The environment features an LLM-simulated user, domain policies, and 58 interconnected tools spanning navigation, productivity, charging, and vehicle control. Beyond standard task completion, CAR-bench introduces Hallucination tasks that test agents’ limit-awareness under missing tools or information, and Disambiguation tasks that require resolving uncertainty through clarification or internal information gathering. Baseline results reveal large gaps between occasional and consistent success on all task types. Even frontier reasoning LLMs achieve less than 50% consistent pass rate on Disambiguation tasks due to premature actions, and frequently violate policies or fabricate information to satisfy user requests in Hallucination tasks, underscoring the need for more reliable and self-aware LLM agents in real-world settings.

Method: Agent Workflow Optimization (AWO) analyzes existing workflow traces to discover recurring sequences of tool calls and transforms them into meta-tools - deterministic, composite tools that bundle multiple agent actions into a single invocation, bypassing unnecessary intermediate LLM reasoning steps.

Result: Experiments on two agentic AI benchmarks show AWO reduces the number of LLM calls up to 11.9% while increasing task success rate by up to 4.2 percentage points.

Conclusion: AWO provides an effective framework for optimizing agentic workflows by identifying and replacing redundant tool execution patterns with meta-tools, improving both efficiency and robustness of LLM-based agent systems.

Abstract: Agentic AI enables LLM to dynamically reason, plan, and interact with tools to solve complex tasks. However, agentic workflows often require many iterative reasoning steps and tool invocations, leading to significant operational expense, end-to-end latency and failures due to hallucinations. This work introduces Agent Workflow Optimization (AWO), a framework that identifies and optimizes redundant tool execution patterns to improve the efficiency and robustness of agentic workflows. AWO analyzes existing workflow traces to discover recurring sequences of tool calls and transforms them into meta-tools, which are deterministic, composite tools that bundle multiple agent actions into a single invocation. Meta-tools bypass unnecessary intermediate LLM reasoning steps and reduce operational cost while also shortening execution paths, leading to fewer failures. Experiments on two agentic AI benchmarks show that AWO reduces the number of LLM calls up to 11.9% while also increasing the task success rate by up to 4.2 percent points.

Method: Proposes a Safety-by-Design method to define ODDs a posteriori from previously collected data using multi-dimensional kernel-based representation. Validated through Monte Carlo methods and a real-world aviation use case for collision-avoidance systems.

Result: The method enables data-driven ODD definition that can equal the original hidden ODD of the data. Demonstrated through validation with Monte Carlo simulations and a practical aviation safety application.

Conclusion: The kernel-based ODD approach enables future certification of data-driven, safety-critical AI-based systems by providing a systematic way to define operational domains from collected data.

Abstract: Artificial Intelligence (AI) has been on the rise in many domains, including numerous safety-critical applications. However, for complex systems found in the real world, or when data already exist, defining the underlying environmental conditions is extremely challenging. This often results in an incomplete description of the environment in which the AI-based system must operate. Nevertheless, this description, called the Operational Design Domain (ODD), is required in many domains for the certification of AI-based systems. Traditionally, the ODD is created in the early stages of the development process, drawing on sophisticated expert knowledge and related standards. This paper presents a novel Safety-by-Design method to a posteriori define the ODD from previously collected data using a multi-dimensional kernel-based representation. This approach is validated through both Monte Carlo methods and a real-world aviation use case for a future safety-critical collision-avoidance system. Moreover, by defining under what conditions two ODDs are equal, the paper shows that the data-driven ODD can equal the original, underlying hidden ODD of the data. Utilizing the novel, Safe-by-Design kernel-based ODD enables future certification of data-driven, safety-critical AI-based systems.

Method: RTL (Routing the Lottery) discovers multiple specialized subnetworks (adaptive tickets) tailored to different data classes, semantic clusters, or environmental conditions through adaptive pruning.

Result: RTL consistently outperforms single- and multi-model baselines in balanced accuracy and recall, uses up to 10x fewer parameters than independent models, and exhibits semantically aligned subnetworks. Also identifies subnetwork collapse under aggressive pruning.

Conclusion: Pruning can be recast as a mechanism for aligning model structure with data heterogeneity, paving the way toward more modular and context-aware deep learning.

Abstract: In pruning, the Lottery Ticket Hypothesis posits that large networks contain sparse subnetworks, or winning tickets, that can be trained in isolation to match the performance of their dense counterparts. However, most existing approaches assume a single universal winning ticket shared across all inputs, ignoring the inherent heterogeneity of real-world data. In this work, we propose Routing the Lottery (RTL), an adaptive pruning framework that discovers multiple specialized subnetworks, called adaptive tickets, each tailored to a class, semantic cluster, or environmental condition. Across diverse datasets and tasks, RTL consistently outperforms single- and multi-model baselines in balanced accuracy and recall, while using up to 10 times fewer parameters than independent models and exhibiting semantically aligned. Furthermore, we identify subnetwork collapse, a performance drop under aggressive pruning, and introduce a subnetwork similarity score that enables label-free diagnosis of oversparsification. Overall, our results recast pruning as a mechanism for aligning model structure with data heterogeneity, paving the way toward more modular and context-aware deep learning.

Method: Introduces SMB-Structure, a world model for structured EHR that grounds a joint-embedding prediction architecture (JEPA) with next-token prediction (SFT). SFT reconstructs future patient states in token space, while JEPA predicts futures in latent space from initial patient representation alone.

Result: Validated across two large-scale cohorts (MSK oncology and INSPECT pulmonary embolism). Linear probe evaluation shows embeddings capture disease dynamics not recoverable by autoregressive baselines, achieving competitive performance on complex tasks with high patient heterogeneity.

Conclusion: The training paradigm learns embeddings that capture disease dynamics, enabling SMB-Structure to simulate patient trajectories rather than just predict tokens, with model weights publicly available.

Abstract: Large language models (LLMs) trained with next-word-prediction have achieved success as clinical foundation models. Representations from these language backbones yield strong linear probe performance across biomedical tasks, suggesting that patient semantics emerge from next-token prediction at scale. However, this paradigm treats patients as a document to be summarized rather than a dynamical system to be simulated; a patient’s trajectory emerges from their state evolving under interventions and time, requiring models that simulate dynamics rather than predict tokens. To address this, we introduce SMB-Structure, a world model for structured EHR that grounds a joint-embedding prediction architecture (JEPA) with next-token prediction (SFT). SFT grounds our model to reconstruct future patient states in token space, while JEPA predicts those futures in latent space from the initial patient representation alone, forcing trajectory dynamics to be encoded before the next state is observed. We validate across two large-scale cohorts: Memorial Sloan Kettering (23,319 oncology patients; 323,000+ patient-years) and INSPECT (19,402 pulmonary embolism patients). Using a linear probe evaluated at multiple points along the disease trajectory, we demonstrate that our training paradigm learns embeddings that capture disease dynamics not recoverable by autoregressive baselines, enabling SMB-Structure to achieve competitive performance on complex tasks characterized by high patient heterogeneity. Model weights are available at https://huggingface.co/standardmodelbio/SMB-v1-1.7B-Structure.

Method: Created World of Workflows (WoW) - a ServiceNow-based environment with 4,000+ business rules and 55 workflows, plus WoW-bench with 234 tasks to evaluate agentic task completion and dynamics modeling.

Result: Frontier LLMs suffer from dynamics blindness (failing to predict cascading side effects) and need grounded world modeling to bridge observability gaps in opaque systems.

Conclusion: Enterprise agents require explicit learning of system dynamics for reliability, motivating a new paradigm beyond current LLM capabilities.

Abstract: Frontier large language models (LLMs) excel as autonomous agents in many domains, yet they remain untested in complex enterprise systems where hidden workflows create cascading effects across interconnected databases. Existing enterprise benchmarks evaluate surface-level agentic task completion similar to general consumer benchmarks, ignoring true challenges in enterprises, such as limited observability, large database state, and hidden workflows with cascading side effects. We introduce World of Workflows (WoW), a realistic ServiceNow-based environment incorporating 4,000+ business rules and 55 active workflows embedded in the system, alongside WoW-bench, a benchmark of 234 tasks evaluating constrained agentic task completion and enterprise dynamics modeling capabilities. We reveal two major takeaways: (1) Frontier LLMs suffer from dynamics blindness, consistently failing to predict the invisible, cascading side effects of their actions, which leads to silent constraint violations, and (2) reliability in opaque systems requires grounded world modeling, where agents must mentally simulate hidden state transitions to bridge the observability gap when high-fidelity feedback is unavailable. For reliable and useful enterprise agents, WoW motivates a new paradigm to explicitly learn system dynamics. We release our GitHub for setting up and evaluating WoW.

Method: Proposes EndoAgent with dual-memory design (short-term action tracking and long-term experiential learning) that integrates expert-designed tools within a unified reasoning loop. Also introduces EndoAgentBench benchmark with 5,709 visual question-answer pairs.

Result: EndoAgent consistently outperforms both general and medical multimodal models, demonstrating strong flexibility and reasoning capabilities in endoscopic analysis.

Conclusion: EndoAgent represents the first memory-guided agent for vision-to-decision endoscopic analysis, showing promise for developing general AI systems to support endoscopic image diagnosis.

Abstract: Developing general artificial intelligence (AI) systems to support endoscopic image diagnosis is an emerging research priority. Existing methods based on large-scale pretraining often lack unified coordination across tasks and struggle to handle the multi-step processes required in complex clinical workflows. While AI agents have shown promise in flexible instruction parsing and tool integration across domains, their potential in endoscopy remains underexplored. To address this gap, we propose EndoAgent, the first memory-guided agent for vision-to-decision endoscopic analysis that integrates iterative reasoning with adaptive tool selection and collaboration. Built on a dual-memory design, it enables sophisticated decision-making by ensuring logical coherence through short-term action tracking and progressively enhancing reasoning acuity through long-term experiential learning. To support diverse clinical tasks, EndoAgent integrates a suite of expert-designed tools within a unified reasoning loop. We further introduce EndoAgentBench, a benchmark of 5,709 visual question-answer pairs that assess visual understanding and language generation capabilities in realistic scenarios. Extensive experiments show that EndoAgent consistently outperforms both general and medical multimodal models, exhibiting its strong flexibility and reasoning capabilities.

Method: Proposed SafeSearch framework: automated, scalable, cost-efficient, lightweight red-teaming. Generated 300 test cases across five risk categories (misinformation, prompt injection, etc.). Evaluated three search agent scaffolds across 17 representative LLMs.

Result: Revealed substantial vulnerabilities in LLM-based search agents, with highest attack success rate reaching 90.5% for GPT-4.1-mini in search-workflow settings. Common defenses like reminder prompting offered limited protection.

Conclusion: SafeSearch provides practical way to measure and improve safety of LLM-based search agents. Framework enables systematic safety evaluation, revealing significant vulnerabilities that current defenses inadequately address.

Abstract: Search agents connect LLMs to the Internet, enabling them to access broader and more up-to-date information. However, this also introduces a new threat surface: unreliable search results can mislead agents into producing unsafe outputs. Real-world incidents and our two in-the-wild observations show that such failures can occur in practice. To study this threat systematically, we propose SafeSearch, an automated red-teaming framework that is scalable, cost-efficient, and lightweight, enabling harmless safety evaluation of search agents. Using this, we generate 300 test cases spanning five risk categories (e.g., misinformation and prompt injection) and evaluate three search agent scaffolds across 17 representative LLMs. Our results reveal substantial vulnerabilities in LLM-based search agents, with the highest ASR reaching 90.5% for GPT-4.1-mini in a search-workflow setting. Moreover, we find that common defenses, such as reminder prompting, offer limited protection. Overall, SafeSearch provides a practical way to measure and improve the safety of LLM-based search agents. Our codebase and test cases are publicly available: https://github.com/jianshuod/SafeSearch.

Method: SQuID (Survey and Questionnaire Item Embeddings Differentials) processes LLM embeddings to recover psychometric structures, comparing multiple embedding models using internal consistency, dimension correlations, and multidimensional scaling configurations.

Result: Explains 55% of variance in dimension-dimension similarities compared to human data, shows alignment with pooled human data from 49 countries, and generalizes across three personality inventories (IPIP, BFI-2, HEXACO).

Conclusion: Semantic embeddings can effectively replicate psychometric structures with cost, scalability, and flexibility advantages while maintaining comparable quality to traditional human survey methods.

Abstract: We demonstrate that embeddings derived from large language models, when processed with “Survey and Questionnaire Item Embeddings Differentials” (SQuID), can recover the structure of human values obtained from human rater judgments on the Revised Portrait Value Questionnaire (PVQ-RR). We compare multiple embedding models across a number of evaluation metrics including internal consistency, dimension correlations and multidimensional scaling configurations. Unlike previous approaches, SQuID addresses the challenge of obtaining negative correlations between dimensions without requiring domain-specific fine-tuning or training data re-annotation. Quantitative analysis reveals that our embedding-based approach explains 55% of variance in dimension-dimension similarities compared to human data. Multidimensional scaling configurations show alignment with pooled human data from 49 different countries. Generalizability tests across three personality inventories (IPIP, BFI-2, HEXACO) demonstrate that SQuID consistently increases correlation ranges, suggesting applicability beyond value theory. These results show that semantic embeddings can effectively replicate psychometric structures previously established through extensive human surveys. The approach offers substantial advantages in cost, scalability and flexibility while maintaining comparable quality to traditional methods. Our findings have significant implications for psychometrics and social science research, providing a complementary methodology that could expand the scope of human behavior and experience represented in measurement tools.

Method: Formal analysis comparing the computational properties of both reasoning approaches, examining their parallel computation efficiency, sequential constraints, and capabilities for approximate counting and sampling through stochastic decoding.

Result: Latent thought reasoning admits more efficient parallel computation than inherently sequential CoT, while CoT enables approximate counting and sampling through stochastic decoding. These computational separations suggest different task suitability for each paradigm.

Conclusion: The analysis provides practical guidance for choosing between reasoning paradigms based on task requirements: depth-driven recursion is more suitable for certain tasks, helping practitioners select appropriate reasoning approaches for different applications.

Abstract: Chain of thought (CoT) elicits reasoning in large language models by explicitly generating intermediate tokens. In contrast, latent thought reasoning operates directly in the continuous latent space, enabling computation beyond discrete linguistic representations. While both approaches exploit iterative computation, their comparative capabilities remain underexplored. In this work, we present a formal analysis showing that latent thought admits more efficient parallel computation than inherently sequential CoT. In contrast, CoT enables approximate counting and sampling through stochastic decoding. These separations suggest the tasks for which depth-driven recursion is more suitable, thereby offering practical guidance for choosing between reasoning paradigms.

Method: The paper introduces Dr. Bench, a multidimensional evaluation framework comprising 214 expert-curated challenging tasks across 10 broad domains, each with manually constructed reference bundles. The framework incorporates metrics for semantic quality, topical focus, and retrieval trustworthiness to comprehensively evaluate long reports generated by DRAs.

Result: Extensive experimentation confirms superior performance of mainstream DRAs over web-search-tool-augmented reasoning models, but reveals considerable scope for further improvement in DRA capabilities.

Conclusion: Dr. Bench provides a robust foundation for capability assessment, architectural refinement, and paradigm advancement of Deep Research Agents, addressing critical gaps in current evaluation methodologies for these advanced AI systems.

Abstract: As an embodiment of intelligence evolution toward interconnected architectures, Deep Research Agents (DRAs) systematically exhibit the capabilities in task decomposition, cross-source retrieval, multi-stage reasoning, information integration, and structured output, which markedly enhance performance on complex and open-ended tasks. However, existing benchmarks remain deficient in evaluation dimensions, response format, and scoring mechanisms, limiting their effectiveness in assessing such agents. This paper introduces Dr. Bench, a multidimensional evaluation framework tailored to DRAs and long-form report-style responses. The benchmark comprises 214 expert-curated challenging tasks across 10 broad domains, each accompanied by manually constructed reference bundles to support composite evaluation. This framework incorporates metrics for semantic quality, topical focus, and retrieval trustworthiness, enabling a comprehensive evaluation of long reports generated by DRAs. Extensive experimentation confirms the superior performance of mainstream DRAs over web-search-tool-augmented reasoning models, yet reveals considerable scope for further improvement. This study provides a robust foundation for capability assessment, architectural refinement, and paradigm advancement of DRAs.

Method: Combines modular Universal Value Function Approximator with hindsight learning for diverse goal achievement, plus automated curriculum learning that focuses on goals maximizing absolute learning progress.

Result: Agents successfully self-organize developmental curriculum, sequentially tackling goals of increasing complexity while revisiting forgotten goals, showing robustness to distractions, forgetting, and body changes.

Conclusion: CURIOUS enables effective autonomous learning in open-ended environments through intrinsic motivation and adaptive curriculum building.

Abstract: In open-ended environments, autonomous learning agents must set their own goals and build their own curriculum through an intrinsically motivated exploration. They may consider a large diversity of goals, aiming to discover what is controllable in their environments, and what is not. Because some goals might prove easy and some impossible, agents must actively select which goal to practice at any moment, to maximize their overall mastery on the set of learnable goals. This paper proposes CURIOUS, an algorithm that leverages 1) a modular Universal Value Function Approximator with hindsight learning to achieve a diversity of goals of different kinds within a unique policy and 2) an automated curriculum learning mechanism that biases the attention of the agent towards goals maximizing the absolute learning progress. Agents focus sequentially on goals of increasing complexity, and focus back on goals that are being forgotten. Experiments conducted in a new modular-goal robotic environment show the resulting developmental self-organization of a learning curriculum, and demonstrate properties of robustness to distracting goals, forgetting and changes in body properties.

Method: Adapts Intrinsically Motivated Goal Exploration Processes (IMGEPs) to explore diverse Flow-Lenia environments using simulation-wide metrics like evolutionary activity, compression-based complexity, and multi-scale entropy. Combines automated discovery with interactive exploration tools for human-AI collaboration.

Result: The method discovers significantly more diverse dynamics compared to random search, enabling self-organization of complex collective behaviors not captured by previous individual pattern search approaches.

Conclusion: Provides a framework for automated discovery of emergent collective properties in parameterizable complex systems, demonstrated with Flow-Lenia but potentially applicable to other systems where understanding system-level dynamics is important.

Abstract: We present a method for the automated discovery of system-level dynamics in Flow-Lenia–a continuous cellular automaton (CA) with mass conservation and parameter localization-using a curiosity–driven AI scientist. This method aims to uncover processes leading to self-organization of evolutionary and ecosystemic dynamics in CAs. We build on previous work which uses diversity search algorithms in Lenia to find self-organized individual patterns, and extend it to large environments that support distinct interacting patterns. We adapt Intrinsically Motivated Goal Exploration Processes (IMGEPs) to drive exploration of diverse Flow-Lenia environments using simulation-wide metrics, such as evolutionary activity, compression-based complexity, and multi-scale entropy. We test our method in two experiments, showcasing its ability to illuminate significantly more diverse dynamics compared to random search. We show qualitative results illustrating how ecosystemic simulations enable self-organization of complex collective behaviors not captured by previous individual pattern search and analysis. We complement automated discovery with an interactive exploration tool, creating an effective human-AI collaborative workflow for scientific investigation. Though demonstrated specifically with Flow-Lenia, this methodology provides a framework potentially applicable to other parameterizable complex systems where understanding emergent collective properties is of interest.

Method: Proposes PathWise, a multi-agent reasoning framework that formulates heuristic generation as a sequential decision process over an entailment graph serving as compact, stateful memory. Uses policy agent for planning evolutionary actions, world model agent for generating heuristic rollouts, and critic agents for providing routed reflections.

Result: Experiments across diverse combinatorial optimization problems show PathWise converges faster to better heuristics, generalizes across different LLM backbones, and scales to larger problem sizes.

Conclusion: PathWise shifts LLM-based automated heuristic design from trial-and-error evolution toward state-aware planning through reasoning, demonstrating improved performance and scalability.

Abstract: Large Language Models (LLMs) have enabled automated heuristic design (AHD) for combinatorial optimization problems (COPs), but existing frameworks’ reliance on fixed evolutionary rules and static prompt templates often leads to myopic heuristic generation, redundant evaluations, and limited reasoning about how new heuristics should be derived. We propose a novel multi-agent reasoning framework, referred to as Planning through World Model for Automated Heuristic Design via Self-Evolving LLMs (PathWise), which formulates heuristic generation as a sequential decision process over an entailment graph serving as a compact, stateful memory of the search trajectory. This approach allows the system to carry forward past decisions and reuse or avoid derivation information across generations. A policy agent plans evolutionary actions, a world model agent generates heuristic rollouts conditioned on those actions, and critic agents provide routed reflections summarizing lessons from prior steps, shifting LLM-based AHD from trial-and-error evolution toward state-aware planning through reasoning. Experiments across diverse COPs show that PathWise converges faster to better heuristics, generalizes across different LLM backbones, and scales to larger problem sizes.

Method: Introduces Theory of Agent (ToA) framework where agents make sequential decisions about whether to resolve uncertainty internally or delegate externally. This provides a normative criterion for tool use based on epistemic necessity rather than just task success.

Result: The framework explains common agent failure modes (overthinking, overacting) as miscalibrated decisions under uncertainty rather than deficiencies in reasoning or tool execution alone. It establishes epistemic necessity as a criterion for justified tool use.

Conclusion: Agents should invoke tools only when epistemically necessary to avoid inefficiency and impediment of internal reasoning development. This normative criterion complements decision-theoretic models and is essential for building increasingly intelligent agents.

Abstract: As large language models evolve into tool-augmented agents, a central question remains unresolved: when is external tool use actually justified? Existing agent frameworks typically treat tools as ordinary actions and optimize for task success or reward, offering little principled distinction between epistemically necessary interaction and unnecessary delegation. This position paper argues that agents should invoke external tools only when epistemically necessary. Here, epistemic necessity means that a task cannot be completed reliably via the agent’s internal reasoning over its current context, without any external interaction. We introduce the Theory of Agent (ToA), a framework that treats agents as making sequential decisions about whether remaining uncertainty should be resolved internally or delegated externally. From this perspective, common agent failure modes (e.g., overthinking and overacting) arise from miscalibrated decisions under uncertainty rather than deficiencies in reasoning or tool execution alone. We further discuss implications for training, evaluation, and agent design, highlighting that unnecessary delegation not only causes inefficiency but can impede the development of internal reasoning capability. Our position provides a normative criterion for tool use that complements existing decision-theoretic models and is essential for building agents that are not only correct, but increasingly intelligent.

Method: WorldLLM combines Bayesian inference and autonomous active exploration with reinforcement learning. It uses LLMs’ in-context learning to guide world model predictions via natural language hypotheses, which are iteratively refined through Bayesian inference with a second LLM as proposal distribution. Evidence is collected using curiosity-driven RL policy that explores environments to find low-likelihood transitions.

Result: Experiments in textual game environment requiring object manipulation and combination show enhanced predictive accuracy and generation of human-interpretable theories of environment dynamics.

Conclusion: WorldLLM framework effectively improves LLM-based world modeling by autonomously driving continual improvement of predictions through alternating hypothesis refinement and evidence collection.

Abstract: Large Language Models (LLMs) possess general world knowledge but often struggle to generate precise predictions in structured, domain-specific contexts such as simulations. These limitations arise from their inability to ground their broad, unstructured understanding in specific environments. To address this, we present WorldLLM, a framework that enhances LLM-based world modeling by combining Bayesian inference and autonomous active exploration with reinforcement learning. WorldLLM leverages the in-context learning abilities of LLMs to guide an LLM-based world model’s predictions using natural language hypotheses given in its prompt. These hypotheses are iteratively refined through a Bayesian inference framework that leverages a second LLM as the proposal distribution given collected evidence. This evidence is collected using a curiosity-driven reinforcement learning policy that explores the environment to find transitions with a low log-likelihood under our LLM-based predictive model using the current hypotheses. By alternating between refining hypotheses and collecting new evidence, our framework autonomously drives continual improvement of the predictions. Our experiments demonstrate the effectiveness of WorldLLM in a textual game environment that requires agents to manipulate and combine objects. The framework not only enhances predictive accuracy, but also generates human-interpretable theories of environment dynamics.

Method: GenOM uses LLMs to generate textual definitions for ontology concepts to enrich semantic representations, employs embedding models to retrieve alignment candidates, and incorporates exact matching-based tools to improve precision. It leverages few-shot prompting for better adaptation.

Result: Experiments on OAEI Bio-ML track show GenOM achieves competitive performance, surpassing traditional OM systems and recent LLM-based methods. Ablation studies confirm the effectiveness of semantic enrichment and few-shot prompting.

Conclusion: GenOM demonstrates robustness and adaptability in biomedical ontology matching through semantic enrichment and LLM integration, offering improved alignment precision over existing approaches.

Abstract: Ontology matching (OM) plays an essential role in enabling semantic interoperability and integration across heterogeneous knowledge sources, particularly in the biomedical domain which contains numerous complex concepts related to diseases and pharmaceuticals. This paper introduces GenOM, a large language model (LLM)-based ontology alignment framework, which enriches the semantic representations of ontology concepts via generating textual definitions, retrieves alignment candidates with an embedding model, and incorporates exact matching-based tools to improve precision. Extensive experiments conducted on the OAEI Bio-ML track demonstrate that GenOM can often achieve competitive performance, surpassing many baselines including traditional OM systems and recent LLM-based methods. Further ablation studies confirm the effectiveness of semantic enrichment and few-shot prompting, highlighting the framework’s robustness and adaptability.

Method: Domain-agnostic, agentic AI Scientist system that independently handles hypothesis generation, experimental design, online data collection (288 participants), analysis pipeline development through extended coding sessions, and manuscript preparation for psychological studies on visual working memory, mental rotation, and imagery vividness.

Result: System successfully conducted three psychological studies with theoretical reasoning and methodological rigor comparable to experienced researchers, demonstrating capability for non-trivial research, though with limitations in conceptual nuance and theoretical interpretation.

Conclusion: AI scientific discovery pipelines can autonomously explore scientific spaces that human constraints might leave unexplored, representing a step toward embodied AI that tests hypotheses through real-world experiments, while raising questions about scientific understanding and credit attribution.

Abstract: Artificial intelligence systems are transforming scientific discovery by accelerating specific research tasks, from protein structure prediction to materials design, yet remain confined to narrow domains requiring substantial human oversight. The exponential growth of scientific literature and increasing domain specialisation constrain researchers’ capacity to synthesise knowledge across disciplines and develop unifying theories, motivating exploration of more general-purpose AI systems for science. Here we show that a domain-agnostic, agentic AI Scientist system can independently navigate the scientific workflow - from hypothesis generation through data collection to manuscript preparation. The system autonomously designed and executed three psychological studies on visual working memory, mental rotation, and imagery vividness, executed one new online data collection with 288 participants, developed analysis pipelines through 8-hour+ continuous coding sessions, and produced completed manuscripts. The results demonstrate the capability of AI scientific discovery pipelines to conduct non-trivial research with theoretical reasoning and methodological rigour comparable to experienced researchers, though with limitations in conceptual nuance and theoretical interpretation. This is a step toward embodied AI that can test hypotheses through real-world experiments, accelerating discovery by autonomously exploring regions of scientific space that human cognitive and resource constraints might otherwise leave unexplored. It raises important questions about the nature of scientific understanding and the attribution of scientific credit.

Method: Developed a novel benchmark with decision scenarios across four cities using the Capability Approach framework. Created an automated pipeline connecting LLM-recommended policies to agent-based modeling in one location to compare social impact against expert recommendations.

Result: LLMs show variation in policy recommendations compared to local experts, but demonstrate potential benefits for policymaking insights when paired with responsible guardrails, contextual calibrations, and local domain expertise.

Conclusion: LLMs can provide valuable insights for homelessness alleviation policymaking but require careful integration with domain expertise and ethical frameworks; the work operationalizes the Capability Approach computationally and offers new perspectives on human dignity-focused policymaking.

Abstract: Large language models (LLMs) are increasingly being adopted in high-stakes domains. Their potential to encode evolving social contexts and to generate plausible scenarios position them as promising tools in social policymaking. This article evaluates whether LLMs are aligned with domain experts (and among themselves) on policy recommendations to alleviate homelessness - a challenge affecting over 150 million people worldwide. We develop a novel benchmark comprised of decision scenarios across four cities, with policy choices that are grounded in the conceptual framework of the Capability Approach for human development. We also present an automated pipeline that connects the policies to an agent-based model in one location, and compare the social impact of the policies recommended by LLMs to those recommended by experts. Our exploratory analysis reveals variation across LLMs in their policy recommendations compared to local experts, yet suggests potential benefits of the use of LLMs to provide insights for policymaking, if paired with responsible guardrails, contextual calibrations, and local domain expertise. Our work operationalizes the Capability Approach in a computational framework and provides new insights on homelessness alleviation policymaking with a focus on human dignity.

Method: Two-stage modular system: Stage 1 uses structured prompting with entropy-based adaptive sampling to construct diverse agent pool; Stage 2 employs L1-regularized regression to select compact ensemble whose aggregate responses align with observed population data

Result: Validated on 14 waves of American Trends Panel with average test MSE of 0.014 across diverse topics at ~$0.80 per survey; also tested on World Values Survey showing cross-locale generalization; outperforms SFT-aligned baseline using <3% of training data

Conclusion: P2P provides cost-effective, privacy-preserving method for constructing LLM-based proxies that accurately reflect human population preferences without fine-tuning or sensitive data access

Abstract: Large language models are increasingly used as proxies for human subjects in social science research, yet external validity requires that synthetic agents faithfully reflect the preferences of target human populations. We introduce preference reconstruction theory, a framework that formalizes preference alignment as a representation learning problem: constructing a functional basis of proxy agents and recovering population preferences through weighted aggregation. We implement this via Prompts to Proxies ($\texttt{P2P}$), a modular two-stage system. Stage 1 uses structured prompting with entropy-based adaptive sampling to construct a diverse agent pool spanning the latent preference space. Stage 2 employs L1-regularized regression to select a compact ensemble whose aggregate response distributions align with observed data from the target population. $\texttt{P2P}$ requires no finetuning and no access to sensitive demographic data, incurring only API inference costs. We validate the approach on 14 waves of the American Trends Panel, achieving an average test MSE of 0.014 across diverse topics at approximately 0.8 USD per survey. We additionally test it on the World Values Survey, demonstrating its potential to generalize across locales. When stress-tested against an SFT-aligned baseline, $\texttt{P2P}$ achieves competitive performance using less than 3% of the training data.

Method: Proposes DeFacto framework with three complementary training paradigms: (1) positive training, (2) counterfactual training, and (3) random-masking training. Develops a pipeline to automatically localize question-relevant evidence and construct positive, counterfactual, and random variants, creating a 100k image dataset. Trains models with GRPO-based reinforcement learning using three complementary rewards to guide accurate answering and evidence-grounded reasoning.

Result: Experiments on diverse benchmarks show DeFacto substantially improves both answer accuracy and reasoning faithfulness, establishing a stronger foundation for interpretable multimodal reasoning.

Conclusion: DeFacto successfully addresses the reasoning fidelity problem in multimodal language models by enforcing both accurate answering and faithful reasoning through counterfactual training paradigms, leading to more interpretable and reliable multimodal reasoning.

Abstract: Recent advances in multimodal language models (MLLMs) have achieved remarkable progress in vision-language reasoning, especially with the emergence of “thinking with images,” which integrates explicit visual steps into the reasoning process. While this paradigm strengthens image-based reasoning, a significant challenge remains: models may arrive at correct answers by relying on irrelevant or spurious regions, driven by prior knowledge or dataset biases. Even when the answer is correct, flawed reasoning indicates that the model has not truly understood the image, highlighting the critical importance of reasoning fidelity in multimodal tasks. To address this issue, we propose DeFacto, a counterfactual reasoning framework that jointly enforces accurate answering and faithful reasoning. A key component of our approach is the design of three complementary training paradigms: (i) positive, (ii) counterfactual, and (iii) random-masking. To enable these paradigms, we develop a pipeline that automatically localizes question-relevant evidence and constructs positive, counterfactual, and random variants, resulting in a dataset of about 100k images. Building on this framework, we train multimodal language models with GRPO-based reinforcement learning, where we design three complementary rewards to guide the model toward accurate answering and evidence-grounded reasoning. Experiments on diverse benchmarks demonstrate that DeFacto substantially improves both answer accuracy and reasoning faithfulness, establishing a stronger foundation for interpretable multimodal reasoning. The code is available on GitHub and the dataset is released on HuggingFace.

Method: Proposes Preference-Based Reward Repair (PBRR): an iterative framework that learns an additive, transition-dependent correction term to fix proxy reward functions. Uses targeted exploration and a new preference-learning objective to identify critical transitions needing correction.

Result: PBRR has cumulative regret matching prior preference-based RL methods in tabular domains. On reward-hacking benchmarks, it outperforms baselines learning from scratch or modifying proxy rewards, requiring substantially fewer preferences.

Conclusion: PBRR provides an efficient alternative to learning reward functions from scratch, effectively repairing misaligned proxy rewards with fewer human preferences while maintaining theoretical guarantees.

Abstract: Human-designed reward functions for reinforcement learning (RL) agents are frequently misaligned with the humans’ true, unobservable objectives, and thus act only as proxies. Optimizing for a misspecified proxy reward function often induces reward hacking, resulting in a policy misaligned with the human’s true objectives. An alternative is to perform RL from human feedback, which involves learning a reward function from scratch by collecting human preferences over pairs of trajectories. However, building such datasets is costly. To address the limitations of both approaches, we propose Preference-Based Reward Repair (PBRR): an automated iterative framework that repairs a human-specified proxy reward function by learning an additive, transition-dependent correction term from preferences. A manually specified reward function can yield policies that are highly suboptimal under the ground-truth objective, yet corrections on only a few transitions may suffice to recover optimal performance. To identify and correct for those transitions, PBRR uses a targeted exploration strategy and a new preference-learning objective. We prove in tabular domains PBRR has a cumulative regret that matches, up to constants, that of prior preference-based RL methods. In addition, on a suite of reward-hacking benchmarks, PBRR consistently outperforms baselines that learn a reward function from scratch from preferences or modify the proxy reward function using other approaches, requiring substantially fewer preferences to learn high performing policies.

Method: Proposes a Socratic collaboration paradigm where agent actively probes for information. Uses information theory framework with information gain (reduction of Shannon entropy) as intrinsic reward signal. Trains agent (Nous) via automated simulation pipeline generating large-scale preference-based dataset for scientific diagram generation.

Result: Comprehensive experiments show Nous achieves leading efficiency and output quality, robust to varying user expertise levels. Avoids reliance on costly human preference annotations or external reward models.

Conclusion: Provides systematic methodology and new perspective for addressing ambiguous intentions in complex human-machine collaboration through active inquiry-based approach.

Abstract: A fundamental bottleneck in human-AI collaboration is the ``intention expression gap," the difficulty for humans to effectively convey complex, high-dimensional thoughts to AI. This challenge often traps users in inefficient trial-and-error loops and is exacerbated by the diverse expertise levels of users. We reframe this problem from passive instruction following to a Socratic collaboration paradigm, proposing an agent that actively probes for information to resolve its uncertainty about user intent. we name the proposed agent Nous, trained to acquire proficiency in this inquiry policy. The core mechanism of Nous is a training framework grounded in the first principles of information theory. Within this framework, we define the information gain from dialogue as an intrinsic reward signal, which is fundamentally equivalent to the reduction of Shannon entropy over a structured task space. This reward design enables us to avoid reliance on costly human preference annotations or external reward models. To validate our framework, we develop an automated simulation pipeline to generate a large-scale, preference-based dataset for the challenging task of scientific diagram generation. Comprehensive experiments, including ablations, subjective and objective evaluations, and tests across user expertise levels, demonstrate the effectiveness of our proposed framework. Nous achieves leading efficiency and output quality, while remaining robust to varying user expertise. In conclusion, our research provides a systematic methodology and a new perspective for addressing the issue of ambiguous intentions in complex human-machine collaboration.

Method: SofT-GRPO injects Gumbel noise into logits, uses Gumbel-Softmax to keep soft tokens within pre-trained embedding space, and leverages reparameterization trick in policy gradient. Tested on LLMs from 1.5B to 7B parameters.

Result: SofT-GRPO enables soft-thinking LLMs to slightly outperform discrete-token GRPO on Pass@1 (+0.13% average accuracy) and show substantial improvement on Pass@32 (+2.19% average accuracy).

Conclusion: The proposed SofT-GRPO successfully unlocks the potential of soft-thinking reasoning by enabling effective RL-based policy optimization, demonstrating superior performance over discrete-token approaches.

Abstract: The soft-thinking paradigm for Large Language Model (LLM) reasoning can outperform the conventional discrete-token Chain-of-Thought (CoT) reasoning in some scenarios, underscoring its research and application value. However, while the discrete-token CoT reasoning pattern can be reinforced through policy optimization algorithms such as group relative policy optimization (GRPO), extending the soft-thinking pattern with Reinforcement Learning (RL) remains challenging. This difficulty stems from the complexities of injecting stochasticity into soft-thinking tokens and updating soft-thinking policies accordingly. As a result, previous attempts to combine soft-thinking with GRPO typically underperform their discrete-token GRPO counterparts. To fully unlock the potential of soft-thinking, this paper presents a novel policy optimization algorithm, SofT-GRPO, to reinforce LLMs under the soft-thinking reasoning pattern. SofT-GRPO injects the Gumbel noise into logits, employs the Gumbel-Softmax technique to avoid soft-thinking tokens outside the pre-trained embedding space, and leverages the reparameterization trick in policy gradient. We conduct experiments across base LLMs ranging from 1.5B to 7B parameters, and results demonstrate that SofT-GRPO enables soft-thinking LLMs to slightly outperform discrete-token GRPO on Pass@1 (+0.13% on average accuracy), while exhibiting a substantial uplift on Pass@32 (+2.19% on average accuracy). Codes and weights are available on https://github.com/zz1358m/SofT-GRPO-master

Method: Proposes CMM framework that decouples LLM features across three dimensions (layer, task, data) and uses multiple student models to collaboratively learn different feature aspects. Introduces Hájek-MoE to integrate student model outputs by analyzing contributions in a kernel function-generated common feature space.

Result: Extensive experiments on four real-world market datasets show CMM outperforms current distillation methods and RL-based market-making strategies, demonstrating superior performance.

Conclusion: CMM provides an effective knowledge distillation framework for market making that addresses LLM inference speed limitations while maintaining performance, offering a practical solution for deploying LLM capabilities in financial trading.

Abstract: Market making (MM) through Reinforcement Learning (RL) has attracted significant attention in financial trading. With the development of Large Language Models (LLMs), more and more attempts are being made to apply LLMs to financial areas. A simple, direct application of LLM as an agent shows significant performance. Such methods are hindered by their slow inference speed, while most of the current research has not studied LLM distillation for this specific task. To address this, we first propose the normalized fluorescent probe to study the mechanism of the LLM’s feature. Based on the observation found by our investigation, we propose Cooperative Market Making (CMM), a novel framework that decouples LLM features across three orthogonal dimensions: layer, task, and data. Various student models collaboratively learn simple LLM features along with different dimensions, with each model responsible for a distinct feature to achieve knowledge distillation. Furthermore, CMM introduces an Hájek-MoE to integrate the output of the student models by investigating the contribution of different models in a kernel function-generated common feature space. Extensive experimental results on four real-world market datasets demonstrate the superiority of CMM over the current distillation method and RL-based market-making strategies.

Method: GAC decomposes action generation into a direction vector and learnable concentration parameter, enabling efficient interpolation between deterministic actions and uniform spherical noise. This reduces parameters from 2d to d+1 and avoids O(dk) vMF rejection sampling complexity with simple O(d) operations.

Result: GAC consistently matches or exceeds state-of-the-art methods across six MuJoCo benchmarks, achieving 37.6% improvement over SAC on Ant-v4 and up to 112% on complex DMControl tasks. Ablation studies show both spherical normalization and adaptive concentration control are essential.

Conclusion: Robust and efficient continuous control doesn’t require complex distributions but principled respect for action space geometry. GAC demonstrates that spherical distributions with simplified computation can outperform traditional approaches.

Abstract: Gaussian policies have dominated continuous control in deep reinforcement learning (RL), yet they suffer from a fundamental mismatch: their unbounded support requires ad-hoc squashing functions that distort the geometry of bounded action spaces. While von Mises-Fisher (vMF) distributions offer a theoretically grounded alternative on the sphere, their reliance on Bessel functions and rejection sampling hinders practical adoption. We propose \textbf{Geometric Action Control (GAC)}, a novel action generation paradigm that preserves the geometric benefits of spherical distributions while \textit{simplifying computation}. GAC decomposes action generation into a direction vector and a learnable concentration parameter, enabling efficient interpolation between deterministic actions and uniform spherical noise. This design reduces parameter count from (2d) to (d+1), and avoids the (O(dk)) complexity of vMF rejection sampling, achieving simple (O(d)) operations. Empirically, GAC consistently matches or exceeds state-of-the-art methods across six MuJoCo benchmarks, achieving 37.6% improvement over SAC on Ant-v4 and up to 112% on complex DMControl tasks, demonstrating strong performance across diverse benchmarks. Our ablation studies reveal that both \textbf{spherical normalization} and \textbf{adaptive concentration control} are essential to GAC’s success. These findings suggest that robust and efficient continuous control does not require complex distributions, but a principled respect for the geometry of action spaces.

Method: Introduces Stateful Reflective Decision Process as a formal model with episodic memory operations (writing for policy evaluation, reading for policy improvement). Develops read-write reflective learning framework integrating retrieval into soft policy iteration.

Result: Establishes convergence guarantees showing that as memory grows and covers state space more densely, the composite policy converges to optimal solution. Empirical findings suggest reflection enables generalized adaptation across open-ended tasks.

Conclusion: Provides rigorous mathematical basis for building reflective, memory-embedded agents capable of continual general-purpose learning, connecting practical memory-based methods with principled reinforcement learning.

Abstract: We present a theoretical study of continual and experiential learning in large language model agents that combine episodic memory with reinforcement learning. We argue that the key mechanism for continual adaptation, without updating model parameters, is reflection: the agent’s ability to use past experience to guide future actions. Empirical findings suggest that episodic, experience-driven reflection enables generalised adaptation across a wide range of open-ended, long-horizon tasks. This indicates that efficient learning can occur during deployment and weakens the traditional separation between training and testing. Motivated by this, we introduce the Stateful Reflective Decision Process, a formal model of reflective memory dynamics. In this abstraction, an agent maintains an episodic memory and performs two core operations. Writing stores interaction outcomes and plays the role of policy evaluation. Reading retrieves relevant past cases to inform decisions and plays the role of policy improvement. This perspective treats reflective memory as a control object that can be analysed using classical reinforcement learning tools. We then develop a read-write reflective learning framework by integrating retrieval into soft policy iteration and establish convergence guarantees. We show that as memory grows and provides denser coverage of the state space, the resulting composite policy converges to the optimal solution. Overall, this framework connects practical memory-based methods with principled reinforcement learning, providing a rigorous mathematical basis for building reflective, memory-embedded agents capable of continual general-purpose learning.

Method: Evolutionary Realistic Instance Synthesis (EvoReal) uses an evolutionary module guided by large language models to generate synthetic instances with structural patterns that statistically mimic real-world instances. Pre-trained NCO models are then refined through progressive adaptation: first aligning with enriched synthetic distributions, then fine-tuning on actual benchmark instances.

Result: EvoReal significantly improves generalization of state-of-the-art neural solvers, reducing performance gap to optimal solutions on TSPLib (1.05%) and CVRPLib (2.71%) benchmarks across various problem scales.

Conclusion: The proposed EvoReal framework effectively bridges the generalization gap in neural combinatorial optimization by generating realistic synthetic instances and enabling progressive model adaptation, demonstrating strong performance on real-world routing benchmarks.

Abstract: Recent advances in Neural Combinatorial Optimization (NCO) methods have significantly improved the capability of neural solvers to handle synthetic routing instances. Nonetheless, existing neural solvers typically struggle to generalize effectively from synthetic, uniformly-distributed training data to real-world VRP scenarios, including widely recognized benchmark instances from TSPLib and CVRPLib. To bridge this generalization gap, we present Evolutionary Realistic Instance Synthesis (EvoReal), which leverages an evolutionary module guided by large language models (LLMs) to generate synthetic instances characterized by diverse and realistic structural patterns. Specifically, the evolutionary module produces synthetic instances whose structural attributes statistically mimics those observed in authentic real-world instances. Subsequently, pre-trained NCO models are progressively refined, firstly aligning them with these structurally enriched synthetic distributions and then further adapting them through direct fine-tuning on actual benchmark instances. Extensive experimental evaluations demonstrate that EvoReal markedly improves the generalization capabilities of state-of-the-art neural solvers, yielding a notable reduced performance gap compared to the optimal solutions on the TSPLib (1.05%) and CVRPLib (2.71%) benchmarks across a broad spectrum of problem scales.

Method: Two-module framework: 1) Hyperdimensional Transformer (HDT) for intent prediction using hyperdimensional vector encoding and symbolic computations, 2) Double Actions MAPPO (DA-MAPPO) for decision-making with two independently parameterized networks for user-intent and trajectory planning.

Result: Superior performance across diverse scenarios on real IoT action dataset with authentic wireless data.

Conclusion: The proposed framework effectively addresses intent prediction and decision-making challenges in AAV-assisted IoT networks through novel hyperdimensional encoding and multi-agent reinforcement learning approaches.

Abstract: Autonomous Aerial Vehicle (AAV)-assisted Internet of Things (IoT) represents a collaborative architecture in which AAV allocate resources over 6G links to jointly enhance user-intent interpretation and overall network performance. Owing to this mutual dependence, improvements in intent inference and policy decisions on one component reinforce the efficiency of others, making highly reliable intent prediction and low-latency action execution essential. Although numerous approaches can model intent relationships, they encounter severe obstacles when scaling to high-dimensional action sequences and managing intensive on-board computation. We propose an Intent-Driven Framework for Autonomous Network Optimization comprising prediction and decision modules. First, implicit intent modeling is adopted to mitigate inaccuracies arising from ambiguous user expressions. For prediction, we introduce Hyperdimensional Transformer (HDT), which embeds data into a Hyperdimensional space via Hyperdimensional vector encoding and replaces standard matrix and attention operations with symbolic Hyperdimensional computations. For decision-making, where AAV must respond to user intent while planning trajectories, we design Double Actions based Multi-Agent Proximal Policy Optimization (DA-MAPPO). Building upon MAPPO, it samples actions through two independently parameterized networks and cascades the user-intent network into the trajectory network to maintain action dependencies. We evaluate our framework on a real IoT action dataset with authentic wireless data. Experimental results demonstrate that HDT and DA-MAPPO achieve superior performance across diverse scenarios.

Method: Novel deterministic solver over state-augmented Disassembly Sequencing Planning (DSP) graph that encodes disassembly history into state to enforce Markov property, enabling optimal recursive evaluation where each decision depends only on previous state.

Result: Demonstrated framework flexibility with hierarchical triage of electric vehicle batteries, showing how unified formalism accommodates varying mechanical complexity, safety requirements, and economic drivers through recursive component valuation.

Conclusion: The unified formalism provides tractable and generalizable foundation for optimizing circular economy triage decisions across diverse products and operational contexts.

Abstract: Circular economy (CE) triage is the assessment of products to determine which sustainable pathway they can follow once they reach the end of their usefulness as they are currently being used. Effective CE triage requires adaptive decisions that balance retained value against the costs and constraints of processing and labour. This paper presents a novel decision-making framework as a simple deterministic solver over a state-augmented Disassembly Sequencing Planning (DSP) graph. By encoding the disassembly history into the state, our framework enforces the Markov property, enabling optimal, recursive evaluation by ensuring each decision only depends on the previous state. The triage decision involves choices between continuing disassembly or committing to a CE option. The model integrates condition-aware utility based on diagnostic health scores and complex operational constraints. We demonstrate the framework’s flexibility with a worked example: the hierarchical triage of electric vehicle (EV) batteries, where decisions are driven by the recursive valuation of components. The example illustrates how a unified formalism enables the accommodation of varying mechanical complexity, safety requirements, and economic drivers. This unified formalism therefore provides a tractable and generalisable foundation for optimising CE triage decisions across diverse products and operational contexts.

Method: TS2R uses segmentation, semantic abstraction, and rule-based interpretation to encode short-term temporal dynamics into natural language reports, bridging low-level sensor signals with high-level contextual insights for LLM processing.

Result: TS2R consistently improves LLM performance across accuracy, robustness, and explainability metrics for anomaly detection, state-of-charge prediction, and charging/discharging management, achieving expert-level decision quality without retraining.

Conclusion: TS2R establishes a practical path for adaptive, LLM-driven battery intelligence by enabling semantic translation of time-series data into structured reports that enhance LLM reasoning capabilities for BESS management.

Abstract: Large language models (LLMs) offer promising capabilities for interpreting multivariate time-series data, yet their application to real-world battery energy storage system (BESS) operation and maintenance remains largely unexplored. Here, we present TimeSeries2Report (TS2R), a semantic translation framework that converts raw lithium-ion battery operational time-series into structured, semantically enriched reports, enabling LLMs to reason, predict, and make decisions in BESS management scenarios. TS2R encodes short-term temporal dynamics into natural language through a combination of segmentation, semantic abstraction, and rule-based interpretation, effectively bridging low-level sensor signals with high-level contextual insights. We benchmark TS2R across both lab-scale and real-world datasets, evaluating report quality and downstream task performance in anomaly detection, state-of-charge prediction, and charging/discharging management. Compared with vision-, embedding-, and text-based prompting baselines, report-based prompting via TS2R consistently improves LLM performance in terms of across accuracy, robustness, and explainability metrics. Notably, TS2R-integrated LLMs achieve expert-level decision quality and predictive consistency without retraining or architecture modification, establishing a practical path for adaptive, LLM-driven battery intelligence.

Method: Theoretical analysis proving an if-and-only-if characterization: when methods observe only aggregate outcomes (aggregate-only observation) and evaluation depends only on submitted artifacts (method-blind evaluation), swapping humans for personas is equivalent to changing the evaluation population. Also defines information-theoretic discriminability of the induced aggregate channel and provides sample-size bounds for reliable method comparison.

Result: Proves that persona simulation is valid under specific conditions (aggregate-only observation and method-blind evaluation). Provides explicit bounds on the number of independent persona evaluations needed to distinguish meaningfully different methods at chosen resolution.

Conclusion: LLM-based persona simulation can serve as a valid alternative to field experiments for benchmarking methods in societal systems under specific conditions, with reliability fundamentally determined by sample size rather than simulation validity.

Abstract: Field experiments (A/B tests) are often the most credible benchmark for methods (algorithms) in societal systems, but their cost and latency bottleneck rapid methodological progress. LLM-based persona simulation offers a cheap synthetic alternative, yet it is unclear whether replacing humans with personas preserves the benchmark interface that adaptive methods optimize against. We prove an if-and-only-if characterization: when (i) methods observe only the aggregate outcome (aggregate-only observation) and (ii) evaluation depends only on the submitted artifact and not on the method’s identity or provenance (method-blind evaluation), swapping humans for personas is just panel change from the method’s point of view, indistinguishable from changing the evaluation population (e.g., New York to Jakarta). Furthermore, we move from validity to usefulness: we define an information-theoretic discriminability of the induced aggregate channel and show that making persona benchmarking as decision-relevant as a field experiment is fundamentally a sample-size question, yielding explicit bounds on the number of independent persona evaluations required to reliably distinguish meaningfully different methods at a chosen resolution.

Method: Three-stage pipeline: (1) Semantic Structured Compression distills interactions into compact, multi-view indexed memory units; (2) Online Semantic Synthesis integrates related context into unified abstract representations; (3) Intent-Aware Retrieval Planning infers search intent to dynamically determine retrieval scope.

Result: Outperforms baselines in accuracy, retrieval efficiency, and inference cost, achieving 26.4% average F1 improvement in LoCoMo while reducing inference-time token consumption by up to 30-fold.

Conclusion: SimpleMem demonstrates superior balance between performance and efficiency for LLM agent memory systems through semantic lossless compression.

Abstract: To support long-term interaction in complex environments, LLM agents require memory systems that manage historical experiences. Existing approaches either retain full interaction histories via passive context extension, leading to substantial redundancy, or rely on iterative reasoning to filter noise, incurring high token costs. To address this challenge, we introduce SimpleMem, an efficient memory framework based on semantic lossless compression. We propose a three-stage pipeline designed to maximize information density and token utilization: (1) Semantic Structured Compression, which distills unstructured interactions into compact, multi-view indexed memory units; (2) Online Semantic Synthesis, an intra-session process that instantly integrates related context into unified abstract representations to eliminate redundancy; and (3) Intent-Aware Retrieval Planning, which infers search intent to dynamically determine retrieval scope and construct precise context efficiently. Experiments on benchmark datasets show that our method consistently outperforms baseline approaches in accuracy, retrieval efficiency, and inference cost, achieving an average F1 improvement of 26.4% in LoCoMo while reducing inference-time token consumption by up to 30-fold, demonstrating a superior balance between performance and efficiency. Code is available at https://github.com/aiming-lab/SimpleMem.

Method: Developed an open recipe for training behavior cloning models using 8300+ hours of human gameplay data, trained models up to 1.2B parameters, and analyzed scaling laws in both controlled toy settings and at scale

Result: Best model achieves performance competitive with human players across various 3D games; scaling analysis shows that increasing training data and network depth leads to models learning more causal policies

Conclusion: Behavior cloning scales effectively for video game playing, with larger models and more data improving causal reasoning capabilities, enabling competitive real-time gameplay on consumer hardware

Abstract: Behavior cloning has seen a resurgence as scaling model and data sizes demonstrate strong performance. In this work, we introduce an open recipe for training a video game playing foundation model designed for inference in realtime on a consumer GPU. We release all data (8300+ hours of high quality human gameplay), training and inference code, and pretrained checkpoints under an open license. Empirically, we show that our best model achieves performance competitive with human players across a variety of 3D games. We use this recipe to investigate the scaling laws of behavior cloning, with a focus on causal reasoning. In a controlled toy setting, we first demonstrate that increasing training data and network depth leads to the model learning a more causal policy. We then validate these findings at scale, analyzing models up to 1.2 billion parameters. We observe that the causal improvements seen in the toy domain hold true as model size and training steps increase.

Method: ExeFuse uses a neuro-symbolic framework based on a novel Fact-as-Program paradigm. It treats fusion as an executable process, utilizing neuro-symbolic execution to infer logical relevance beyond surface similarity and employing target space grounding to calibrate granularity between coarse-grained GKG facts and fine-grained DKG requirements.

Result: The authors construct two new datasets to establish the first standardized evaluation suite for domain-specific knowledge graph fusion. Extensive experiments demonstrate that ExeFuse effectively overcomes domain barriers to achieve superior fusion performance.

Conclusion: ExeFuse addresses the critical gap in leveraging comprehensive GKGs to supplement DKGs, overcoming challenges of domain relevance ambiguity and cross-domain knowledge granularity misalignment through its neuro-symbolic approach.

Abstract: Domain-specific knowledge graphs (DKGs) are critical yet often suffer from limited coverage compared to General Knowledge Graphs (GKGs). Existing tasks to enrich DKGs rely primarily on extracting knowledge from external unstructured data or completing KGs through internal reasoning, but the scope and quality of such integration remain limited. This highlights a critical gap: little systematic exploration has been conducted on how comprehensive, high-quality GKGs can be effectively leveraged to supplement DKGs. To address this gap, we propose a new and practical task: domain-specific knowledge graph fusion (DKGF), which aims to mine and integrate relevant facts from general knowledge graphs into domain-specific knowledge graphs to enhance their completeness and utility. Unlike previous research, this new task faces two key challenges: (1) high ambiguity of domain relevance, i.e., difficulty in determining whether knowledge from a GKG is truly relevant to the target domain , and (2) cross-domain knowledge granularity misalignment, i.e., GKG facts are typically abstract and coarse-grained, whereas DKGs frequently require more contextualized, fine-grained representations aligned with particular domain scenarios. To address these, we present ExeFuse, a neuro-symbolic framework based on a novel Fact-as-Program paradigm. ExeFuse treats fusion as an executable process, utilizing neuro-symbolic execution to infer logical relevance beyond surface similarity and employing target space grounding to calibrate granularity. We construct two new datasets to establish the first standardized evaluation suite for this task. Extensive experiments demonstrate that ExeFuse effectively overcomes domain barriers to achieve superior fusion performance.

Method: Developed the LEG (Large language model and Extended Greedy) framework that combines: 1) a provable approximation algorithm for population coverage optimization, and 2) LLM-driven iterative refinement with human-AI alignment to incorporate expert qualitative guidance while preserving mathematical guarantees.

Result: Experiments on real-world data from three Ethiopian regions demonstrate the framework’s effectiveness in producing solutions that balance quantitative coverage optimization with qualitative expert preferences, showing potential for equitable, data-driven health system planning.

Conclusion: The LEG framework successfully bridges the gap between formal optimization methods and real-world qualitative decision-making by integrating LLMs with classical algorithms, offering a practical approach for resource-constrained health system planning that respects both mathematical guarantees and human expertise.

Abstract: Ethiopia’s Ministry of Health is upgrading health posts to improve access to essential services, particularly in rural areas. Limited resources, however, require careful prioritization of which facilities to upgrade to maximize population coverage while accounting for diverse expert and stakeholder preferences. In collaboration with the Ethiopian Public Health Institute and Ministry of Health, we propose a hybrid framework that systematically integrates expert knowledge with optimization techniques. Classical optimization methods provide theoretical guarantees but require explicit, quantitative objectives, whereas stakeholder criteria are often articulated in natural language and difficult to formalize. To bridge these domains, we develop the Large language model and Extended Greedy (LEG) framework. Our framework combines a provable approximation algorithm for population coverage optimization with LLM-driven iterative refinement that incorporates human-AI alignment to ensure solutions reflect expert qualitative guidance while preserving coverage guarantees. Experiments on real-world data from three Ethiopian regions demonstrate the framework’s effectiveness and its potential to inform equitable, data-driven health system planning.

Method: Proposes a symmetry-based framework using four specific symmetries: inference equivariance, information invariance, concept-closure invariance, and structural invariance. These symmetries formalize interpretable models as a subclass of probabilistic models and provide testable conditions for interpretability.

Result: The symmetry framework enables formalization of interpretable models, unified formulation of interpretable inference as Bayesian inversion, and provides a formal framework for verifying compliance with safety standards and regulations.

Conclusion: Interpretability research needs mathematically rigorous foundations, and symmetry-based definitions provide the necessary formal framework for designing, testing, and verifying interpretable AI systems.

Abstract: This paper argues that interpretability research in Artificial Intelligence (AI) is fundamentally ill-posed as existing definitions of interpretability fail to describe how interpretability can be formally tested or designed for. We posit that actionable definitions of interpretability must be formulated in terms of symmetries that inform model design and lead to testable conditions. Under a probabilistic view, we hypothesise that four symmetries (inference equivariance, information invariance, concept-closure invariance, and structural invariance) suffice to (i) formalise interpretable models as a subclass of probabilistic models, (ii) yield a unified formulation of interpretable inference (e.g., alignment, interventions, and counterfactuals) as a form of Bayesian inversion, and (iii) provide a formal framework to verify compliance with safety standards and regulations.

Method: Use graph neural networks with global structural constraints as inductive bias, enabling non-autoregressive solution generation via direct forward passes. At inference, employ dropout and snapshot ensembling to create implicit ensembles for increased solution diversity.

Result: The approach successfully generates solutions for the Travelling Salesman Problem without search, supervision, or sequential decision-making, reducing optimality gaps through solution diversity from implicit ensembles.

Conclusion: Graph neural networks can internalize global combinatorial structure to function as strong learned heuristics, reframing learning’s role in combinatorial optimization from augmenting classical algorithms to directly instantiating new heuristics.

Abstract: We demonstrate that a single training trajectory can transform a graph neural network into an unsupervised heuristic for combinatorial optimization. Focusing on the Travelling Salesman Problem, we show that encoding global structural constraints as an inductive bias enables a non-autoregressive model to generate solutions via direct forward passes, without search, supervision, or sequential decision-making. At inference time, dropout and snapshot ensembling allow a single model to act as an implicit ensemble, reducing optimality gaps through increased solution diversity. Our results establish that graph neural networks do not require supervised training nor explicit search to be effective. Instead, they can internalize global combinatorial structure and function as strong, learned heuristics. This reframes the role of learning in combinatorial optimization: from augmenting classical algorithms to directly instantiating new heuristics.

Method: Proposes EoG (Explanations over Graphs) framework that formulates investigation as abductive reasoning over dependency graphs. LLM performs bounded local evidence mining and labeling while a deterministic controller manages traversal, state, and belief propagation to compute minimal explanatory frontier.

Result: On ITBench diagnostics task, EoG improves both accuracy and run-to-run consistency over ReAct baselines, including 7x average gain in Majority-at-k entity F1.

Conclusion: Disaggregating reasoning from control and using structured dependency graphs addresses reliability issues in LLM agents for complex investigations, providing more deterministic and accurate results.

Abstract: LLM agents excel when environments are mostly static and the needed information fits in a model’s context window, but they often fail in open-ended investigations where explanations must be constructed by iteratively mining evidence from massive, heterogeneous operational data. These investigations exhibit hidden dependency structure: entities interact, signals co-vary, and the importance of a fact may only become clear after other evidence is discovered. Because the context window is bounded, agents must summarize intermediate findings before their significance is known, increasing the risk of discarding key evidence. ReAct-style agents are especially brittle in this regime. Their retrieve-summarize-reason loop makes conclusions sensitive to exploration order and introduces run-to-run non-determinism, producing a reliability gap where Pass-at-k may be high but Majority-at-k remains low. Simply sampling more rollouts or generating longer reasoning traces does not reliably stabilize results, since hypotheses cannot be autonomously checked as new evidence arrives and there is no explicit mechanism for belief bookkeeping and revision. In addition, ReAct entangles semantic reasoning with controller duties such as tool orchestration and state tracking, so execution errors and plan drift degrade reasoning while consuming scarce context. We address these issues by formulating investigation as abductive reasoning over a dependency graph and proposing EoG (Explanations over Graphs), a disaggregated framework in which an LLM performs bounded local evidence mining and labeling (cause vs symptom) while a deterministic controller manages traversal, state, and belief propagation to compute a minimal explanatory frontier. On a representative ITBench diagnostics task, EoG improves both accuracy and run-to-run consistency over ReAct baselines, including a 7x average gain in Majority-at-k entity F1.

Method: PROTEUS uses Lagrangian dual control where a learned dual variable lambda tracks constraint violations during training and conditions the policy network, allowing the router to translate specified accuracy targets (tau) into routing decisions that satisfy them.

Result: PROTEUS achieves consistent floor compliance (accuracy meets/exceeds tau) with target-response correlation of 0.97-0.98, operates across tau in [0.85, 0.95] from a single model, achieves 90.1% accuracy on RouterBench (within 1.3% of oracle) and 94.0% on SPROUT (within 4.6% of oracle), with cost savings up to 89.8% versus best fixed model.

Conclusion: PROTEUS enables operators to specify accuracy targets directly as runtime input, providing predictable performance across diverse workloads while maintaining high accuracy and significant cost savings compared to existing routing approaches.

Abstract: Production LLM deployments serve diverse workloads where cost and quality requirements vary by customer tier, time of day, and query criticality. Model serving systems accept latency SLOs directly. LLM routers do not. They force operators to tune parameters offline and guess what accuracy might result. The relationship between parameters and outcomes is indirect, non-monotonic, and dataset-dependent. Operators need to specify accuracy targets, not infer them from opaque settings. We present PROTEUS (Polymorphic Router for Operational Target Enforcement with Unified SLA), a router that accepts accuracy targets tau as runtime input. PROTEUS uses Lagrangian dual control. A learned dual variable lambda tracks constraint violations during training and conditions the policy network. This lets the router translate specified tau values into routing decisions that satisfy them. A single trained model serves the full accuracy spectrum without retraining.We evaluate on RouterBench (11 models, 405K queries) and SPROUT (14 models, 45K queries). PROTEUS achieves consistent floor compliance where accuracy meets or exceeds tau. The target-response correlation reaches 0.97 to 0.98. The closest baseline, OmniRouter, meets floors only 22% of the time despite also using Lagrangian optimization. PROTEUS operates across tau in [0.85, 0.95] from a single model. On RouterBench it achieves 90.1% accuracy, within 1.3% of oracle. On SPROUT it achieves 94.0% accuracy, within 4.6% of oracle. Cost savings reach 89.8% versus the best fixed model.

Method: Proposes modeling activations as interpretable cognitive elements (CEs) - fine-grained factors like ‘making a threat’ and ‘payment processing’. Builds a framework with predicate rules over CEs that can detect violations in real time without retraining models or detectors.

Result: Compositional rule-based activation safety improves precision, supports domain customization, and enables scalable, interpretable, and auditable AI governance. The GAVEL framework will be released as open-source with an automated rule creation tool.

Conclusion: Rule-based activation safety offers a practical, flexible, and interpretable approach to LLM safety monitoring that addresses limitations of existing methods and supports real-world deployment with transparency and auditability.

Abstract: Large language models (LLMs) are increasingly paired with activation-based monitoring to detect and prevent harmful behaviors that may not be apparent at the surface-text level. However, existing activation safety approaches, trained on broad misuse datasets, struggle with poor precision, limited flexibility, and lack of interpretability. This paper introduces a new paradigm: rule-based activation safety, inspired by rule-sharing practices in cybersecurity. We propose modeling activations as cognitive elements (CEs), fine-grained, interpretable factors such as ‘‘making a threat’’ and ‘‘payment processing’’, that can be composed to capture nuanced, domain-specific behaviors with higher precision. Building on this representation, we present a practical framework that defines predicate rules over CEs and detects violations in real time. This enables practitioners to configure and update safeguards without retraining models or detectors, while supporting transparency and auditability. Our results show that compositional rule-based activation safety improves precision, supports domain customization, and lays the groundwork for scalable, interpretable, and auditable AI governance. We will release GAVEL as an open-source framework and provide an accompanying automated rule creation tool.

Method: Models user-agent-environment interaction as a structural causal model, uses test-time scaling for probabilistic abduction, and applies conformal counterfactual generation with offline calibration for reliability guarantees.

Result: Demonstrated on wireless network control, showing significant advantages over naive re-execution baselines with formal reliability guarantees.

Conclusion: Provides a principled framework for counterfactual reasoning in LLM-driven control with formal reliability guarantees, enabling users to explore “what-if” scenarios.

Abstract: Large language model (LLM)-powered agents can translate high-level user intents into plans and actions in an environment. Yet after observing an outcome, users may wonder: What if I had phrased my intent differently? We introduce a framework that enables such counterfactual reasoning in agentic LLM-driven control scenarios, while providing formal reliability guarantees. Our approach models the closed-loop interaction between a user, an LLM-based agent, and an environment as a structural causal model (SCM), and leverages test-time scaling to generate multiple candidate counterfactual outcomes via probabilistic abduction. Through an offline calibration phase, the proposed conformal counterfactual generation (CCG) yields sets of counterfactual outcomes that are guaranteed to contain the true counterfactual outcome with high probability. We showcase the performance of CCG on a wireless network control use case, demonstrating significant advantages compared to naive re-execution baselines.

Method: Online experiment using repeated four-player Public Goods Game with 236 participants. Groups had 3 humans + 1 bot (framed as human or AI) following three strategies: unconditional cooperation, conditional cooperation, or free-riding.

Result: Cooperation driven by reciprocal group dynamics and behavioral inertia, operating identically across human/AI conditions. No significant differences in cooperation levels, norm persistence, or normative perceptions between human and AI labels.

Conclusion: Cooperative norms are flexible enough to extend to AI agents, supporting normative equivalence where mechanisms sustaining cooperation function similarly in mixed human-AI and all-human groups.

Abstract: The introduction of artificial intelligence (AI) agents into human group settings raises essential questions about how these novel participants influence cooperative social norms. While previous studies on human-AI cooperation have primarily focused on dyadic interactions, little is known about how integrating AI agents affects the emergence and maintenance of cooperative norms in small groups. This study addresses this gap through an online experiment using a repeated four-player Public Goods Game (PGG). Each group consisted of three human participants and one bot, which was framed either as human or AI and followed one of three predefined decision strategies: unconditional cooperation, conditional cooperation, or free-riding. In our sample of 236 participants, we found that reciprocal group dynamics and behavioural inertia primarily drove cooperation. These normative mechanisms operated identically across conditions, resulting in cooperation levels that did not differ significantly between human and AI labels. Furthermore, we found no evidence of differences in norm persistence in a follow-up Prisoner’s Dilemma, or in participants’ normative perceptions. Participants’ behaviour followed the same normative logic across human and AI conditions, indicating that cooperation depended on group behaviour rather than partner identity. This supports a pattern of normative equivalence, in which the mechanisms that sustain cooperation function similarly in mixed human-AI and all human groups. These findings suggest that cooperative norms are flexible enough to extend to artificial agents, blurring the boundary between humans and AI in collective decision-making.

Method: Proposes Semantically Expanded Prompt Tuning (SEPT) that uses LLM-generated semantic neighbors to regularize prompt embedding space with a novel semantic expansion loss promoting intra-class compactness and inter-class separability.

Result: SEPT consistently improves generalization performance across multiple prompt tuning baselines for audio-language models, enhancing both base-to-new generalization and cross-dataset transferability.

Conclusion: SEPT effectively addresses the Base-New Tradeoff in ALMs by preserving semantic structure in prompt embedding space, establishing a benchmark for prompt generalization in audio-language models.

Abstract: Prompt tuning has achieved remarkable progress in vision-language models (VLMs) and is recently being adopted for audio-language models (ALMs). However, its generalization ability in ALMs remains largely underexplored. We observe that conventional prompt tuning for ALMs also suffers from the Base-New Tradeoff, and we identify that this issue stems from the disrupted semantic structure of the embedding space. To address this issue, we propose Semantically Expanded Prompt Tuning (SEPT)-a plug-and-play framework that explicitly regularizes the prompt embedding space by incorporating semantic neighbors generated by large language models. SEPT introduces a novel semantic expansion loss with margin constraints that promote intra-class compactness and inter-class separability, thereby enhancing the semantic structure of the prompt embedding space. For comprehensive evaluation, we establish the first benchmark setup for prompt generalization in ALMs, covering both base-to-new generalization and cross-dataset transferability. Extensive experiments demonstrate that SEPT consistently improves generalization performance across multiple prompt tuning baselines, while maintaining computational cost during inference. Codes are available in https://github.com/jhyukjang/SEPT.

Method: Disentangles vocal traits into prosody and timbre embeddings, uses Spherical Linear Interpolation (Slerp) for fine-grained interpolation, and synthesizes with autoregressive language model coupled with Conditional Flow Matching network.

Result: Achieves 2.6x gain in audio quality, 73% reduction in intelligibility errors, and 67.8% morphing attack success rate on automated speaker verification systems under strict security thresholds.

Conclusion: Establishes a practical and scalable paradigm for voice morphing with significant implications for biometric security, enabling zero-shot morphing from minimal audio input.

Abstract: Morphing techniques generate artificial biometric samples that combine features from multiple individuals, allowing each contributor to be verified against a single enrolled template. While extensively studied in face recognition, this vulnerability remains largely unexplored in voice biometrics. Prior work on voice morphing is computationally expensive, non-scalable, and limited to acoustically similar identity pairs, constraining practical deployment. Moreover, existing sound-morphing methods target audio textures, music, or environmental sounds and are not transferable to voice identity manipulation. We propose VoxMorph, a zero-shot framework that produces high-fidelity voice morphs from as little as five seconds of audio per subject without model retraining. Our method disentangles vocal traits into prosody and timbre embeddings, enabling fine-grained interpolation of speaking style and identity. These embeddings are fused via Spherical Linear Interpolation (Slerp) and synthesized using an autoregressive language model coupled with a Conditional Flow Matching network. VoxMorph achieves state-of-the-art performance, delivering a 2.6x gain in audio quality, a 73% reduction in intelligibility errors, and a 67.8% morphing attack success rate on automated speaker verification systems under strict security thresholds. This work establishes a practical and scalable paradigm for voice morphing with significant implications for biometric security. The code and dataset are available on our project page: https://vcbsl.github.io/VoxMorph/

Method: Proposes a modular framework with denoising/normalization, hybrid ASR frontend (Whisper + Vosk), and verification layer supporting multiple matching strategies: embedding similarity, edit distance, and LLM-based matching with optional contextual guidance.

Result: Evaluation on Google Speech Commands dataset and real-world telephony/messaging datasets shows hybrid ASR frontend performs well on clean audio, but verification layer significantly improves accuracy on noisy/compressed channels. Context-guided and LLM-based matching yield largest gains.

Conclusion: Lightweight verification and context mechanisms can substantially improve single-word ASR robustness without sacrificing latency required for real-time telephony applications.

Abstract: Single-word Automatic Speech Recognition (ASR) is a challenging task due to the lack of linguistic context and sensitivity to noise, pronunciation variation, and channel artifacts, especially in low-resource, communication-critical domains such as healthcare and emergency response. This paper reviews recent deep learning approaches and proposes a modular framework for robust single-word detection. The system combines denoising and normalization with a hybrid ASR front end (Whisper + Vosk) and a verification layer designed to handle out-of-vocabulary words and degraded audio. The verification layer supports multiple matching strategies, including embedding similarity, edit distance, and LLM-based matching with optional contextual guidance. We evaluate the framework on the Google Speech Commands dataset and a curated real-world dataset collected from telephony and messaging platforms under bandwidth-limited conditions. Results show that while the hybrid ASR front end performs well on clean audio, the verification layer significantly improves accuracy on noisy and compressed channels. Context-guided and LLM-based matching yield the largest gains, demonstrating that lightweight verification and context mechanisms can substantially improve single-word ASR robustness without sacrificing latency required for real-time telephony applications.

Method: Systematically examined curated subsets of pre-training data for ASR, comparing strategies based on acoustic, speaker, and linguistic diversity versus random sampling and length-based selection.

Result: Optimizing for diversity yields no clear improvements over random sampling, but prioritizing longest utterances achieves superior ASR results using only half the original dataset with 24% faster pre-training.

Conclusion: For speech SSL pre-training, data length is more critical than diversity or overall quantity for both performance and efficiency, offering new data selection strategies.

Abstract: Self-supervised learning (SSL) has transformed speech processing, yet its reliance on massive pre-training datasets remains a bottleneck. While robustness is often attributed to scale and diversity, the role of the data distribution is less understood. We systematically examine how curated subsets of pre-training data influence Automatic Speech Recognition (ASR) performance. Surprisingly, optimizing for acoustic, speaker, or linguistic diversity yields no clear improvements over random sampling. Instead, we find that prioritizing the longest utterances achieves superior ASR results while using only half the original dataset, reducing pre-training time by 24% on a large corpora. These findings suggest that for pre-training speech SSL models, data length is a more critical factor than either data diversity or overall data quantity for performance and efficiency, offering a new perspective for data selection strategies in SSL speech processing.

Method: Introduces a text-only adaptation method that emulates audio projection as a text denoising task - trains LLM to recover clean transcripts from noisy inputs, preserving cross-modal alignment without architectural changes

Result: Achieves up to 22.1% relative improvement on two datasets, outperforming recent state-of-the-art text-only adaptation methods

Conclusion: Proposes an effective lightweight text-only adaptation approach for LLM-based ASR that maintains speech-text alignment through denoising-based training

Abstract: Adapting automatic speech recognition (ASR) systems based on large language models (LLMs) to new domains using text-only data is a significant yet underexplored challenge. Standard fine-tuning of the LLM on target-domain text often disrupts the critical alignment between speech and text modalities learned by the projector, degrading performance. We introduce a novel text-only adaptation method that emulates the audio projection task by treating it as a text denoising task. Our approach thus trains the LLM to recover clean transcripts from noisy inputs. This process effectively adapts the model to a target domain while preserving cross-modal alignment. Our solution is lightweight, requiring no architectural changes or additional parameters. Extensive evaluation on two datasets demonstrates up to 22.1% relative improvement, outperforming recent state-of-the-art text-only adaptation methods.

Method: PhaseCoder is a transformer-only spatial audio encoder that takes raw multichannel audio and microphone coordinates as inputs to perform localization and produce robust spatial embeddings. The system enables fine-tuning LLMs like Gemma 3n to reason over “Spatial Audio Tokens” generated by PhaseCoder.

Result: PhaseCoder achieves state-of-the-art results on microphone-invariant localization benchmarks and enables an LLM to perform complex spatial reasoning and targeted transcription tasks from arbitrary microphone arrays for the first time.

Conclusion: PhaseCoder bridges the gap between multimodal LLMs and spatial audio processing, enabling geometry-agnostic spatial audio understanding that can be integrated with large language models for advanced audio reasoning tasks.

Abstract: Current multimodal LLMs process audio as a mono stream, ignoring the rich spatial information essential for embodied AI. Existing spatial audio models, conversely, are constrained to fixed microphone geometries, preventing deployment across diverse devices. We present PhaseCoder, a transformer-only spatial audio encoder that is agnostic to microphone geometry. PhaseCoder takes raw multichannel audio and microphone coordinates as inputs to perform localization and produces robust spatial embeddings. We demonstrate that Gemma 3n LLM can be fine-tuned to reason over “Spatial Audio Tokens” produced by PhaseCoder. We show our encoder achieves state-of-the-art results on microphone-invariant localization benchmarks and, for the first time, enables an LLM to perform complex spatial reasoning and targeted transcription tasks from an arbitrary microphone array.

Method: Defines music plagiarism detection task distinct from other MIR tasks, introduces Similar Music Pair dataset, and proposes segment transcription-based detection method.

Result: Provides clear task definition, releases dataset, and demonstrates proposed method with available demo and dataset on GitHub.

Conclusion: Clear task definition enables more focused research on music plagiarism detection, with proposed method and dataset serving as foundation for future work.

Abstract: Recently, the problem of music plagiarism has emerged as an even more pressing social issue. As music information retrieval research advances, there is a growing effort to address issues related to music plagiarism. However, many studies, including our previous work, have conducted research without clearly defining what the music plagiarism detection task actually involves. This lack of a clear definition has slowed research progress and made it hard to apply results to real-world scenarios. To fix this situation, we defined how Music Plagiarism Detection is different from other MIR tasks and explained what problems need to be solved. We introduce the Similar Music Pair dataset to support this newly defined task. In addition, we propose a method based on segment transcription as one way to solve the task. Our demo and dataset are available at https://github.com/Mippia/ICASSP2026-MPD.

Method: Two-stage generation: first predicts structured multi-level song programs, then renders audio. Uses Global-Local Diffusion Transformer with global path for long-range progression and local path for fine details. Includes Motif Memory Retrieval for consistent motif recurrence and Projected Diffusion Inpainting for localized editing.

Result: Achieves state-of-the-art fidelity, controllability, and long-range coherence. Introduces Structure Coherence Score and Edit Fidelity Score for evaluation.

Conclusion: MusicWeaver enables practical music creation with structural editing and long-range coherence through interpretable representations and specialized architecture.

Abstract: Recent advances in music generation produce impressive samples, however, practical creation still lacks two key capabilities: composer-style structural editing and minute-scale coherence. We present MusicWeaver, a framework for generating and editing long-range music using a human-interpretable intermediate representation with guaranteed edit locality. MusicWeaver decomposes generation into two stages: it first predicts a structured plan, a multi-level song program encoding musical attributes that composers can directly edit, and then renders audio conditioned on this plan. To ensure minute-scale coherence, we introduce a Global-Local Diffusion Transformer, where a global path captures long-range musical progression via compressed representations and memory, while a local path synthesizes fine-grained acoustic detail. We further propose a Motif Memory Retrieval module that enables consistent motif recurrence with controllable variation. For editing, we propose Projected Diffusion Inpainting, an inpainting method that denoises only user-specified regions and preserves unchanged content, allowing repeated edits without drift. Finally, we introduce Structure Coherence Score and Edit Fidelity Score to evaluate long-range form and edit realization. Experiments demonstrate that MusicWeaver achieves state-of-the-art fidelity, controllability, and long-range coherence.

Method: Comprehensive evaluation of FSD and SMMD under varied embeddings and conditions, incorporating human listening evaluations alongside TTS intelligibility and synthetic-trained ASR WER to validate perceptual relevance.

Result: WavLM Base+ features yield the most stable alignment with human ratings. While FSD and SMMD cannot fully replace subjective evaluation, they serve as complementary, cost-efficient, and reproducible measures.

Conclusion: FSD and SMMD are useful objective metrics for synthetic speech quality assessment, particularly when large-scale or direct listening assessments are infeasible, with WavLM Base+ features providing the best results.

Abstract: Objective evaluation of synthetic speech quality remains a critical challenge. Human listening tests are the gold standard, but costly and impractical at scale. Fréchet Distance has emerged as a promising alternative, yet its reliability depends heavily on the choice of embeddings and experimental settings. In this work, we comprehensively evaluate Fréchet Speech Distance (FSD) and its variant Speech Maximum Mean Discrepancy (SMMD) under varied embeddings and conditions. We further incorporate human listening evaluations alongside TTS intelligibility and synthetic-trained ASR WER to validate the perceptual relevance of these metrics. Our findings show that WavLM Base+ features yield the most stable alignment with human ratings. While FSD and SMMD cannot fully replace subjective evaluation, we show that they can serve as complementary, cost-efficient, and reproducible measures, particularly useful when large-scale or direct listening assessments are infeasible. Code is available at https://github.com/kaen2891/FrechetSpeechDistance.

Method: Constructed AiEdit dataset using LLMs for semantic tampering logic and neural speech editing methods; proposed PELM framework that unifies detection and localization as audio QA, incorporates word-level probability priors, and uses centroid-aggregation-based acoustic consistency perception loss.

Result: PELM significantly outperforms SOTA methods on HumanEdit and AiEdit datasets, achieving EERs of 0.57% and 9.28% for localization respectively.

Conclusion: PELM effectively addresses neural speech editing detection challenges by leveraging audio LLMs with explicit acoustic priors and consistency modeling, advancing the field of speech manipulation detection.

Abstract: Speech editing achieves semantic inversion by performing fine-grained segment-level manipulation on original utterances, while preserving global perceptual naturalness. Existing detection studies mainly focus on manually edited speech with explicit splicing artifacts, and therefore struggle to cope with emerging end-to-end neural speech editing techniques that generate seamless acoustic transitions. To address this challenge, we first construct a large-scale bilingual dataset, AiEdit, which leverages large language models to drive precise semantic tampering logic and employs multiple advanced neural speech editing methods for data synthesis, thereby filling the gap of high-quality speech editing datasets. Building upon this foundation, we propose PELM (Prior-Enhanced Audio Large Language Model), the first large-model framework that unifies speech editing detection and content localization by formulating them as an audio question answering task. To mitigate the inherent forgery bias and semantic-priority bias observed in existing audio large models, PELM incorporates word-level probability priors to provide explicit acoustic cues, and further designs a centroid-aggregation-based acoustic consistency perception loss to explicitly enforce the modeling of subtle local distribution anomalies. Extensive experimental results demonstrate that PELM significantly outperforms state-of-the-art methods on both the HumanEdit and AiEdit datasets, achieving equal error rates (EER) of 0.57% and 9.28% (localization), respectively.

Method: Proposes two novel objectives: Embodied Contrastive Loss (ECL) with tempo-aware and beat-jitter negatives for rhythmic discrimination, and Structural Rhythm Alignment Loss (SRAL) to align music accents with motion events. Architecture includes bar-equivariant phase rotations and contact-guided attention.

Result: Outperforms state-of-the-art audio encoders in music-to-dance generation and shows strong transfer performance on beat tracking, music tagging, genre/instrument classification, emotion recognition, and audio-visual retrieval.

Conclusion: MotionBeat successfully incorporates embodied motion information into music representation learning, demonstrating superior performance on dance-related tasks and broad transfer capabilities across multiple audio understanding tasks.

Abstract: Music is both an auditory and an embodied phenomenon, closely linked to human motion and naturally expressed through dance. However, most existing audio representations neglect this embodied dimension, limiting their ability to capture rhythmic and structural cues that drive movement. We propose MotionBeat, a framework for motion-aligned music representation learning. MotionBeat is trained with two newly proposed objectives: the Embodied Contrastive Loss (ECL), an enhanced InfoNCE formulation with tempo-aware and beat-jitter negatives to achieve fine-grained rhythmic discrimination, and the Structural Rhythm Alignment Loss (SRAL), which ensures rhythm consistency by aligning music accents with corresponding motion events. Architecturally, MotionBeat introduces bar-equivariant phase rotations to capture cyclic rhythmic patterns and contact-guided attention to emphasize motion events synchronized with musical accents. Experiments show that MotionBeat outperforms state-of-the-art audio encoders in music-to-dance generation and transfers effectively to beat tracking, music tagging, genre and instrument classification, emotion recognition, and audio-visual retrieval. Our project demo page: https://motionbeat2025.github.io/.

Method: Proposes Segment-Aware Learning (SAL) with two core techniques: 1) Segment Positional Labeling - provides fine-grained frame supervision based on relative position within a segment, 2) Cross-Segment Mixing - data augmentation method that generates diverse segment patterns.

Result: Experiments across multiple deepfake localization datasets show SAL consistently achieves strong performance in both in-domain and out-of-domain settings, with notable gains in non-boundary regions and reduced reliance on transition artifacts.

Conclusion: SAL effectively addresses the limitations of boundary-focused approaches by encouraging models to focus on internal segment structure, leading to more robust deepfake audio localization.

Abstract: Localizing partial deepfake audio, where only segments of speech are manipulated, remains challenging due to the subtle and scattered nature of these modifications. Existing approaches typically rely on frame-level predictions to identify spoofed segments, and some recent methods improve performance by concentrating on the transitions between real and fake audio. However, we observe that these models tend to over-rely on boundary artifacts while neglecting the manipulated content that follows. We argue that effective localization requires understanding the entire segments beyond just detecting transitions. Thus, we propose Segment-Aware Learning (SAL), a framework that encourages models to focus on the internal structure of segments. SAL introduces two core techniques: Segment Positional Labeling, which provides fine-grained frame supervision based on relative position within a segment; and Cross-Segment Mixing, a data augmentation method that generates diverse segment patterns. Experiments across multiple deepfake localization datasets show that SAL consistently achieves strong performance in both in-domain and out-of-domain settings, with notable gains in non-boundary regions and reduced reliance on transition artifacts. The code is available at https://github.com/SentryMao/SAL.

Method: Created AudioEval dataset with 4,200 generated audio samples from 24 systems, collected 126,000 ratings from experts and non-experts across 5 dimensions (enjoyment, usefulness, complexity, quality, text alignment). Proposed Qwen-DisQA as baseline model that jointly processes prompts and audio to predict multi-dimensional ratings with distributional prediction for rater disagreement.

Result: Benchmarked diverse automatic evaluators using AudioEval, revealing perspective- and dimension-level differences across model families. Qwen-DisQA achieved strong performance as a reference baseline for multi-dimensional rating prediction.

Conclusion: AudioEval provides a comprehensive evaluation resource for text-to-audio generation, and Qwen-DisQA serves as a strong baseline model for predicting multi-dimensional audio quality ratings while modeling rater disagreement.

Abstract: Text-to-audio (TTA) generation is advancing rapidly, but evaluation remains challenging because human listening studies are expensive and existing automatic metrics capture only limited aspects of perceptual quality. We introduce AudioEval, a large-scale TTA evaluation dataset with 4,200 generated audio samples (11.7 hours) from 24 systems and 126,000 ratings collected from both experts and non-experts across five dimensions: enjoyment, usefulness, complexity, quality, and text alignment. Using AudioEval, we benchmark diverse automatic evaluators to compare perspective- and dimension-level differences across model families. We also propose Qwen-DisQA as a strong reference baseline: it jointly processes prompts and generated audio to predict multi-dimensional ratings for both annotator groups, modeling rater disagreement via distributional prediction and achieving strong performance. We will release AudioEval to support future research in TTA evaluation.

Method: Uses LLMs with chain-of-thought planning to understand audio inputs, select effect types, determine order, and estimate parameters. Introduces LP-Fx dataset with structured CoT annotations and tool calls. Combines autoregressive sequence modeling, tool calling, and CoT reasoning.

Result: System can infer Fx-chains and parameters from audio pairs, validated in style transfer settings. LLM-as-a-judge evaluation shows appropriate CoT reasoning for music production queries.

Conclusion: First work to apply LLM-based tool calling to audio effects modules, enabling interpretable and controllable music production through multimodal understanding and generation.

Abstract: This paper introduces LLM2Fx-Tools, a multimodal tool-calling framework that generates executable sequences of audio effects (Fx-chain) for music post-production. LLM2Fx-Tools uses a large language model (LLM) to understand audio inputs, select audio effects types, determine their order, and estimate parameters, guided by chain-of-thought (CoT) planning. We also present LP-Fx, a new instruction-following dataset with structured CoT annotations and tool calls for audio effects modules. Experiments show that LLM2Fx-Tools can infer an Fx-chain and its parameters from pairs of unprocessed and processed audio, enabled by autoregressive sequence modeling, tool calling, and CoT reasoning. We further validate the system in a style transfer setting, where audio effects information is transferred from a reference source and applied to new content. Finally, LLM-as-a-judge evaluation demonstrates that our approach generates appropriate CoT reasoning and responses for music production queries. To our knowledge, this is the first work to apply LLM-based tool calling to audio effects modules, enabling interpretable and controllable music production.

Method: Conducted comprehensive experiments on 11 types of FAEs to investigate how learning methods, training data, and model context length affect MSA performance. Evaluated different pretraining approaches including self-supervised learning with masked language modeling.

Result: FAEs using self-supervised learning with masked language modeling on music data are particularly effective for music structure analysis. The study identifies key factors that influence MSA performance across different encoder architectures.

Conclusion: The findings provide guidance for selecting appropriate FAEs for MSA tasks and pave the way for future research in foundational audio encoders and music structure analysis.

Abstract: In music information retrieval (MIR) research, the use of pretrained foundational audio encoders (FAEs) has recently become a trend. FAEs pretrained on large amounts of music and audio data have been shown to improve performance on MIR tasks such as music tagging and automatic music transcription. However, their use for music structure analysis (MSA) remains underexplored: only a small subset of FAEs has been examined for MSA, and the impact of factors such as learning methods, training data, and model context length on MSA performance remains unclear. In this study, we conduct comprehensive experiments on 11 types of FAEs to investigate how these factors affect MSA performance. Our results demonstrate that FAEs using self-supervised learning with masked language modeling on music data are particularly effective for MSA. These findings pave the way for future research in FAE and MSA.

Method: Proposes DASH framework with convergence-aware metric to co-optimize solver search mechanisms and runtime schedules, plus Profiled Library Retrieval (PLR) to archive specialized solvers for cost-effective warm-starts.

Result: DASH improves runtime efficiency by over 3x while surpassing solution quality of SOTA baselines across diverse problem scales, and cuts LLM adaptation costs by over 90% through profile-based warm starts.

Conclusion: DASH effectively addresses limitations of existing LHD frameworks by co-optimizing search mechanisms and runtime schedules, with PLR enabling efficient adaptation to heterogeneous distributions.

Abstract: Large Language Models (LLMs) have advanced the field of Combinatorial Optimization through automated heuristic generation. Instead of relying on manual design, this LLM-Driven Heuristic Design (LHD) process leverages LLMs to iteratively generate and refine solvers to achieve high performance. However, existing LHD frameworks face two critical limitations: (1) Endpoint-only evaluation, which ranks solvers solely by final quality, ignoring the convergence process and runtime efficiency; (2) High adaptation costs, where distribution shifts necessitate re-adaptation to generate specialized solvers for new instance groups. To address these issues, we propose Dynamics-Aware Solver Heuristics (DASH), a framework that co-optimizes solver search mechanisms and runtime schedules guided by a convergence-aware metric, thereby identifying efficient and high-performance solvers. Furthermore, to mitigate expensive re-adaptation, DASH incorporates Profiled Library Retrieval (PLR). PLR efficiently archives specialized solvers concurrently with the evolutionary process, enabling cost-effective warm-starts for heterogeneous distributions. Experiments on four combinatorial optimization problems demonstrate that DASH improves runtime efficiency by over 3 times, while surpassing the solution quality of state-of-the-art baselines across diverse problem scales. Furthermore, by enabling profile-based warm starts, DASH maintains superior accuracy under different distributions while cutting LLM adaptation costs by over 90%.

Method: FIP biases representation learning toward a designated target modality through three techniques: higher masking difficulty, stronger loss weighting, and increased decoder capacity for the target modality, without modifying the shared encoder or requiring additional supervision.

Result: When applied to masked modeling on constellation diagrams for wireless signals, FIP consistently improves downstream fine-tuned performance with no extra data or compute.

Conclusion: FIP is a simple, model-agnostic method that is architecture-compatible and broadly applicable across multimodal masked modeling pipelines, effectively optimizing representations for the modality that matters in downstream applications.

Abstract: Multimodal pretraining is effective for building general-purpose representations, but in many practical deployments, only one modality is heavily used during downstream fine-tuning. Standard pretraining strategies treat all modalities uniformly, which can lead to under-optimized representations for the modality that actually matters. We propose Finetune-Informed Pretraining (FIP), a model-agnostic method that biases representation learning toward a designated target modality needed at fine-tuning time. FIP combines higher masking difficulty, stronger loss weighting, and increased decoder capacity for the target modality, without modifying the shared encoder or requiring additional supervision. When applied to masked modeling on constellation diagrams for wireless signals, FIP consistently improves downstream fine-tuned performance with no extra data or compute. FIP is simple to implement, architecture-compatible, and broadly applicable across multimodal masked modeling pipelines.

Method: Integrates causal discovery with lightweight generative ML model trained on 450 samples (270 training, 90 validation, 90 testing) to generate 1,000 novel metal hydride candidates, then ranks and filters them.

Result: Generated 6 previously unreported chemical formulas and crystal structures, with 4 validated by density functional theory simulations showing strong potential for experimental investigation.

Conclusion: The framework provides scalable, time-efficient approach for expanding hydrogen storage datasets and accelerating materials discovery beyond existing database limitations.

Abstract: Developing new metal hydrides is a critical step toward efficient hydrogen storage in carbon-neutral energy systems. However, existing materials databases, such as the Materials Project, contain a limited number of well-characterized hydrides, which constrains the discovery of optimal candidates. This work presents a framework that integrates causal discovery with a lightweight generative machine learning model to generate novel metal hydride candidates that may not exist in current databases. Using a dataset of 450 samples (270 training, 90 validation, and 90 testing), the model generates 1,000 candidates. After ranking and filtering, six previously unreported chemical formulas and crystal structures are identified, four of which are validated by density functional theory simulations and show strong potential for future experimental investigation. Overall, the proposed framework provides a scalable and time-efficient approach for expanding hydrogen storage datasets and accelerating materials discovery.

Method: Proposes a prompt-sharing framework with: 1) Global prompt pool, 2) Task-aware gated routing mechanism that sparsely activates subsets of prompts for dynamic decoupling and collaborative optimization, 3) History-aware modulator using cumulative prompt activation statistics to protect frequently used prompts from excessive updates.

Result: Extensive analysis and empirical results demonstrate the approach consistently outperforms existing static allocation strategies in both effectiveness and efficiency.

Conclusion: The proposed prompt-sharing framework with dynamic routing and history-aware modulation effectively addresses parameter utilization and knowledge forgetting issues in continual learning.

Abstract: Prompt-based continual learning methods effectively mitigate catastrophic forgetting. However, most existing methods assign a fixed set of prompts to each task, completely isolating knowledge across tasks and resulting in suboptimal parameter utilization. To address this, we consider the practical needs of continual learning and propose a prompt-sharing framework. This framework constructs a global prompt pool and introduces a task-aware gated routing mechanism that sparsely activates a subset of prompts to achieve dynamic decoupling and collaborative optimization of task-specific feature representations. Furthermore, we introduce a history-aware modulator that leverages cumulative prompt activation statistics to protect frequently used prompts from excessive updates, thereby mitigating inefficient parameter usage and knowledge forgetting. Extensive analysis and empirical results demonstrate that our approach consistently outperforms existing static allocation strategies in effectiveness and efficiency.

Method: Proposed a new initialization technique for predictive coding networks that aims to preserve the iterative progress made on previous training samples, reducing required training times.

Result: Experiments demonstrate substantial improvements in convergence speed and final test loss in both supervised and unsupervised settings.

Conclusion: The proposed initialization technique offers a promising path toward reconciling computational efficiency disparities between predictive coding and backpropagation while maintaining performance.

Abstract: Research aimed at scaling up neuroscience inspired learning algorithms for neural networks is accelerating. Recently, a key research area has been the study of energy-based learning algorithms such as predictive coding, due to their versatility and mathematical grounding. However, the applicability of such methods is held back by the large computational requirements caused by their iterative nature. In this work, we address this problem by showing that the choice of initialization of the neurons in a predictive coding network matters significantly and can notably reduce the required training times. Consequently, we propose a new initialization technique for predictive coding networks that aims to preserve the iterative progress made on previous training samples. Our approach suggests a promising path toward reconciling the disparities between predictive coding and backpropagation in terms of computational efficiency and final performance. In fact, our experiments demonstrate substantial improvements in convergence speed and final test loss in both supervised and unsupervised settings.

Method: TwinWeaver serializes longitudinal patient histories into text format, enabling unified event prediction and forecasting with large language models. The framework builds Genie Digital Twin (GDT) on 93,054 patients across 20 cancer types by converting clinical data into textual representations that LLMs can process.

Result: GDT significantly reduces forecasting error with median MASE of 0.87 vs 0.97 for best time-series baseline (p<0.001). It improves risk stratification with average C-index of 0.703 vs 0.662 baseline. It generalizes to out-of-distribution clinical trials, achieving median MASE of 0.75-0.88 and outperforming baselines in event prediction with C-index of 0.672 vs 0.648.

Conclusion: TwinWeaver provides a scalable and transparent foundation for longitudinal clinical modeling, enabling interpretable clinical reasoning and demonstrating that LLMs can effectively process serialized clinical data for superior forecasting and risk prediction in oncology.

Abstract: Precision oncology requires forecasting clinical events and trajectories, yet modeling sparse, multi-modal clinical time series remains a critical challenge. We introduce TwinWeaver, an open-source framework that serializes longitudinal patient histories into text, enabling unified event prediction as well as forecasting with large language models, and use it to build Genie Digital Twin (GDT) on 93,054 patients across 20 cancer types. In benchmarks, GDT significantly reduces forecasting error, achieving a median Mean Absolute Scaled Error (MASE) of 0.87 compared to 0.97 for the strongest time-series baseline (p<0.001). Furthermore, GDT improves risk stratification, achieving an average concordance index (C-index) of 0.703 across survival, progression, and therapy switching tasks, surpassing the best baseline of 0.662. GDT also generalizes to out-of-distribution clinical trials, matching trained baselines at zero-shot and surpassing them with fine-tuning, achieving a median MASE of 0.75-0.88 and outperforming the strongest baseline in event prediction with an average C-index of 0.672 versus 0.648. Finally, TwinWeaver enables an interpretable clinical reasoning extension, providing a scalable and transparent foundation for longitudinal clinical modeling.

Method: Introduces a “Noisy but Valid” hypothesis testing framework that uses a small human-labeled calibration set to estimate judge’s True Positive/False Positive Rates, then applies a variance-corrected critical threshold to large judge-labeled datasets while guaranteeing finite-sample Type-I error control.

Result: Theoretical guarantees show conditions where noisy testing outperforms direct evaluation; empirical validation on Jigsaw Comment, Hate Speech and SafeRLHF datasets confirms the theory; reveals significant “Oracle Gap” between practical methods and theoretical optimum.

Conclusion: Provides first systematic treatment of imperfect-judge setting, yielding interpretable diagnostics of judge reliability and clarifying evaluation power dependencies on judge quality, dataset size, and certification levels.

Abstract: Reliable certification of Large Language Models (LLMs)-verifying that failure rates are below a safety threshold-is critical yet challenging. While “LLM-as-a-Judge” offers scalability, judge imperfections, noise, and bias can invalidate statistical guarantees. We introduce a “Noisy but Valid” hypothesis testing framework to address this. By leveraging a small human-labelled calibration set to estimate the judge’s True Positive and False Positive Rates (TPR/FPR), we derive a variance-corrected critical threshold applied to a large judge-labelled dataset. Crucially, our framework theoretically guarantees finite-sample Type-I error control (validity) despite calibration uncertainty. This distinguishes our work from Prediction-Powered Inference (PPI), positioning our method as a diagnostic tool that explicitly models judge behavior rather than a black-box estimator. Our contributions include: (1) Theoretical Guarantees: We derive the exact conditions under which noisy testing yields higher statistical power than direct evaluation; (2) Empirical Validation: Experiments on Jigsaw Comment, Hate Speech and SafeRLHF confirm our theory; (3) The Oracle Gap: We reveal a significant performance gap between practical methods and the theoretical “Oracle” (perfectly known judge parameters), quantifying the cost of estimation. Specifically, we provide the first systematic treatment of the imperfect-judge setting, yielding interpretable diagnostics of judge reliability and clarifying how evaluation power depends on judge quality, dataset size, and certification levels. Together, these results sharpen understanding of statistical evaluation with LLM judges, and highlight trade-offs among competing inferential tools.

Method: Uses subspace system identification to model cerebral hemodynamics from noninvasive signals (ABP, CBv, R-R interval). Learns a mapping function between signal features and estimation errors using ranking constraints through convex optimization. Patients were randomly split into training/testing datasets for evaluation.

Result: Approximately 31.88% of testing entries achieved estimation errors within 2 mmHg, and 34.07% had errors between 2-6 mmHg using the nonlinear mapping with constraints.

Conclusion: The approach demonstrates feasibility for noninvasive ICP estimation, laying foundation for safe, accessible ICP monitoring, though further validation and refinement are needed before clinical deployment.

Abstract: Objective: Accurate noninvasive estimation of intracranial pressure (ICP) remains a major challenge in critical care. We developed a bespoke machine learning algorithm that integrates system identification and ranking-constrained optimization to estimate mean ICP from noninvasive signals. Methods: A machine learning framework was proposed to obtain accurate mean ICP values using arbitrary noninvasive signals. The subspace system identification algorithm is employed to identify cerebral hemodynamics models for ICP simulation using arterial blood pressure (ABP), cerebral blood velocity (CBv), and R-wave to R-wave interval (R-R interval) signals in a comprehensive database. A mapping function to describe the relationship between the features of noninvasive signals and the estimation errors is learned using innovative ranking constraints through convex optimization. Patients across multiple clinical settings were randomly split into testing and training datasets for performance evaluation of the mapping function. Results: The results indicate that about 31.88% of testing entries achieved estimation errors within 2 mmHg and 34.07% of testing entries between 2 mmHg to 6 mmHg from the nonlinear mapping with constraints. Conclusion: Our results demonstrate the feasibility of the proposed noninvasive ICP estimation approach. Significance: Further validation and technical refinement are required before clinical deployment, but this work lays the foundation for safe and broadly accessible ICP monitoring in patients with acute brain injury and related conditions.

Method: Theoretical analysis identifying a fundamental tetrachotomy of optimal rates: for every concept class, the optimal universal rate of convergence of the excess error rate falls into one of four categories: e^{-n}, e^{-o(n)}, o(n^{-1/2}), or arbitrarily slow

Result: Identifies simple combinatorial structures that determine which of the four categories any given concept class falls into, providing a complete characterization of optimal universal rates for binary classification in agnostic setting

Conclusion: Provides a complete theory of optimal universal rates for binary classification in the agnostic setting, extending previous realizable-case theory and establishing a fundamental tetrachotomy of convergence rates determined by combinatorial properties of concept classes

Abstract: We provide a complete theory of optimal universal rates for binary classification in the agnostic setting. This extends the realizable-case theory of Bousquet, Hanneke, Moran, van Handel, and Yehudayoff (2021) by removing the realizability assumption on the distribution. We identify a fundamental tetrachotomy of optimal rates: for every concept class, the optimal universal rate of convergence of the excess error rate is one of $e^{-n}$, $e^{-o(n)}$, $o(n^{-1/2})$, or arbitrarily slow. We further identify simple combinatorial structures which determine which of these categories any given concept class falls into.

Method: Introduces Homogeneous-Monotone Radial Inverse (HM-RI) networks that predict POA’s projection primitive via radial inverse, enforcing monotonicity and homogeneity properties to enable fast projection estimation without explicit function learning

Result: Substantial speed-ups across monotone optimization benchmarks (indefinite quadratic programming, multiplicative programming, transmit power optimization) while maintaining solution quality, outperforming baselines that don’t exploit monotonic structure

Conclusion: Algorithm-aware learning approach integrating learned models into POA via radial inverse prediction provides efficient solution for data-driven monotone optimization problems

Abstract: Monotone optimisation problems admit specialised global solvers such as the Polyblock Outer Approximation (POA) algorithm, but these methods typically require explicit objective and constraint functions. In many applications, these functions are only available through data, making POA difficult to apply directly. We introduce an algorithm-aware learning approach that integrates learned models into POA by directly predicting its projection primitive via the radial inverse, avoiding the costly bisection procedure used in standard POA. We propose Homogeneous-Monotone Radial Inverse (HM-RI) networks, structured neural architectures that enforce key monotonicity and homogeneity properties, enabling fast projection estimation. We provide a theoretical characterisation of radial inverse functions and show that, under mild structural conditions, a HM-RI predictor corresponds to the radial inverse of a valid set of monotone constraints. To reduce training overhead, we further develop relaxed monotonicity conditions that remain compatible with POA. Across multiple monotone optimisation benchmarks (indefinite quadratic programming, multiplicative programming, and transmit power optimisation), our approach yields substantial speed-ups in comparison to direct function estimation while maintaining strong solution quality, outperforming baselines that do not exploit monotonic structure.

Method: Created formal abstraction of RL algorithms spanning model-based, distributional, and model-free approaches. Integrated active inference into distributional RL framework without requiring transition dynamics modeling.

Result: Performance advantages of active inference become accessible without the need for transition dynamics modeling, potentially improving sample efficiency in complex robotic environments.

Conclusion: Successfully bridges active inference theory with practical RL frameworks, making biological-inspired efficient control accessible to artificial intelligence systems.

Abstract: Optimal control of complex environments with robotic systems faces two complementary and intertwined challenges: efficient organization of sensory state information and far-sighted action planning. Because the reinforcement learning framework addresses only the latter, it tends to deliver sample-inefficient solutions. Active inference is the state-of-the-art process theory that explains how biological brains handle this dual problem. However, its applications to artificial intelligence have thus far been limited to extensions of existing model-based approaches. We present a formal abstraction of reinforcement learning algorithms that spans model-based, distributional, and model-free approaches. This abstraction seamlessly integrates active inference into the distributional reinforcement learning framework, making its performance advantages accessible without transition dynamics modeling.

Method: Developed a pre-trained encoder trained on 357,709 children across 44 countries using UNICEF survey data, enabling few-shot learning with transfer learning approach.

Result: With only 50 training samples, achieves AUC of 0.65 (outperforming cold-start gradient boosting by 8-12%); at N=500, AUC of 0.73; zero-shot deployment to unseen countries achieves AUCs up to 0.84.

Conclusion: Pre-trained encoders can transform feasibility of ML for Sustainable Development Goal 4.2.1 monitoring in resource-constrained settings by enabling few-shot generalization.

Abstract: A large number of children experience preventable developmental delays each year, yet the deployment of machine learning in new countries has been stymied by a data bottleneck: reliable models require thousands of samples, while new programs begin with fewer than 100. We introduce the first pre-trained encoder for global child development, trained on 357,709 children across 44 countries using UNICEF survey data. With only 50 training samples, the pre-trained encoder achieves an average AUC of 0.65 (95% CI: 0.56-0.72), outperforming cold-start gradient boosting at 0.61 by 8-12% across regions. At N=500, the encoder achieves an AUC of 0.73. Zero-shot deployment to unseen countries achieves AUCs up to 0.84. We apply a transfer learning bound to explain why pre-training diversity enables few-shot generalization. These results establish that pre-trained encoders can transform the feasibility of ML for SDG 4.2.1 monitoring in resource-constrained settings.

Method: Develops a framework exposing causal dependencies between parameters, beliefs, and controls. Derives a general information-gathering cost based on Massey’s directed information that assumes only Markov dynamics with additive noise, remaining agnostic to other modeling choices.

Result: Shows that mutual information cost used in existing literature is a special case of their cost. Establishes explicit connection between mutual information cost and information gain in linearized Bayesian estimation, providing theoretical justification for mutual information-based active learning.

Conclusion: Provides a unifying theoretical framework for active information gathering that generalizes existing approaches and offers practical utility across linear, nonlinear, and multi-agent systems.

Abstract: An agent operating in an unknown dynamical system must learn its dynamics from observations. Active information gathering accelerates this learning, but existing methods derive bespoke costs for specific modeling choices: dynamics models, belief update procedures, observation models, and planners. We present a unifying framework that decouples these choices from the information-gathering cost by explicitly exposing the causal dependencies between parameters, beliefs, and controls. Using this framework, we derive a general information-gathering cost based on Massey’s directed information that assumes only Markov dynamics with additive noise and is otherwise agnostic to modeling choices. We prove that the mutual information cost used in existing literature is a special case of our cost. Then, we leverage our framework to establish an explicit connection between the mutual information cost and information gain in linearized Bayesian estimation, thereby providing theoretical justification for mutual information-based active learning approaches. Finally, we illustrate the practical utility of our framework through experiments spanning linear, nonlinear, and multi-agent systems.

Method: Two-phase approach: 1) ACE algorithm uses weak oracle confidence intervals to focus scarce strong oracle queries on critical boundary items near top-k threshold. 2) ACE-W extends with adaptive weak budget allocation before running ACE to further reduce strong costs.

Result: ACE achieves O(m(4ε_max)) strong calls matching theoretical lower bound Ω(m(ε_max)), where m(·) is near-tie mass around top-k threshold. ACE-W further reduces strong costs through adaptive weak budget allocation.

Conclusion: Adaptive certification algorithms can significantly reduce expensive strong oracle calls for top-k identification while maintaining theoretical guarantees, making expensive expert verification more practical.

Abstract: Identifying the top-$k$ items is fundamental but often prohibitive when exact valuations are expensive. We study a two-oracle setting with a fast, noisy weak oracle and a scarce, high-fidelity strong oracle (e.g., human expert verification or expensive simulation). We first analyze a simple screen-then-certify baseline (STC) and prove it makes at most $m(4\varepsilon_{\max})$ strong calls given jointly valid weak confidence intervals with maximum radius $\varepsilon_{\max}$, where $m(\cdot)$ denotes the near-tie mass around the top-$k$ threshold. We establish a conditional lower bound of $Ω(m(\varepsilon_{\max}))$ for any algorithm given the same weak uncertainty. Our main contribution is ACE, an adaptive certification algorithm that focuses strong queries on critical boundary items, achieving the same $O(m(4\varepsilon_{\max}))$ bound while reducing strong calls in practice. We then introduce ACE-W, a fully adaptive two-phase method that allocates weak budget adaptively before running ACE, further reducing strong costs.

Method: Freezes model weights and adapts each test sample by optimizing low-dimensional coefficient vector in pre-computed principal latent subspace (via truncated SVD). Uses CMA-ES to optimize coefficients for prediction confidence, effectively optimizing Gaussian-smoothed objective for stability near decision boundaries.

Result: Achieves state-of-the-art accuracy across six benchmarks and multiple architectures under strict and continual single-instance protocols. Reduces compute by up to 63× and peak memory by 11×. Demonstrated on-device deployment on ZYNQ-7020 platform.

Conclusion: ELaTTA provides efficient gradient-free test-time adaptation suitable for edge deployment with minimal overhead, addressing practical constraints of real-world deployment.

Abstract: Real-world deployment often exposes models to distribution shifts, making test-time adaptation (TTA) critical for robustness. Yet most TTA methods are unfriendly to edge deployment, as they rely on backpropagation, activation buffering, or test-time mini-batches, leading to high latency and memory overhead. We propose $\textbf{ELaTTA}$ ($\textit{Efficient Latent Test-Time Adaptation}$), a gradient-free framework for single-instance TTA under strict on-device constraints. ELaTTA freezes model weights and adapts each test sample by optimizing a low-dimensional coefficient vector in a source-induced principal latent subspace, pre-computed offline via truncated SVD and stored with negligible overhead. At inference, ELaTTA encourages prediction confidence by optimizing the $k$-D coefficients with CMA-ES, effectively optimizing a Gaussian-smoothed objective and improving stability near decision boundaries. Across six benchmarks and multiple architectures, ELaTTA achieves state-of-the-art accuracy under both strict and continual single-instance protocols, while reducing compute by up to $\textit{63$\times$}$ and peak memory by $\textit{11$\times$}$. We further demonstrate on-device deployment on a ZYNQ-7020 platform. Code will be released upon acceptance.

Method: Proposed architecture-conditioned scaling laws that decompose loss dependence on depth and width. Analyzed 30 transformer architectures from 17M to 7B parameters trained on representative high-compute samples to derive scaling relationships.

Result: Found optimal depth scales as D* ~ C^0.12 and optimal width as W* ~ C^0.34, meaning width grows 2.8x faster than depth. Discovered critical depth phenomenon: beyond D_crit ~ W^0.44, adding layers increases loss despite adding parameters. Validated with R^2 = 0.922 across architectures.

Conclusion: Optimal depth-width tradeoffs persist at production scale, with width being more important than depth for scaling. The “Depth Delusion” shows that beyond critical depth, deeper models underperform despite having more parameters.

Abstract: Neural scaling laws describe how language model loss decreases with parameters and data, but treat architecture as interchangeable–a billion parameters could arise from a shallow-wide model (10 layers & 8,192 hidden dimension) or a deep-narrow one (80 layers & 2,048 hidden dimension). We propose architecture-conditioned scaling laws decomposing this dependence, finding that optimal depth scales as D* ~ C^0.12 while optimal width scales as W* ~ C^0.34, meaning width should grow 2.8x faster than depth. We discover a critical depth phenomenon: beyond D_crit ~ W^0.44 (sublinear in W), adding layers increases loss despite adding parameters–the Depth Delusion. Empirically, we validate these findings across 30 transformer architectures spanning 17M to 7B parameters, each trained on representative high-compute samples, achieving R^2 = 0.922. Our central finding: at 7B scale, a 64-layer model (6.38B params) underperforms a 32-layer model (6.86B params) by 0.12 nats, despite being significantly deeper. This demonstrates that optimal depth-width tradeoffs persist at the production scale.

Method: Modified SEANet-style vector-quantizer reduces multichannel MEG into flattened token stream, then train Qwen2.5-VL backbone from scratch for next-token prediction and recursive MEG generation. Evaluation uses task-matched tests: on-manifold stability and conditional specificity with neurophysiologically grounded metrics.

Result: Model trained on CamCAN and Omega datasets, evaluated on held-out MOUS dataset showing cross-dataset generalization. Generations remain stable over long rollouts and are closer to correct continuation than swapped controls across metrics.

Conclusion: Successfully developed a scalable autoregressive model for MEG generation that generalizes across datasets and maintains neurophysiological fidelity over long sequences.

Abstract: We present a large autoregressive model for source-space MEG that scales next-token prediction to long context across datasets and scanners: handling a corpus of over 500 hours and thousands of sessions across the three largest MEG datasets. A modified SEANet-style vector-quantizer reduces multichannel MEG into a flattened token stream on which we train a Qwen2.5-VL backbone from scratch to predict the next brain token and to recursively generate minutes of MEG from up to a minute of context. To evaluate long-horizon generation, we introduce task-matched tests: (i) on-manifold stability via generated-only drift compared to the time-resolved distribution of real sliding windows, and (ii) conditional specificity via correct context versus prompt-swap controls using a neurophysiologically grounded metric set. We train on CamCAN and Omega and run all analyses on held-out MOUS, establishing cross-dataset generalization. Across metrics, generations remain relatively stable over long rollouts and are closer to the correct continuation than swapped controls. Code available at: https://github.com/ricsinaruto/brain-gen.

Method: MADE simulates closed-loop discovery campaigns where agents propose, evaluate, and refine candidate materials under constrained oracle budgets. It formalizes discovery as searching for thermodynamically stable compounds relative to convex hulls, with flexible composition of agents from interchangeable components like generative models, filters, and planners.

Result: The framework enables systematic experiments across material systems, ablation studies of pipeline components, and comparison of method scalability with system complexity. It demonstrates evaluation of diverse workflows from fixed pipelines to fully agentic systems with tool use.

Conclusion: MADE provides a comprehensive benchmarking framework for end-to-end autonomous materials discovery that captures the sequential, resource-limited nature of real scientific workflows, enabling better evaluation and comparison of discovery algorithms.

Abstract: Existing benchmarks for computational materials discovery primarily evaluate static predictive tasks or isolated computational sub-tasks. While valuable, these evaluations neglect the inherently iterative and adaptive nature of scientific discovery. We introduce MAterials Discovery Environments (MADE), a novel framework for benchmarking end-to-end autonomous materials discovery pipelines. MADE simulates closed-loop discovery campaigns in which an agent or algorithm proposes, evaluates, and refines candidate materials under a constrained oracle budget, capturing the sequential and resource-limited nature of real discovery workflows. We formalize discovery as a search for thermodynamically stable compounds relative to a given convex hull, and evaluate efficacy and efficiency via comparison to baseline algorithms. The framework is flexible; users can compose discovery agents from interchangeable components such as generative models, filters, and planners, enabling the study of arbitrary workflows ranging from fixed pipelines to fully agentic systems with tool use and adaptive decision making. We demonstrate this by conducting systematic experiments across a family of systems, enabling ablation of components in discovery pipelines, and comparison of how methods scale with system complexity.

Method: Formulates AMR as a system of collaborating homogeneous agents that iteratively split into multiple new agents. Uses spatial reward formulation focused on reducing maximum mesh element error. ASMR++ enables efficient, stable optimization and generates adaptive meshes at user-defined resolution during inference.

Result: Outperforms heuristic approaches and learned baselines, matches performance of expensive error-based oracle AMR strategies. Generalizes to different domains during inference. Produces meshes that simulate up to 2 orders of magnitude faster than uniform refinements in demanding settings.

Conclusion: ASMR++ provides an effective agent-based approach to AMR that offers computational efficiency, generalization capability, and performance matching expensive oracle methods while being more scalable than existing ML-based approaches.

Abstract: Simulating physical systems is essential in engineering, but analytical solutions are limited to straightforward problems. Consequently, numerical methods like the Finite Element Method (FEM) are widely used. However, the FEM becomes computationally expensive as problem complexity and accuracy demands increase. Adaptive Mesh Refinement (AMR) improves the FEM by dynamically placing mesh elements on the domain, balancing computational speed and accuracy. Classical AMR depends on heuristics or expensive error estimators, which may lead to suboptimal performance for complex simulations. While AMR methods based on machine learning are promising, they currently only scale to simple problems. In this work, we formulate AMR as a system of collaborating, homogeneous agents that iteratively split into multiple new agents. This agent-wise perspective enables a spatial reward formulation focused on reducing the maximum mesh element error. Our approach, Adaptive Swarm Mesh Refinement++ (ASMR++), offers efficient, stable optimization and generates highly adaptive meshes at user-defined resolution at inference time. Extensive experiments demonstrate that ASMR++ outperforms heuristic approaches and learned baselines, matching the performance of expensive error-based oracle AMR strategies. ASMR additionally generalizes to different domains during inference, and produces meshes that simulate up to 2 orders of magnitude faster than uniform refinements in more demanding settings.

Method: Introduces two benchmarks: ORDebug (5,000+ problems with 9 error types) that evaluates iterative self-correction with solver feedback, and ORBias (2,000 newsvendor instances) that measures systematic deviations from optimal policies.

Result: Domain-specific RLVR training enables an 8B model to surpass frontier APIs: 95.3% vs 86.2% recovery rate, 62.4% vs 47.8% diagnostic accuracy, and 2.25 vs 3.78 steps to resolution. Curriculum training reduces systematic bias by 48%.

Conclusion: Process-level evaluation with verifiable oracles enables targeted training that outperforms scale, demonstrating the importance of evaluating LLMs in realistic iterative workflows rather than one-shot tasks.

Abstract: Operations Research practitioners routinely debug infeasible models through an iterative process: analyzing Irreducible Infeasible Subsystems (\IIS{}), identifying constraint conflicts, and systematically repairing formulations until feasibility is achieved. Yet existing LLM benchmarks evaluate OR as one-shot translation – given a problem description, generate solver code – ignoring this diagnostic loop entirely. We introduce two benchmarks that place the \textbf{solver in the evaluation loop}. \textbf{\ORDebug{}} evaluates iterative self-correction through 5,000+ problems spanning 9 error types; each repair action triggers solver re-execution and \IIS{} recomputation, providing deterministic, verifiable feedback. \textbf{\ORBias{}} evaluates behavioral rationality through 2,000 newsvendor instances (1,000 ID + 1,000 OOD), measuring systematic deviations from closed-form optimal policies. Across 26 models and 12,000+ samples, we find that domain-specific RLVR training enables an 8B model to surpass frontier APIs: 95.3% vs 86.2% recovery rate (+9.1%), 62.4% vs 47.8% diagnostic accuracy (+14.6%), and 2.25 vs 3.78 steps to resolution (1.7$\times$ faster). On \ORBias{}, curriculum training achieves the only negative ID$\rightarrow$OOD bias drift among models evaluated (-9.6%), reducing systematic bias by 48% (from 20.0% to 10.4%). These results demonstrate that process-level evaluation with verifiable oracles enables targeted training that outperforms scale.

Method: Formulates TTA as gradient-free recursive Bayesian estimation using learned dynamic transition matrices as temporal priors. Includes a likelihood-ratio gate for safety when temporal evidence is weak. The approach is lightweight and model-agnostic.

Result: Extensive experiments across image classification, wearable/physiological signal analysis, and language sentiment analysis show OATTA consistently boosts established baselines, improving accuracy by up to 6.35%.

Conclusion: Modeling temporal dynamics provides a critical orthogonal signal beyond standard order-agnostic TTA approaches, demonstrating the importance of temporal structure in test-time adaptation.

Abstract: Test-Time Adaptation (TTA) enables pre-trained models to adjust to distribution shift by learning from unlabeled test-time streams. However, existing methods typically treat these streams as independent samples, overlooking the supervisory signal inherent in temporal dynamics. To address this, we introduce Order-Aware Test-Time Adaptation (OATTA). We formulate test-time adaptation as a gradient-free recursive Bayesian estimation task, using a learned dynamic transition matrix as a temporal prior to refine the base model’s predictions. To ensure safety in weakly structured streams, we introduce a likelihood-ratio gate (LLR) that reverts to the base predictor when temporal evidence is absent. OATTA is a lightweight, model-agnostic module that incurs negligible computational overhead. Extensive experiments across image classification, wearable and physiological signal analysis, and language sentiment analysis demonstrate its universality; OATTA consistently boosts established baselines, improving accuracy by up to 6.35%. Our findings establish that modeling temporal dynamics provides a critical, orthogonal signal beyond standard order-agnostic TTA approaches.

Method: Introduces Conditional Denoising Model (CDM) - a generative model that learns physical manifold geometry by training network to restore clean states from noisy ones, learning a vector field pointing toward valid solution subspace. Uses time-independent formulation transforming inference into deterministic fixed-point iteration.

Result: CDM achieves higher parameter and data efficiency than physics-consistent baselines on low-temperature plasma physics benchmark. Denoising objective acts as powerful implicit regularizer - despite never seeing governing equations during training, model adheres to physical constraints more strictly than baselines with explicit physics losses.

Conclusion: CDM provides novel approach to physics-consistent surrogate modeling by learning solution manifold geometry through denoising, enabling strict physical adherence without explicit constraint enforcement.

Abstract: Surrogate modeling for complex physical systems typically faces a trade-off between data-fitting accuracy and physical consistency. Physics-consistent approaches typically treat physical laws as soft constraints within the loss function, a strategy that frequently fails to guarantee strict adherence to the governing equations, or rely on post-processing corrections that do not intrinsically learn the underlying solution geometry. To address these limitations, we introduce the {Conditional Denoising Model (CDM)}, a generative model designed to learn the geometry of the physical manifold itself. By training the network to restore clean states from noisy ones, the model learns a vector field that points continuously towards the valid solution subspace. We introduce a time-independent formulation that transforms inference into a deterministic fixed-point iteration, effectively projecting noisy approximations onto the equilibrium manifold. Validated on a low-temperature plasma physics and chemistry benchmark, the CDM achieves higher parameter and data efficiency than physics-consistent baselines. Crucially, we demonstrate that the denoising objective acts as a powerful implicit regularizer: despite never seeing the governing equations during training, the model adheres to physical constraints more strictly than baselines trained with explicit physics losses.

Method: Proposes Statistical-prior Informed Generative Masking Architecture (SIGMA-PPG) with Prior-Guided Adversarial Masking mechanism using reinforcement learning-driven teacher with statistical priors, and semantic consistency constraint via vector quantization.

Result: Pre-trained on over 120,000 hours of data, achieves superior average performance compared to five state-of-the-art baselines across 12 diverse downstream tasks.

Conclusion: SIGMA-PPG effectively addresses PPG signal challenges through generative modeling with adversarial masking and semantic consistency, demonstrating strong performance across multiple tasks.

Abstract: Current foundation model for photoplethysmography (PPG) signals is challenged by the intrinsic redundancy and noise of the signal. Standard masked modeling often yields trivial solutions while contrastive methods lack morphological precision. To address these limitations, we propose a Statistical-prior Informed Generative Masking Architecture (SIGMA-PPG), a generative foundation model featuring a Prior-Guided Adversarial Masking mechanism, where a reinforcement learning-driven teacher leverages statistical priors to create challenging learning paths that prevent overfitting to noise. We also incorporate a semantic consistency constraint via vector quantization to ensure that physiologically identical waveforms (even those altered by recording artifacts or minor perturbations) map to shared indices. This enhances codebook semantic density and eliminates redundant feature structures. Pre-trained on over 120,000 hours of data, SIGMA-PPG achieves superior average performance compared to five state-of-the-art baselines across 12 diverse downstream tasks. The code is available at https://github.com/ZonghengGuo/SigmaPPG.

Method: Introduces Predict-Project-Renoise (PPR) - an iterative algorithm that samples from constrained marginals by alternating between denoising predictions, projecting onto the feasible set, and renoising; defines a constrained forward process that diffuses only over constraint-satisfying samples

Result: PPR reduces constraint violations by over an order of magnitude while improving sample consistency and better matching the true constrained distribution compared to baselines in experiments on 2D distributions, PDEs, and global weather forecasting

Conclusion: The constrained sampling framework successfully enforces hard constraints in diffusion models for scientific applications, making neural emulators more physically accurate and reliable

Abstract: Neural emulators based on diffusion models show promise for scientific applications, but vanilla models cannot guarantee physical accuracy or constraint satisfaction. We address this by introducing a constrained sampling framework that enforces hard constraints, such as physical laws or observational consistency, at generation time. Our approach defines a constrained forward process that diffuses only over the feasible set of constraint-satisfying samples, inducing constrained marginal distributions. To reverse this, we propose Predict-Project-Renoise (PPR), an iterative algorithm that samples from the constrained marginals by alternating between denoising predictions, projecting onto the feasible set, and renoising. Experiments on 2D distributions, PDEs, and global weather forecasting demonstrate that PPR reduces constraint violations by over an order of magnitude while improving sample consistency and better matching the true constrained distribution compared to baselines.

Method: Formulates visual reasoning using video generation models, evaluating on two regimes: Maze Navigation (sequential discrete planning with low visual change) and Tangram Puzzle (continuous manipulation with high visual change).

Result: Three key findings: 1) Robust zero-shot generalization on unseen data distributions, 2) Effective use of visual context as explicit control, 3) Test-time scaling law where longer generated videos improve zero-shot generalization to complex paths.

Conclusion: Video generation is not just a media tool but a scalable, generalizable paradigm for visual reasoning that can handle both discrete planning and continuous manipulation tasks.

Abstract: Vision-Language Models have excelled at textual reasoning, but they often struggle with fine-grained spatial understanding and continuous action planning, failing to simulate the dynamics required for complex visual reasoning. In this work, we formulate visual reasoning by means of video generation models, positing that generated frames can act as intermediate reasoning steps between initial states and solutions. We evaluate their capacity in two distinct regimes: Maze Navigation for sequential discrete planning with low visual change and Tangram Puzzle for continuous manipulation with high visual change. Our experiments reveal three critical insights: (1) Robust Zero-Shot Generalization: In both tasks, the model demonstrates strong performance on unseen data distributions without specific finetuning. (2) Visual Context: The model effectively uses visual context as explicit control, such as agent icons and tangram shapes, enabling it to maintain high visual consistency and adapt its planning capability robustly to unseen patterns. (3) Visual Test-Time Scaling: We observe a test-time scaling law in sequential planning; increasing the generated video length (visual inference budget) empowers better zero-shot generalization to spatially and temporally complex paths. These findings suggest that video generation is not merely a media tool, but a scalable, generalizable paradigm for visual reasoning.

Method: Proposes TACO, a model-agnostic test-time adaptation framework that uses strategic warm-starting to partially relax trained parameters while preserving inductive bias. This enables rapid unsupervised adaptation at test time without the computational cost of training from scratch or the poor performance of naive fine-tuning.

Result: TACO achieves better solution quality than naive fine-tuning of generalizable models or optimizing instance-specific models from scratch, with negligible additional computational cost. Experiments on Minimum Vertex Cover and Maximum Clique problems demonstrate effectiveness across static, distribution-shifted, and dynamic CO problems.

Conclusion: TACO successfully bridges generalizable and instance-specific unsupervised NCO paradigms, providing a practical framework that leverages learned inductive bias while enabling effective test-time adaptation for combinatorial optimization problems.

Abstract: Unsupervised neural combinatorial optimization (NCO) enables learning powerful solvers without access to ground-truth solutions. Existing approaches fall into two disjoint paradigms: models trained for generalization across instances, and instance-specific models optimized independently at test time. While the former are efficient during inference, they lack effective instance-wise adaptability; the latter are flexible but fail to exploit learned inductive structure and are prone to poor local optima. This motivates the central question of our work: how can we leverage the inductive bias learned through generalization while unlocking the flexibility required for effective instance-wise adaptation? We first identify a challenge in bridging these two paradigms: generalization-focused models often constitute poor warm starts for instance-wise optimization, potentially underperforming even randomly initialized models when fine-tuned at test time. To resolve this incompatibility, we propose TACO, a model-agnostic test-time adaptation framework that unifies and extends the two existing paradigms for unsupervised NCO. TACO applies strategic warm-starting to partially relax trained parameters while preserving inductive bias, enabling rapid and effective unsupervised adaptation. Crucially, compared to naively fine-tuning a trained generalizable model or optimizing an instance-specific model from scratch, TACO achieves better solution quality while incurring negligible additional computational cost. Experiments on canonical CO problems, Minimum Vertex Cover and Maximum Clique, demonstrate the effectiveness and robustness of TACO across static, distribution-shifted, and dynamic combinatorial optimization problems, establishing it as a practical bridge between generalizable and instance-specific unsupervised NCO.

Method: SMKC decouples dynamic input structure from anomaly detection using permutation-invariant feature hashing to sketch raw inputs into fixed-size state sequences, constructs hybrid kernel images to capture global temporal structure through pairwise comparisons, and learns normal patterns via masked reconstruction and teacher-student prediction.

Result: Robust log-distance channels provide primary discriminative signal while cosine representations often fail; detector using random projections and nearest neighbors on SMKC representation performs competitively with fully trained baselines without gradient updates.

Conclusion: SMKC effectively handles sensor churn in multivariate time series anomaly detection, with the representation itself being highly effective, offering practical cold-start solutions for resource-constrained deployments.

Abstract: Conventional anomaly detection in multivariate time series relies on the assumption that the set of observed variables remains static. In operational environments, however, monitoring systems frequently experience sensor churn. Signals may appear, disappear, or be renamed, creating data windows where the cardinality varies and may include values unseen during training. To address this challenge, we propose SMKC, a framework that decouples the dynamic input structure from the anomaly detector. We first employ permutation-invariant feature hashing to sketch raw inputs into a fixed size state sequence. We then construct a hybrid kernel image to capture global temporal structure through pairwise comparisons of the sequence and its derivatives. The model learns normal patterns using masked reconstruction and a teacher-student prediction objective. Our evaluation reveals that robust log-distance channels provide the primary discriminative signal, whereas cosine representations often fail to capture sufficient contrast. Notably, we find that a detector using random projections and nearest neighbors on the SMKC representation performs competitively with fully trained baselines without requiring gradient updates. This highlights the effectiveness of the representation itself and offers a practical cold-start solution for resource-constrained deployments.

Method: Presents Snowball, a digital, scalable, all-to-all coupled Ising machine architecture. It integrates dual-mode Markov chain Monte Carlo (MCMC) spin selection with asynchronous spin updates to promote convergence. The digital architecture supports wide, configurable coupling precision, unlike analog realizations. Implemented as a prototype on an AMD Alveo U250 accelerator card.

Result: Achieves an 8× reduction in time-to-solution relative to a state-of-the-art Ising machine on the same benchmark instance. The digital architecture enables configurable coupling precision at high bit widths.

Conclusion: Snowball demonstrates significant improvements in Ising machine performance through digital architecture, advanced spin selection algorithms, and scalable precision, making practical deployment more feasible for combinatorial optimization problems.

Abstract: Ising machines have emerged as accelerators for combinatorial optimization. To enable practical deployment, this work aims to reduce time-to-solution by addressing three challenges: (1) hardware topology, (2) spin selection and update algorithms, and (3) scalable coupling-coefficient precision. Restricted topologies require minor embedding; naive parallel updates can oscillate or stall; and limited precision can preclude feasible mappings or degrade solution quality. This work presents Snowball, a digital, scalable, all-to-all coupled Ising machine that integrates dual-mode Markov chain Monte Carlo spin selection with asynchronous spin updates to promote convergence and reduce time-to-solution. The digital architecture supports wide, configurable coupling precision, unlike many analog realizations at high bit widths. A prototype on an AMD Alveo U250 accelerator card achieves an 8$\times$ reduction in time-to-solution relative to a state-of-the-art Ising machine on the same benchmark instance.

Method: Proposes a human-LLM collaborative framework that decouples transformation operation proposal (using LLMs to generate candidates) from selection (guided by explicit modeling of operation utility and uncertainty). Incorporates selective human expert preference feedback to compare which operations are more promising, especially useful when accurate utility estimation is difficult in early rounds.

Result: Evaluations on synthetic studies and real user studies demonstrate improved feature engineering performance across various tabular datasets and reduced cognitive load for users during the feature engineering process.

Conclusion: The proposed framework effectively combines LLM capabilities for operation proposal with principled selection mechanisms and human feedback, leading to better feature engineering outcomes and reduced user burden compared to black-box LLM approaches.

Abstract: Large language models (LLMs) are increasingly used to automate feature engineering in tabular learning. Given task-specific information, LLMs can propose diverse feature transformation operations to enhance downstream model performance. However, current approaches typically assign the LLM as a black-box optimizer, responsible for both proposing and selecting operations based solely on its internal heuristics, which often lack calibrated estimations of operation utility and consequently lead to repeated exploration of low-yield operations without a principled strategy for prioritizing promising directions. In this paper, we propose a human-LLM collaborative feature engineering framework for tabular learning. We begin by decoupling the transformation operation proposal and selection processes, where LLMs are used solely to generate operation candidates, while the selection is guided by explicitly modeling the utility and uncertainty of each proposed operation. Since accurate utility estimation can be difficult especially in the early rounds of feature engineering, we design a mechanism within the framework that selectively elicits and incorporates human expert preference feedback, comparing which operations are more promising, into the selection process to help identify more effective operations. Our evaluations on both the synthetic study and the real user study demonstrate that the proposed framework improves feature engineering performance across a variety of tabular datasets and reduces users’ cognitive load during the feature engineering process.

Method: The paper introduces SUB0-GFN which uses submodularity to compute upper bounds on rewards of unobserved compositional objects. It analyzes probability and coverage of such bounds, then applies Optimism in the Face of Uncertainty principle to train GFNs using these bounds instead of actual reward queries.

Result: SUB0-GFN generates orders of magnitude more training data than classical GFNs for the same number of reward function queries. It demonstrates effectiveness in distribution matching and high-quality candidate generation on both synthetic and real-world submodular tasks.

Conclusion: Harnessing submodular structure through upper bounds enables dramatically more efficient GFN training, making them more practical for applications where reward queries are expensive.

Abstract: Generative Flow Networks (GFlowNets; GFNs) are a class of generative models that learn to sample compositional objects proportionally to their a priori unknown value, their reward. We focus on the case where the reward has a specified, actionable structure, namely that it is submodular. We show submodularity can be harnessed to retrieve upper bounds on the reward of compositional objects that have not yet been observed. We provide in-depth analyses of the probability of such bounds occurring, as well as how many unobserved compositional objects can be covered by a bound. Following the Optimism in the Face of Uncertainty principle, we then introduce SUBo-GFN, which uses the submodular upper bounds to train a GFN. We show that SUBo-GFN generates orders of magnitude more training data than classical GFNs for the same number of queries to the reward function. We demonstrate the effectiveness of SUBo-GFN in terms of distribution matching and high-quality candidate generation on synthetic and real-world submodular tasks.

Method: Textual Equilibrium Propagation (TEP) uses local learning with two phases: free phase where local LLM critics iteratively refine prompts to equilibrium, and nudged phase with proximal prompt edits using forward signaling rather than backward feedback chains

Result: TEP consistently improves accuracy and efficiency over global propagation methods like TextGrad across long-horizon QA benchmarks and multi-agent tool-use datasets, with gains increasing with system depth

Conclusion: TEP enables effective optimization of deep compound AI systems while preserving practicality of black-box LLM components, addressing scaling issues of global textual feedback methods

Abstract: Large language models (LLMs) are increasingly deployed as part of compound AI systems that coordinate multiple modules (e.g., retrievers, tools, verifiers) over long-horizon workflows. Recent approaches that propagate textual feedback globally (e.g., TextGrad) make it feasible to optimize such pipelines, but we find that performance degrades as system depth grows. In particular, long-horizon agentic workflows exhibit two depth-scaling failure modes: 1) exploding textual gradient, where textual feedback grows exponentially with depth, leading to prohibitively long message and amplifies evaluation biases; and 2) vanishing textual gradient, where limited long-context ability causes models overemphasize partial feedback and compression of lengthy feedback causes downstream messages to lose specificity gradually as they propagate many hops upstream. To mitigate these issues, we introduce Textual Equilibrium Propagation (TEP), a local learning principle inspired by Equilibrium Propagation in energy-based models. TEP includes two phases: 1) a free phase where a local LLM critics iteratively refine prompts until reaching equilibrium (no further improvements are suggested); and 2) a nudged phase which applies proximal prompt edits with bounded modification intensity, using task-level objectives that propagate via forward signaling rather than backward feedback chains. This design supports local prompt optimization followed by controlled adaptation toward global goals without the computational burden and signal degradation of global textual backpropagation. Across long-horizon QA benchmarks and multi-agent tool-use dataset, TEP consistently improves accuracy and efficiency over global propagation methods such as TextGrad. The gains grows with depth, while preserving the practicality of black-box LLM components in deep compound AI system.

Method: Survey methodology: 1) Discuss main challenges of distribution shifts in graph learning and outline unified problem setting, 2) Organize existing approaches based on whether they operate under fixed task specifications or support generalization across heterogeneous tasks, 3) Summarize corresponding OOD handling strategies and pretraining objectives, 4) Review common evaluation protocols.

Result: The paper provides a comprehensive survey of graph foundation models focusing on OOD generalization, organizing the field into systematic categories and identifying key strategies for handling distribution shifts in graph learning.

Conclusion: This is the first survey for OOD generalization in graph foundation models, highlighting the importance of addressing distribution shifts and providing a structured overview of current approaches while identifying open research directions for future work.

Abstract: Graphs are a fundamental data structure for representing relational information in domains such as social networks, molecular systems, and knowledge graphs. However, graph learning models often suffer from limited generalization when applied beyond their training distributions. In practice, distribution shifts may arise from changes in graph structure, domain semantics, available modalities, or task formulations. To address these challenges, graph foundation models (GFMs) have recently emerged, aiming to learn general-purpose representations through large-scale pretraining across diverse graphs and tasks. In this survey, we review recent progress on GFMs from the perspective of out-of-distribution (OOD) generalization. We first discuss the main challenges posed by distribution shifts in graph learning and outline a unified problem setting. We then organize existing approaches based on whether they are designed to operate under a fixed task specification or to support generalization across heterogeneous task formulations, and summarize the corresponding OOD handling strategies and pretraining objectives. Finally, we review common evaluation protocols and discuss open directions for future research. To the best of our knowledge, this paper is the first survey for OOD generalization in GFMs.

Method: LOCUS uses an attention-based approach that generates embeddings through deterministic forward passes over query encodings and evaluation scores via an encoder model. It also trains a correctness predictor using model embeddings and query encodings for routing.

Result: LOCUS needs up to 4.8x fewer query evaluation samples than baselines to produce informative embeddings. The learned embedding space is geometrically meaningful, enabling model comparison, clustering, portfolio selection, and proxy modeling.

Conclusion: LOCUS provides an effective method for compactly representing LLM capabilities, enabling efficient model management, routing, and various downstream applications with minimal evaluation data.

Abstract: The rapidly growing ecosystem of Large Language Models (LLMs) makes it increasingly challenging to manage and utilize the vast and dynamic pool of models effectively. We propose LOCUS, a method that produces low-dimensional vector embeddings that compactly represent a language model’s capabilities across queries. LOCUS is an attention-based approach that generates embeddings by a deterministic forward pass over query encodings and evaluation scores via an encoder model, enabling seamless incorporation of new models to the pool and refinement of existing model embeddings without having to perform any retraining. We additionally train a correctness predictor that uses model embeddings and query encodings to achieve state-of-the-art routing accuracy on unseen queries. Experiments show that LOCUS needs up to 4.8x fewer query evaluation samples than baselines to produce informative and robust embeddings. Moreover, the learned embedding space is geometrically meaningful: proximity reflects model similarity, enabling a range of downstream applications including model comparison and clustering, model portfolio selection, and resilient proxies of unavailable models.

Method: MapPFN uses prior-data fitted networks pretrained on synthetic data generated from a prior over causal perturbations, employing in-context learning to predict post-perturbation distributions without gradient-based optimization.

Result: Despite being pretrained only on in silico gene knockouts, MapPFN identifies differentially expressed genes with performance matching models trained on real single-cell data.

Conclusion: MapPFN demonstrates effective meta-learning for perturbation effect estimation across biological contexts using in-context learning, enabling adaptation to unseen contexts without retraining.

Abstract: Planning effective interventions in biological systems requires treatment-effect models that adapt to unseen biological contexts by identifying their specific underlying mechanisms. Yet single-cell perturbation datasets span only a handful of biological contexts, and existing methods cannot leverage new interventional evidence at inference time to adapt beyond their training data. To meta-learn a perturbation effect estimator, we present MapPFN, a prior-data fitted network (PFN) pretrained on synthetic data generated from a prior over causal perturbations. Given a set of experiments, MapPFN uses in-context learning to predict post-perturbation distributions, without gradient-based optimization. Despite being pretrained on in silico gene knockouts alone, MapPFN identifies differentially expressed genes, matching the performance of models trained on real single-cell data. Our code and data are available at https://github.com/marvinsxtr/MapPFN.

Method: Benchmarked eight safe RL algorithms on a unified clinical diabetes simulator, evaluated their performance across three diabetes types and three age groups, and implemented test-time shielding that filters unsafe actions using learned dynamics models to restore safety during deployment.

Result: Revealed a safety generalization gap where policies satisfying constraints during training frequently violate safety requirements on unseen patients. Test-time shielding achieved Time-in-Range gains of 13-14% for strong baselines like PPO-Lag and CPO while reducing clinical risk index and glucose variability across all tested algorithms and patient populations.

Conclusion: Training-time safety guarantees do not necessarily transfer to deployment under distribution shift, but test-time shielding using learned dynamics models can effectively restore safety across different algorithms and patient populations, providing a practical solution for safety-critical control domains with distribution shift.

Abstract: Safe Reinforcement Learning (RL) algorithms are typically evaluated under fixed training conditions. We investigate whether training-time safety guarantees transfer to deployment under distribution shift, using diabetes management as a safety-critical testbed. We benchmark safe RL algorithms on a unified clinical simulator and reveal a safety generalization gap: policies satisfying constraints during training frequently violate safety requirements on unseen patients. We demonstrate that test-time shielding, which filters unsafe actions using learned dynamics models, effectively restores safety across algorithms and patient populations. Across eight safe RL algorithms, three diabetes types, and three age groups, shielding achieves Time-in-Range gains of 13–14% for strong baselines such as PPO-Lag and CPO while reducing clinical risk index and glucose variability. Our simulator and benchmark provide a platform for studying safety under distribution shift in safety-critical control domains. Code is available at https://github.com/safe-autonomy-lab/GlucoSim and https://github.com/safe-autonomy-lab/GlucoAlg.

Method: TRACE models transitional mechanisms as convex combinations of finitely many atomic mechanisms with time-varying mixing coefficients. It uses a Mixture-of-Experts framework where each expert learns one atomic mechanism during training, enabling recovery of continuous mechanism trajectories at test time.

Result: Experiments on synthetic and real-world data show TRACE recovers mixing trajectories with up to 0.99 correlation, substantially outperforming discrete-switching baselines. The method generalizes to intermediate mechanism states never observed during training.

Conclusion: TRACE successfully addresses the limitation of discrete mechanism switching by modeling continuous causal transitions, providing theoretical identifiability guarantees and practical performance improvements for real-world temporal systems.

Abstract: Temporal causal representation learning methods assume that causal mechanisms switch instantaneously between discrete domains, yet real-world systems often exhibit continuous mechanism transitions. For example, a vehicle’s dynamics evolve gradually through a turning maneuver, and human gait shifts smoothly from walking to running. We formalize this setting by modeling transitional mechanisms as convex combinations of finitely many atomic mechanisms, governed by time-varying mixing coefficients. Our theoretical contributions establish that both the latent causal variables and the continuous mixing trajectory are jointly identifiable. We further propose TRACE, a Mixture-of-Experts framework where each expert learns one atomic mechanism during training, enabling recovery of mechanism trajectories at test time. This formulation generalizes to intermediate mechanism states never observed during training. Experiments on synthetic and real-world data demonstrate that TRACE recovers mixing trajectories with up to 0.99 correlation, substantially outperforming discrete-switching baselines.

Method: Introduces dynamic cutoff formulation that induces sparsity on atom graphs by targeting specific number of neighbors per atom, implemented on four state-of-the-art MLIPs (MACE, Nequip, Orbv3, TensorNet).

Result: Achieves 2.26x less memory consumption and 2.04x faster inference time with minimal accuracy dropoff compared to fixed cutoff models on materials and molecular datasets.

Conclusion: Dynamic cutoff enables more efficient MLIPs for molecular dynamics simulations while maintaining accuracy, with all implementations to be open-sourced.

Abstract: Machine learning interatomic potentials (MLIPs) have proven to be wildly useful for molecular dynamics simulations, powering countless drug and materials discovery applications. However, MLIPs face two primary bottlenecks preventing them from reaching realistic simulation scales: inference time and memory consumption. In this work, we address both issues by challenging the long-held belief that the cutoff radius for the MLIP must be held to a fixed, constant value. For the first time, we introduce a dynamic cutoff formulation that still leads to stable, long timescale molecular dynamics simulation. In introducing the dynamic cutoff, we are able to induce sparsity onto the underlying atom graph by targeting a specific number of neighbors per atom, significantly reducing both memory consumption and inference time. We show the effectiveness of a dynamic cutoff by implementing it onto 4 state of the art MLIPs: MACE, Nequip, Orbv3, and TensorNet, leading to 2.26x less memory consumption and 2.04x faster inference time, depending on the model and atomic system. We also perform an extensive error analysis and find that the dynamic cutoff models exhibit minimal accuracy dropoff compared to their fixed cutoff counterparts on both materials and molecular datasets. All model implementations and training code will be fully open sourced.

Method: ME-POIs framework encodes individual visits as temporally contextualized embeddings and aligns them with learnable POI representations via contrastive learning. It propagates temporal visit patterns from nearby frequently visited POIs across multiple spatial scales to address long-tail sparsity.

Result: ME-POIs consistently outperforms both text-only and mobility-only baselines across five map enrichment tasks. Notably, ME-POIs trained on mobility data alone can surpass text-only models on certain tasks.

Conclusion: POI function is a critical component of accurate and generalizable POI representations. Augmenting text-based embeddings with mobility data captures both identity and function, enabling better map enrichment and understanding of real-world locations.

Abstract: Recent progress in geospatial foundation models highlights the importance of learning general-purpose representations for real-world locations, particularly points-of-interest (POIs) where human activity concentrates. Existing approaches, however, focus primarily on place identity derived from static textual metadata, or learn representations tied to trajectory context, which capture movement regularities rather than how places are actually used (i.e., POI’s function). We argue that POI function is a missing but essential signal for general POI representations. We introduce Mobility-Embedded POIs (ME-POIs), a framework that augments POI embeddings derived, from language models with large-scale human mobility data to learn POI-centric, context-independent representations grounded in real-world usage. ME-POIs encodes individual visits as temporally contextualized embeddings and aligns them with learnable POI representations via contrastive learning to capture usage patterns across users and time. To address long-tail sparsity, we propose a novel mechanism that propagates temporal visit patterns from nearby, frequently visited POIs across multiple spatial scales. We evaluate ME-POIs on five newly proposed map enrichment tasks, testing its ability to capture both the identity and function of POIs. Across all tasks, augmenting text-based embeddings with ME-POIs consistently outperforms both text-only and mobility-only baselines. Notably, ME-POIs trained on mobility data alone can surpass text-only models on certain tasks, highlighting that POI function is a critical component of accurate and generalizable POI representations.

Method: Train neural networks to predict group operations (modular arithmetic, permutation composition), then design tests to assess if models capture group-theoretic concepts like identity elements, commutativity, and subgroups through representation analysis.

Result: Models learn representations that capture some abstract algebraic properties: evidence of capturing commutativity in modular arithmetic, ability to train linear classifiers that distinguish subgroup elements without explicit labels. However, unable to extract concepts like identity elements.

Conclusion: Even small neural networks can distill interesting abstract structure from mathematical objects, suggesting potential for AI to go beyond simple question-answering in mathematical discovery.

Abstract: While a real-world research program in mathematics may be guided by a motivating question, the process of mathematical discovery is typically open-ended. Ideally, exploration needed to answer the original question will reveal new structures, patterns, and insights that are valuable in their own right. This contrasts with the exam-style paradigm in which the machine learning community typically applies AI to math. To maximize progress in mathematics using AI, we will need to go beyond simple question answering. With this in mind, we explore the extent to which narrow models trained to solve a fixed mathematical task learn broader mathematical structure that can be extracted by a researcher or other AI system. As a basic test case for this, we use the task of training a neural network to predict a group operation (for example, performing modular arithmetic or composition of permutations). We describe a suite of tests designed to assess whether the model captures significant group-theoretic notions such as the identity element, commutativity, or subgroups. Through extensive experimentation we find evidence that models learn representations capable of capturing abstract algebraic properties. For example, we find hints that models capture the commutativity of modular arithmetic. We are also able to train linear classifiers that reliably distinguish between elements of certain subgroups (even though no labels for these subgroups are included in the data). On the other hand, we are unable to extract notions such as the concept of the identity element. Together, our results suggest that in some cases the representations of even small neural networks can be used to distill interesting abstract structure from new mathematical objects.

Method: PARADIS decomposes weather prediction into three functional blocks: 1) Neural Semi-Lagrangian operator for advection using differentiable interpolation on the sphere, 2) depthwise-separable spatial mixing for diffusion, and 3) pointwise channel interactions for local source terms and vertical interactions. This enables operator-level physical structure.

Result: The 1-degree PARADIS model, trained with less than a GPU month, meets or exceeds performance of 0.25-degree traditional and ML baselines including ECMWF HRES forecast and DeepMind’s GraphCast on ERA5-based benchmarks.

Conclusion: Explicit functional decomposition with physics-inspired inductive biases enables efficient and accurate weather forecasting, achieving state-of-the-art performance with significantly reduced computational cost compared to monolithic approaches.

Abstract: Recent machine-learning approaches to weather forecasting often employ a monolithic architecture, where distinct physical mechanisms (advection, transport), diffusion-like mixing, thermodynamic processes, and forcing are represented implicitly within a single large network. This representation is particularly problematic for advection, where long-range transport must be treated with expensive global interaction mechanisms or through deep, stacked convolutional layers. To mitigate this, we present PARADIS, a physics-inspired global weather prediction model that imposes inductive biases on network behavior through a functional decomposition into advection, diffusion, and reaction blocks acting on latent variables. We implement advection through a Neural Semi-Lagrangian operator that performs trajectory-based transport via differentiable interpolation on the sphere, enabling end-to-end learning of both the latent modes to be transported and their characteristic trajectories. Diffusion-like processes are modeled through depthwise-separable spatial mixing, while local source terms and vertical interactions are modeled via pointwise channel interactions, enabling operator-level physical structure. PARADIS provides state-of-the-art forecast skill at a fraction of the training cost. On ERA5-based benchmarks, the 1 degree PARADIS model, with a total training cost of less than a GPU month, meets or exceeds the performance of 0.25 degree traditional and machine-learning baselines, including the ECMWF HRES forecast and DeepMind’s GraphCast.

Method: Clients perform EM steps locally and construct uncertainty sets around each local component maximizer. The central server uses these uncertainty sets to learn potential cluster overlaps between clients and infer the global number of clusters via closed-form computations.

Result: Theoretical probabilistic convergence guarantees under common assumptions, with specific tractable computations for isotropic GMMs. Empirical results show comparable performance to centralized EM and outperformance of existing federated clustering methods.

Conclusion: FedGEM provides an effective federated clustering solution for unknown number of clusters with heterogeneous client data, offering theoretical guarantees and practical performance comparable to centralized approaches.

Abstract: We study the problem of federated clustering when the total number of clusters $K$ across clients is unknown, and the clients have heterogeneous but potentially overlapping cluster sets in their local data. To that end, we develop FedGEM: a federated generalized expectation-maximization algorithm for the training of mixture models with an unknown number of components. Our proposed algorithm relies on each of the clients performing EM steps locally, and constructing an uncertainty set around the maximizer associated with each local component. The central server utilizes the uncertainty sets to learn potential cluster overlaps between clients, and infer the global number of clusters via closed-form computations. We perform a thorough theoretical study of our algorithm, presenting probabilistic convergence guarantees under common assumptions. Subsequently, we study the specific setting of isotropic GMMs, providing tractable, low-complexity computations to be performed by each client during each iteration of the algorithm, as well as rigorously verifying assumptions required for algorithm convergence. We perform various numerical experiments, where we empirically demonstrate that our proposed method achieves comparable performance to centralized EM, and that it outperforms various existing federated clustering methods.

Method: Proposes deterministic algorithm based on contextual linear bandits and self-concordant analysis, plus new Thompson Sampling variant tailored for simple-regret setting. Extends randomized approach to logistic case with similar structure.

Result: Achieves simple regret Õ(d/√T) for deterministic algorithm and Õ(d³/²/√T) for randomized algorithms, both without dependence on exponential constant κ = O(exp(S)). Algorithms are tractable for finite action sets and cheaper to run for randomized versions.

Conclusion: First simple regret guarantees for stochastic contextual logistic bandits with κ-independent bounds, introducing both deterministic and randomized algorithms with theoretical guarantees validated empirically.

Abstract: We study stochastic contextual logistic bandits under the simple regret objective. While simple regret guarantees have been established for the linear case, no such results were previously known for the logistic setting. Building on ideas from contextual linear bandits and self-concordant analysis, we propose the first algorithm that achieves simple regret $\tilde{\mathcal{O}}(d/\sqrt{T})$. Notably, the leading term of our regret bound is free of the constant $κ= \mathcal O(\exp(S))$, where $S$ is a bound on the magnitude of the unknown parameter vector. The algorithm is shown to be fully tractable when the action set is finite. We also introduce a new variant of Thompson Sampling tailored to the simple-regret setting. This yields the first simple regret guarantee for randomized algorithms in stochastic contextual linear bandits, with regret $\tilde{\mathcal{O}}(d^{3/2}/\sqrt{T})$. Extending this method to the logistic case, we obtain a similarly structured Thompson Sampling algorithm that achieves the same regret bound – $\tilde{\mathcal{O}}(d^{3/2}/\sqrt{T})$ – again with no dependence on $κ$ in the leading term. The randomized algorithms, as expected, are cheaper to run than their deterministic counterparts. Finally, we conducted a series of experiments to empirically validate these theoretical guarantees.

Method: Proposes a method that learns optimal feature representations from a one-parameter family of estimators (powers of empirical covariance or precision matrix). This provides a principled way to tune into underlying structure driving critical events. A supervised learning module then classifies the learned representation.

Result: Proves structural consistency of the family and demonstrates empirical soundness on seizure detection and churn prediction, attaining competitive results in both. The optimal covariance power shows evidence of good identifiability while capturing structural signatures.

Conclusion: The method reconciles predictive performance with interpretable statistical structure, offering both early detection capability and explainability for emergent phenomena in complex systems.

Abstract: Emergent phenomena – onset of epileptic seizures, sudden customer churn, or pandemic outbreaks – often arise from hidden causal interactions in complex systems. We propose a machine learning method for their early detection that addresses a core challenge: unveiling and harnessing a system’s latent causal structure despite the data-generating process being unknown and partially observed. The method learns an optimal feature representation from a one-parameter family of estimators – powers of the empirical covariance or precision matrix – offering a principled way to tune in to the underlying structure driving the emergence of critical events. A supervised learning module then classifies the learned representation. We prove structural consistency of the family and demonstrate the empirical soundness of our approach on seizure detection and churn prediction, attaining competitive results in both. Beyond prediction, and toward explainability, we ascertain that the optimal covariance power exhibits evidence of good identifiability while capturing structural signatures, thus reconciling predictive performance with interpretable statistical structure.

Method: Proposes AC2L-GAD combining information-theoretic active selection with counterfactual generation. It identifies structurally complex nodes and generates anomaly-preserving positive augmentations alongside normal negative counterparts for hard contrasts, while restricting expensive counterfactual generation to a strategically selected subset.

Result: Reduces computational overhead by approximately 65% compared to full-graph counterfactual generation while maintaining detection quality. Achieves competitive or superior performance on nine benchmark datasets including real-world financial transaction graphs from GADBench, with notable gains in datasets where anomalies exhibit complex attribute-structure interactions.

Conclusion: AC2L-GAD effectively addresses limitations of existing graph contrastive learning methods through principled counterfactual reasoning and active selection, providing an efficient and effective solution for graph anomaly detection.

Abstract: Graph anomaly detection aims to identify abnormal patterns in networks, but faces significant challenges from label scarcity and extreme class imbalance. While graph contrastive learning offers a promising unsupervised solution, existing methods suffer from two critical limitations: random augmentations break semantic consistency in positive pairs, while naive negative sampling produces trivial, uninformative contrasts. We propose AC2L-GAD, an Active Counterfactual Contrastive Learning framework that addresses both limitations through principled counterfactual reasoning. By combining information-theoretic active selection with counterfactual generation, our approach identifies structurally complex nodes and generates anomaly-preserving positive augmentations alongside normal negative counterparts that provide hard contrasts, while restricting expensive counterfactual generation to a strategically selected subset. This design reduces computational overhead by approximately 65% compared to full-graph counterfactual generation while maintaining detection quality. Experiments on nine benchmark datasets, including real-world financial transaction graphs from GADBench, show that AC2L-GAD achieves competitive or superior performance compared to state-of-the-art baselines, with notable gains in datasets where anomalies exhibit complex attribute-structure interactions.

Method: Proposes an EA foundation model with parallel encoding strategy: uses seed EA pairs as local anchors to guide information flow, initializes two parallel streams simultaneously for anchor-conditioned message passing. Incorporates merged relation graph for global dependencies and learnable interaction module for precise matching.

Result: Extensive experiments verify the framework’s effectiveness and highlight strong generalizability to unseen knowledge graphs.

Conclusion: The proposed parallel encoding approach with anchor guidance successfully addresses the reasoning horizon gap in entity alignment, enabling transferable EA models that can handle unseen KGs without retraining.

Abstract: Entity alignment (EA) is critical for knowledge graph (KG) fusion. Existing EA models lack transferability and are incapable of aligning unseen KGs without retraining. While using graph foundation models (GFMs) offer a solution, we find that directly adapting GFMs to EA remains largely ineffective. This stems from a critical “reasoning horizon gap”: unlike link prediction in GFMs, EA necessitates capturing long-range dependencies across sparse and heterogeneous KG structuresTo address this challenge, we propose a EA foundation model driven by a parallel encoding strategy. We utilize seed EA pairs as local anchors to guide the information flow, initializing and encoding two parallel streams simultaneously. This facilitates anchor-conditioned message passing and significantly shortens the inference trajectory by leveraging local structural proximity instead of global search. Additionally, we incorporate a merged relation graph to model global dependencies and a learnable interaction module for precise matching. Extensive experiments verify the effectiveness of our framework, highlighting its strong generalizability to unseen KGs.

Method: Flow Perturbation++ discretizes the probability-flow ODE and performs unbiased stepwise Jacobian estimation at each integration step, creating a multi-step construction that reduces variance while maintaining unbiasedness. It’s integrated into a Sequential Monte Carlo framework.

Result: The method achieves significantly improved equilibrium sampling on a 1000D Gaussian Mixture Model and the all-atom Chignolin protein compared to Hutchinson-based and single-step Flow Perturbation baselines.

Conclusion: Flow Perturbation++ provides an effective variance-reduced approach for unbiased Boltzmann sampling in high-dimensional systems, overcoming limitations of previous Jacobian estimation methods for continuous normalizing flows.

Abstract: The scalability of continuous normalizing flows (CNFs) for unbiased Boltzmann sampling remains limited in high-dimensional systems due to the cost of Jacobian-determinant evaluation, which requires $D$ backpropagation passes through the flow layers. Existing stochastic Jacobian estimators such as the Hutchinson trace estimator reduce computation but introduce bias, while the recently proposed Flow Perturbation method is unbiased yet suffers from high variance. We present \textbf{Flow Perturbation++}, a variance-reduced extension of Flow Perturbation that discretizes the probability-flow ODE and performs unbiased stepwise Jacobian estimation at each integration step. This multi-step construction retains the unbiasedness of Flow Perturbation while achieves substantially lower estimator variance. Integrated into a Sequential Monte Carlo framework, Flow Perturbation++ achieves significantly improved equilibrium sampling on a 1000D Gaussian Mixture Model and the all-atom Chignolin protein compared with Hutchinson-based and single-step Flow Perturbation baselines.

Method: Proposes Bi-stage Flow Refinement (BFR) with two refinement strategies: 1) latent space alignment for approximately invertible generators, and 2) data space refinement trained with lightweight augmentations. Unlike previous methods, BFR preserves the original ODE trajectory and applies deterministic corrections to generated samples without perturbing sampling dynamics.

Result: Experiments on MNIST, CIFAR-10, and FFHQ at 256x256 resolution show consistent improvements in fidelity and coverage. Starting from base samples with FID 3.95, latent space refinement achieves state-of-the-art FID of 1.46 on MNIST using only a single additional function evaluation (1-NFE) while maintaining sample diversity.

Conclusion: Effective bias correction can be achieved as a post-hoc procedure without noise injection or multi-step resampling. BFR provides a practical framework for improving generative model outputs while preserving original sampling dynamics.

Abstract: Generative models, including diffusion and flow-based models, often exhibit systematic biases that degrade sample quality, particularly in high-dimensional settings. We revisit refinement methods and show that effective bias correction can be achieved as a post-hoc procedure, without noise injection or multi-step resampling of the sampling process. We propose a flow-matching-based \textbf{Bi-stage Flow Refinement (BFR)} framework with two refinement strategies operating at different stages: latent space alignment for approximately invertible generators and data space refinement trained with lightweight augmentations. Unlike previous refiners that perturb sampling dynamics, BFR preserves the original ODE trajectory and applies deterministic corrections to generated samples. Experiments on MNIST, CIFAR-10, and FFHQ at 256x256 resolution demonstrate consistent improvements in fidelity and coverage; notably, starting from base samples with FID 3.95, latent space refinement achieves a \textbf{state-of-the-art} FID of \textbf{1.46} on MNIST using only a single additional function evaluation (1-NFE), while maintaining sample diversity.

Method: Proposes a cross-subject self-training framework with Filter-Bank Euclidean Alignment (FBEA), Pre-Training with Adversarial Learning (PTAL) for distribution alignment, Dual-Ensemble Self-Training (DEST) for pseudo-label refinement, and Time-Frequency Augmented Contrastive Learning (TFA-CL) for enhanced feature discriminability.

Result: Achieves state-of-the-art performance on Benchmark and BETA datasets across varying signal lengths, demonstrating superiority over existing methods.

Conclusion: The proposed cross-subject domain adaptation framework effectively addresses signal variability and annotation limitations in SSVEP-based BCIs, enabling improved performance without extensive user-specific training.

Abstract: Steady-state visually evoked potentials (SSVEP)-based brain-computer interfaces (BCIs) are widely used due to their high signal-to-noise ratio and user-friendliness. Accurate decoding of SSVEP signals is crucial for interpreting user intentions in BCI applications. However, signal variability across subjects and the costly user-specific annotation limit recognition performance. Therefore, we propose a novel cross-subject domain adaptation method built upon the self-training paradigm. Specifically, a Filter-Bank Euclidean Alignment (FBEA) strategy is designed to exploit frequency information from SSVEP filter banks. Then, we propose a Cross-Subject Self-Training (CSST) framework consisting of two stages: Pre-Training with Adversarial Learning (PTAL), which aligns the source and target distributions, and Dual-Ensemble Self-Training (DEST), which refines pseudo-label quality. Moreover, we introduce a Time-Frequency Augmented Contrastive Learning (TFA-CL) module to enhance feature discriminability across multiple augmented views. Extensive experiments on the Benchmark and BETA datasets demonstrate that our approach achieves state-of-the-art performance across varying signal lengths, highlighting its superiority.

Method: Introduces cellular sheaf theory to model local feature alignments and agreements in graph-based architectures, with multiscale extensions inspired by topological data analysis (TDA) to capture hierarchical feature interactions.

Result: Provides a topological perspective on feature diffusion and aggregation, enabling joint characterization of GDL/TDL architectures based on their geometric/topological structures and learned signals.

Conclusion: The framework offers insights for future studies on conventional tasks like node classification, substructure detection, and community detection through topological analysis of feature behavior.

Abstract: Combinatorial and topological structures, such as graphs, simplicial complexes, and cell complexes, form the foundation of geometric and topological deep learning (GDL and TDL) architectures. These models aggregate signals over such domains, integrate local features, and generate representations for diverse real-world applications. However, the distribution and diffusion behavior of GDL and TDL features during training remains an open and underexplored problem. Motivated by this gap, we introduce a cellular sheaf theoretic framework for modeling and analyzing the local consistency and harmonicity of node features and edge weights in graph-based architectures. By tracking local feature alignments and agreements through sheaf structures, the framework offers a topological perspective on feature diffusion and aggregation. Furthermore, a multiscale extension inspired by topological data analysis (TDA) is proposed to capture hierarchical feature interactions in graph models. This approach enables a joint characterization of GDL and TDL architectures based on their underlying geometric and topological structures and the learned signals defined on them, providing insights for future studies on conventional tasks such as node classification, substructure detection, and community detection.

Method: Used HBN movie-watching dataset to benchmark five architectures (CNN, LSTM, EEGXF, S4, S5) on a 4-class task across segment lengths from 8s to 128s. Evaluated zero-shot cross-frequency shifts, cross-task OOD inputs, and leave-one-subject-out generalization.

Result: Accuracy improves with longer context: at 64s, S5 reaches 98.7%±0.6 and CNN 98.3%±0.3, with S5 using ~20x fewer parameters. S5 achieves stronger cross-subject accuracy but makes over-confident errors on OOD tasks; EEGXF is more conservative and stable under frequency shifts.

Conclusion: Reveals a practical efficiency-robustness trade-off: S5 for parameter-efficient peak accuracy; EEGXF when robustness and conservative uncertainty are critical in EEG decoding applications.

Abstract: We study how model architecture and temporal context interact in naturalistic EEG decoding. Using the HBN movie-watching dataset, we benchmark five architectures, CNN, LSTM, a stabilized Transformer (EEGXF), S4, and S5, on a 4-class task across segment lengths from 8s to 128s. Accuracy improves with longer context: at 64s, S5 reaches 98.7%+/-0.6 and CNN 98.3%+/-0.3, while S5 uses ~20x fewer parameters than CNN. To probe real-world robustness, we evaluate zero-shot cross-frequency shifts, cross-task OOD inputs, and leave-one-subject-out generalization. S5 achieves stronger cross-subject accuracy but makes over-confident errors on OOD tasks; EEGXF is more conservative and stable under frequency shifts, though less calibrated in-distribution. These results reveal a practical efficiency-robustness trade-off: S5 for parameter-efficient peak accuracy; EEGXF when robustness and conservative uncertainty are critical.

Method: Introduces short-range attractive couplings between neural network weights during training that rapidly induce discretization of weight distribution in a mixed-precision manner using only two additional hyperparameters

Result: Outperforms histogram-equalized post-training quantization on ResNet-20/CIFAR-10 within appropriate hyperparameter ranges

Conclusion: Soft quantization provides a new pipeline for flexible model compression and a tool for investigating compression-generalization trade-offs in high-dimensional loss landscapes

Abstract: We show that introducing short-range attractive couplings between the weights of a neural network during training provides a novel avenue for model quantization. These couplings rapidly induce the discretization of a model’s weight distribution, and they do so in a mixed-precision manner despite only relying on two additional hyperparameters. We demonstrate that, within an appropriate range of hyperparameters, our “soft quantization’’ scheme outperforms histogram-equalized post-training quantization on ResNet-20/CIFAR-10. Soft quantization provides both a new pipeline for the flexible compression of machine learning models and a new tool for investigating the trade-off between compression and generalization in high-dimensional loss landscapes.

Method: Train a Gaussian process distributed Port-Hamiltonian system (GP-dPHS) on limited observations to capture energy-based dynamics representation. Use GP-dPHS to generate physically consistent artificial dataset for diffusion training, and inform diffusion model with structured physics residual loss. Apply split conformal calibration for uncertainty quantification.

Result: Experiments on PDE benchmarks and real-world spring system show improved accuracy and physical consistency under data scarcity compared to baseline methods.

Conclusion: PHDME provides an effective framework for forecasting dynamical systems with sparse observations and incomplete physics knowledge by combining port-Hamiltonian structural priors, diffusion models, and physics-informed learning with uncertainty quantification.

Abstract: Diffusion models provide expressive priors for forecasting trajectories of dynamical systems, but are typically unreliable in the sparse data regime. Physics-informed machine learning (PIML) improves reliability in such settings; however, most methods require \emph{explicit governing equations} during training, which are often only partially known due to complex and nonlinear dynamics. We introduce \textbf{PHDME}, a port-Hamiltonian diffusion framework designed for \emph{sparse observations} and \emph{incomplete physics}. PHDME leverages port-Hamiltonian structural prior but does not require full knowledge of the closed-form governing equations. Our approach first trains a Gaussian process distributed Port-Hamiltonian system (GP-dPHS) on limited observations to capture an energy-based representation of the dynamics. The GP-dPHS is then used to generate a physically consistent artificial dataset for diffusion training, and to inform the diffusion model with a structured physics residual loss. After training, the diffusion model acts as an amortized sampler and forecaster for fast trajectory generation. Finally, we apply split conformal calibration to provide uncertainty statements for the generated predictions. Experiments on PDE benchmarks and a real-world spring system show improved accuracy and physical consistency under data scarcity.

Method: Develop theoretical framework using deep neural networks with SignReLU activation function to approximate ratio-type functionals. Provide L^p approximation bounds and convergence rates under regularity assumptions. Construct SignReLU-based neural estimator for DDPM reverse process.

Result: Establish approximation bounds for ratio-type functionals, derive bounds on excess KL risk between generated and true data distributions, decompose risk into approximation and estimation errors, providing generalization guarantees for diffusion models.

Conclusion: Theoretical framework provides generalization guarantees for finite-sample training of diffusion-based generative models through neural approximation of ratio-type functionals.

Abstract: Motivated by challenges in conditional generative modeling, where the target conditional density takes the form of a ratio f1 over f2, this paper develops a theoretical framework for approximating such ratio-type functionals. Here, f1 and f2 are kernel-based marginal densities that capture structured interactions, a setting central to diffusion-based generative models. We provide a concise proof for approximating these ratio-type functionals using deep neural networks with the SignReLU activation function, leveraging the activation’s piecewise structure. Under standard regularity assumptions, we establish L^p(Omega) approximation bounds and convergence rates. Specializing to Denoising Diffusion Probabilistic Models (DDPMs), we construct a SignReLU-based neural estimator for the reverse process and derive bounds on the excess Kullback-Leibler (KL) risk between the generated and true data distributions. Our analysis decomposes this excess risk into approximation and estimation error components. These results provide generalization guarantees for finite-sample training of diffusion-based generative models.

Method: Proposes Less Noise Sampling Framework (LENS): 1) Identifies and removes interference tokens from prompts, 2) Uses purified prompts for exploration, 3) Transfers successful rollouts to supervise policy optimization on original noisy prompts, enabling models to learn to ignore interference.

Result: LENS significantly outperforms GRPO with 3.88% average performance gain and over 1.6× speedup in convergence, demonstrating improved rollout efficiency and training stability.

Conclusion: Pruning interference tokens is critical for improving rollout efficiency in RLVR, offering a new perspective for RLVR research by addressing prompt noise rather than just task difficulty.

Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has advanced LLM reasoning, but remains constrained by inefficient exploration under limited rollout budgets, leading to low sampling success and unstable training in complex tasks. We find that many exploration failures arise not from problem difficulty, but from a small number of prompt tokens that introduce interference. Building on this insight, we propose the Less Noise Sampling Framework (LENS), which first prompts by identifying and removing interference tokens. then transfers successful rollouts from the purification process to supervise policy optimization on the original noisy prompts, enabling the model to learn to ignore interference in the real-world, noisy prompting settings. Experimental results show that LENS significantly outperforms GRPO, delivering higher performance and faster convergence, with a 3.88% average gain and over 1.6$\times$ speedup. Our work highlights the critical role of pruning interference tokens in improving rollout efficiency, offering a new perspective for RLVR research.

Method: Proposes peak-aware conditional generative adversarial network (CGAN) framework with novel peak-aware mechanism highlighting characteristic GC-MS peaks. Encodes chemical and solvent information in latent vectors to generate synthetic GC-MS signals consistent with experimental conditions.

Result: Achieves cosine similarity and Pearson correlation coefficient values above 0.9, preserves peak number diversity, and reduces false alarms in discrimination models. Generated synthetic data validated through quantitative and qualitative evaluations.

Conclusion: The peak-aware conditional generative model effectively generates realistic GC-MS data under interference conditions, improving AI-based discrimination model performance without requiring extensive real experimental data collection.

Abstract: Gas chromatography-mass spectrometry (GC-MS) is a widely used analytical method for chemical substance detection, but measurement reliability tends to deteriorate in the presence of interfering substances. In particular, interfering substances cause nonspecific peaks, residence time shifts, and increased background noise, resulting in reduced sensitivity and false alarms. To overcome these challenges, in this paper, we propose an artificial intelligence discrimination framework based on a peak-aware conditional generative model to improve the reliability of GC-MS measurements under interference conditions. The framework is learned with a novel peak-aware mechanism that highlights the characteristic peaks of GC-MS data, allowing it to generate important spectral features more faithfully. In addition, chemical and solvent information is encoded in a latent vector embedded with it, allowing a conditional generative adversarial neural network (CGAN) to generate a synthetic GC-MS signal consistent with the experimental conditions. This generates an experimental dataset that assumes indirect substance situations in chemical substance data, where acquisition is limited without conducting real experiments. These data are used for the learning of AI-based GC-MS discrimination models to help in accurate chemical substance discrimination. We conduct various quantitative and qualitative evaluations of the generated simulated data to verify the validity of the proposed framework. We also verify how the generative model improves the performance of the AI discrimination framework. Representatively, the proposed method is shown to consistently achieve cosine similarity and Pearson correlation coefficient values above 0.9 while preserving peak number diversity and reducing false alarms in the discrimination model.

Method: Systematic empirical comparison between model-free neural network models (Transformer-based models, state-space neural networks, recurrent architectures) and classical filtering methods (particle filters, nonlinear Kalman filters) across multiple nonlinear scenarios.

Result: Neural models, particularly state-space models (SSMs), achieve state estimation performance approaching strong nonlinear Kalman filters in nonlinear scenarios and outperform weaker classical baselines despite lacking access to system models, while also attaining substantially higher inference throughput.

Conclusion: Neural network models can serve as effective state estimators without requiring explicit system dynamics knowledge, with state-space neural networks showing competitive performance to classical filtering methods while offering computational advantages.

Abstract: Neural network models are increasingly used for state estimation in control and decision-making problems, yet it remains unclear to what extent they behave as principled filters in nonlinear dynamical systems. Unlike classical filters, which rely on explicit knowledge of system dynamics and noise models, neural estimators can be trained purely from data without access to the underlying system equations. In this work, we present a systematic empirical comparison between such model-free neural network models and classical filtering methods across multiple nonlinear scenarios. Our study evaluates Transformer-based models, state-space neural networks, and recurrent architectures alongside particle filters and nonlinear Kalman filters. The results show that neural models (in particular, state-space models (SSMs)) achieve state estimation performance that approaches strong nonlinear Kalman filters in nonlinear scenarios and outperform weaker classical baselines despite lacking access to system models, while also attaining substantially higher inference throughput.

Method: Proposes Extended Graph Attention Model (EGAM) with multi-head dot-product attention that updates both node and edge embeddings. Uses autoregressive encoder-decoder architecture trained with policy gradient algorithms incorporating a specially designed baseline.

Result: EGAM matches or outperforms existing methods across various routing problems, demonstrating exceptional performance on highly constrained problems and showing efficiency in handling complex graph structures.

Conclusion: Extending graph attention to include edge embeddings significantly improves neural combinatorial optimization solvers, particularly for constrained routing problems, offering a more comprehensive approach to graph-based optimization.

Abstract: Neural combinatorial optimization (NCO) solvers, implemented with graph neural networks (GNNs), have introduced new approaches for solving routing problems. Trained with reinforcement learning (RL), the state-of-the-art graph attention model (GAM) achieves near-optimal solutions without requiring expert knowledge or labeled data. In this work, we generalize the existing graph attention mechanism and propose the extended graph attention model (EGAM). Our model utilizes multi-head dot-product attention to update both node and edge embeddings, addressing the limitations of the conventional GAM, which considers only node features. We employ an autoregressive encoder-decoder architecture and train it with policy gradient algorithms that incorporate a specially designed baseline. Experiments show that EGAM matches or outperforms existing methods across various routing problems. Notably, the proposed model demonstrates exceptional performance on highly constrained problems, highlighting its efficiency in handling complex graph structures.

Method: DUET uses a distillation-based approach where a student model learns to imitate a prompt-steered teacher model. The teacher effectively refuses undesirable knowledge generation while preserving general domain knowledge, combining the strengths of both tuning-based and in-contextualized unlearning.

Result: Extensive evaluations show DUET achieves higher performance in both forgetting undesirable knowledge and preserving utility, while being orders of magnitude more data-efficient than state-of-the-art unlearning methods.

Conclusion: DUET provides an effective and efficient solution for LLM unlearning that addresses the limitations of existing methods, making it a promising approach for trustworthy AI development.

Abstract: LLM unlearning is a technique to remove the impacts of undesirable knowledge from the model without retraining from scratch, which is indispensable towards trustworthy AI. Existing unlearning methods face significant limitations: conventional tuning-based unlearning is computationally heavy and prone to catastrophic forgetting. In contrast, in-contextualized unlearning is lightweight for precise unlearning but vulnerable to prompt removal or reverse engineering attacks. In response, we propose Distilled Unlearning from an Efficient Teacher (DUET), a novel distillation-based unlearning method that combines the merits of these two lines of work. It learns a student model to imitate the behavior of a prompt-steered teacher that effectively refuses undesirable knowledge generation while preserving general domain knowledge. Extensive evaluations on existing benchmarks with our enriched evaluation protocols demonstrate that DUET achieves higher performance in both forgetting and utility preservation, while being orders of magnitude more data-efficient than state-of-the-art unlearning methods.

Method: Introduces Physics-Informed Learning via Diffusion (PILD) with: 1) virtual residual observations from Laplace distribution to supervise generation, and 2) conditional embedding module to inject physical information into denoising network at multiple layers.

Result: PILD substantially improves accuracy, stability, and generalization over existing physics-informed and diffusion-based baselines across engineering/scientific tasks including vehicle trajectories, tire forces, Darcy flow, and plasma dynamics.

Conclusion: PILD provides a concise, modular framework that unifies diffusion modeling with physical constraints, making diffusion models more applicable to practical scientific and engineering problems.

Abstract: Diffusion models have emerged as powerful generative tools for modeling complex data distributions, yet their purely data-driven nature limits applicability in practical engineering and scientific problems where physical laws need to be followed. This paper proposes Physics-Informed Learning via Diffusion (PILD), a framework that unifies diffusion modeling and first-principles physical constraints by introducing a virtual residual observation sampled from a Laplace distribution to supervise generation during training. To further integrate physical laws, a conditional embedding module is incorporated to inject physical information into the denoising network at multiple layers, ensuring consistent guidance throughout the diffusion process. The proposed PILD framework is concise, modular, and broadly applicable to problems governed by ordinary differential equations, partial differential equations, as well as algebraic equations or inequality constraints. Extensive experiments across engineering and scientific tasks including estimating vehicle trajectories, tire forces, Darcy flow and plasma dynamics, demonstrate that our PILD substantially improves accuracy, stability, and generalization over existing physics-informed and diffusion-based baselines.

Method: Zenith uses a novel architecture with Prime Tokens, Token Fusion, and Token Boost modules to handle high-dimensional features efficiently. It focuses on improved token heterogeneity and exhibits superior scaling laws compared to other ranking methods.

Result: Deployed on TikTok Live, Zenith achieved +1.05%/-1.10% improvements in online CTR AUC and Logloss, with +9.93% gains in Quality Watch Session/User and +8.11% in Quality Watch Duration/User.

Conclusion: Zenith demonstrates that scalable and efficient ranking architectures can significantly improve recommendation performance in real-world applications while maintaining practical inference latency.

Abstract: Accurately capturing feature interactions is essential in recommender systems, and recent trends show that scaling up model capacity could be a key driver for next-level predictive performance. While prior work has explored various model architectures to capture multi-granularity feature interactions, relatively little attention has been paid to efficient feature handling and scaling model capacity without incurring excessive inference latency. In this paper, we address this by presenting Zenith, a scalable and efficient ranking architecture that learns complex feature interactions with minimal runtime overhead. Zenith is designed to handle a few high-dimensional Prime Tokens with Token Fusion and Token Boost modules, which exhibits superior scaling laws compared to other state-of-the-art ranking methods, thanks to its improved token heterogeneity. Its real-world effectiveness is demonstrated by deploying the architecture to TikTok Live, a leading online livestreaming platform that attracts billions of users globally. Our A/B test shows that Zenith achieves +1.05%/-1.10% in online CTR AUC and Logloss, and realizes +9.93% gains in Quality Watch Session / User and +8.11% in Quality Watch Duration / User.

Method: TimeSliver jointly utilizes raw time-series data and its symbolic abstraction to construct a representation that maintains original temporal structure. Each element in this representation linearly encodes the contribution of each temporal segment to the final prediction, allowing assignment of meaningful importance scores to every time point.

Result: Outperforms other temporal attribution methods by 11% on 7 distinct synthetic and real-world multivariate time-series datasets. Achieves predictive performance within 2% of state-of-the-art baselines across 26 UEA benchmark datasets.

Conclusion: TimeSliver provides a strong and explainable framework for general time-series classification that offers faithful temporal importance attribution while maintaining competitive predictive performance.

Abstract: Identifying the extent to which every temporal segment influences a model’s predictions is essential for explaining model decisions and increasing transparency. While post-hoc explainable methods based on gradients and feature-based attributions have been popular, they suffer from reference state sensitivity and struggle to generalize across time-series datasets, as they treat time points independently and ignore sequential dependencies. Another perspective on explainable time-series classification is through interpretable components of the model, for instance, leveraging self-attention mechanisms to estimate temporal attribution; however, recent findings indicate that these attention weights often fail to provide faithful measures of temporal importance. In this work, we advance this perspective and present a novel explainability-driven deep learning framework, TimeSliver, which jointly utilizes raw time-series data and its symbolic abstraction to construct a representation that maintains the original temporal structure. Each element in this representation linearly encodes the contribution of each temporal segment to the final prediction, allowing us to assign a meaningful importance score to every time point. For time-series classification, TimeSliver outperforms other temporal attribution methods by 11% on 7 distinct synthetic and real-world multivariate time-series datasets. TimeSliver also achieves predictive performance within 2% of state-of-the-art baselines across 26 UEA benchmark datasets, positioning it as a strong and explainable framework for general time-series classification.

Method: Physics-Guided Tiny-Mamba Transformer (PG-TMT) with three branches: 1) depthwise-separable convolutional stem for micro-transients, 2) Tiny-Mamba state-space branch for long-range dynamics, 3) lightweight local Transformer for cross-channel resonances. Uses analytic temporal-to-spectral mapping to align attention with bearing fault-order bands, and extreme-value theory (EVT) with dual-threshold hysteresis for reliable decision-making.

Result: Achieves higher precision-recall AUC (primary under imbalance), competitive or better ROC AUC, shorter mean time-to-detect at matched false-alarm intensity, and strong cross-domain transfer on CWRU, Paderborn, XJTU-SY, and industrial datasets.

Conclusion: PG-TMT provides calibrated, interpretable, and deployment-ready early warnings by coupling physics-aligned representations with EVT-calibrated decision rules for reliability-centric prognostics and health management.

Abstract: Reliability-centered prognostics for rotating machinery requires early warning signals that remain accurate under nonstationary operating conditions, domain shifts across speed/load/sensors, and severe class imbalance, while keeping the false-alarm rate small and predictable. We propose the Physics-Guided Tiny-Mamba Transformer (PG-TMT), a compact tri-branch encoder tailored for online condition monitoring. A depthwise-separable convolutional stem captures micro-transients, a Tiny-Mamba state-space branch models near-linear long-range dynamics, and a lightweight local Transformer encodes cross-channel resonances. We derive an analytic temporal-to-spectral mapping that ties the model’s attention spectrum to classical bearing fault-order bands, yielding a band-alignment score that quantifies physical plausibility and provides physics-grounded explanations. To ensure decision reliability, healthy-score exceedances are modeled with extreme-value theory (EVT), which yields an on-threshold achieving a target false-alarm intensity (events/hour); a dual-threshold hysteresis with a minimum hold time further suppresses chatter. Under a leakage-free streaming protocol with right-censoring of missed detections on CWRU, Paderborn, XJTU-SY, and an industrial pilot, PG-TMT attains higher precision-recall AUC (primary under imbalance), competitive or better ROC AUC, and shorter mean time-to-detect at matched false-alarm intensity, together with strong cross-domain transfer. By coupling physics-aligned representations with EVT-calibrated decision rules, PG-TMT delivers calibrated, interpretable, and deployment-ready early warnings for reliability-centric prognostics and health management.

Method: Theoretical analysis of PLS-SVD under independent entry-wise missing-completely-at-random masking in proportional high-dimensional spiked model. Study masked cross-covariance as spiked rectangular random matrix with effective signal strength attenuated by √ρ (joint entry retention probability).

Result: PLS-SVD exhibits sharp BBP-type phase transition: below critical signal-to-noise threshold, leading singular vectors are asymptotically uninformative; above threshold, they achieve nontrivial alignment with latent shared directions with closed-form asymptotic overlap formulas.

Conclusion: Missing data significantly impacts PLS-SVD performance, with phase transition behavior determining when meaningful shared structure can be recovered. Results validated through simulations and semi-synthetic multimodal experiments.

Abstract: Partial Least Squares (PLS) learns shared structure from paired data via the top singular vectors of the empirical cross-covariance (PLS-SVD), but multimodal datasets often have missing entries in both views. We study PLS-SVD under independent entry-wise missing-completely-at-random masking in a proportional high-dimensional spiked model. After appropriate normalization, the masked cross-covariance behaves like a spiked rectangular random matrix whose effective signal strength is attenuated by $\sqrtρ$, where $ρ$ is the joint entry retention probability. As a result, PLS-SVD exhibits a sharp BBP-type phase transition: below a critical signal-to-noise threshold the leading singular vectors are asymptotically uninformative, while above it they achieve nontrivial alignment with the latent shared directions, with closed-form asymptotic overlap formulas. Simulations and semi-synthetic multimodal experiments corroborate the predicted phase diagram and recovery curves across aspect ratios, signal strengths, and missingness levels.

Method: Introduces InfoUtil framework with two components: 1) game-theoretic informativeness maximization using Shapley Value to extract key information from samples, and 2) principled utility maximization by selecting globally influential samples based on Gradient Norm

Result: Achieves 6.1% performance improvement over previous state-of-the-art on ImageNet-1K dataset using ResNet-18

Conclusion: Provides a solid theoretical foundation for dataset distillation that balances informativeness and utility, leading to improved performance

Abstract: Dataset Distillation (DD) seeks to create a compact dataset from a large, real-world dataset. While recent methods often rely on heuristic approaches to balance efficiency and quality, the fundamental relationship between original and synthetic data remains underexplored. This paper revisits knowledge distillation-based dataset distillation within a solid theoretical framework. We introduce the concepts of Informativeness and Utility, capturing crucial information within a sample and essential samples in the training set, respectively. Building on these principles, we define optimal dataset distillation mathematically. We then present InfoUtil, a framework that balances informativeness and utility in synthesizing the distilled dataset. InfoUtil incorporates two key components: (1) game-theoretic informativeness maximization using Shapley Value attribution to extract key information from samples, and (2) principled utility maximization by selecting globally influential samples based on Gradient Norm. These components ensure that the distilled dataset is both informative and utility-optimized. Experiments demonstrate that our method achieves a 6.1% performance improvement over the previous state-of-the-art approach on ImageNet-1K dataset using ResNet-18.

Method: Proposes a simple variant of Q-learning that samples from lazified dynamics (system remains in current state with fixed probability). Constructs an instance-dependent seminorm and shows that after lazy transformation, the Bellman operator becomes one-step contractive under this seminorm.

Result: Establishes optimal Õ(ε⁻²) sample complexity guarantees (up to logarithmic factors) for both synchronous and asynchronous Q-learning under a reachability assumption.

Conclusion: The lazy dynamics transformation enables achieving optimal sample complexity for average-reward MDPs by creating a contractive structure that overcomes the fundamental challenge of contraction absence in these settings.

Abstract: We present the convergence rates of synchronous and asynchronous Q-learning for average-reward Markov decision processes, where the absence of contraction poses a fundamental challenge. Existing non-asymptotic results overcome this challenge by either imposing strong assumptions to enforce seminorm contraction or relying on discounted or episodic Markov decision processes as successive approximations, which either require unknown parameters or result in suboptimal sample complexity. In this work, under a reachability assumption, we establish optimal $\widetilde{O}(\varepsilon^{-2})$ sample complexity guarantees (up to logarithmic factors) for a simple variant of synchronous and asynchronous Q-learning that samples from the lazified dynamics, where the system remains in the current state with some fixed probability. At the core of our analysis is the construction of an instance-dependent seminorm and showing that, after a lazy transformation of the Markov decision process, the Bellman operator becomes one-step contractive under this seminorm.

Method: The authors systematically analyze search in model-based RL, identifying that distribution shift is the key issue rather than model accuracy. They develop techniques to mitigate distribution shift and enable effective search.

Result: The paper shows that search can harm performance even with highly accurate models, and that their distribution shift mitigation techniques enable effective search, achieving state-of-the-art performance across multiple benchmark domains.

Conclusion: Distribution shift is a more critical challenge than model accuracy for enabling effective search in model-based RL, and addressing it leads to significant performance improvements.

Abstract: This paper investigates search in model-based reinforcement learning (RL). Conventional wisdom holds that long-term predictions and compounding errors are the primary obstacles for model-based RL. We challenge this view, showing that search is not a plug-and-play replacement for a learned policy. Surprisingly, we find that search can harm performance even when the model is highly accurate. Instead, we show that mitigating distribution shift matters more than improving model or value function accuracy. Building on this insight, we identify key techniques for enabling effective search, achieving state-of-the-art performance across multiple popular benchmark domains.

Method: TGCC extracts domain causal-invariant features from spatial domain using causal interventions, performs enhanced condensation operations to capture structural and feature information, and injects causal-invariant features into condensed graphs through spectral-domain enhanced contrastive learning.

Result: TGCC achieves up to 13.41% improvement in cross-task and cross-domain scenarios compared to existing methods, and achieves state-of-the-art performance on 5 out of 6 datasets in single dataset/task scenarios.

Conclusion: TGCC provides an effective and transferable graph dataset condensation method that preserves causal information and works well in complex cross-task and cross-domain scenarios.

Abstract: The increasing scale of graph datasets has significantly improved the performance of graph representation learning methods, but it has also introduced substantial training challenges. Graph dataset condensation techniques have emerged to compress large datasets into smaller yet information-rich datasets, while maintaining similar test performance. However, these methods strictly require downstream applications to match the original dataset and task, which often fails in cross-task and cross-domain scenarios. To address these challenges, we propose a novel causal-invariance-based and transferable graph dataset condensation method, named \textbf{TGCC}, providing effective and transferable condensed datasets. Specifically, to preserve domain-invariant knowledge, we first extract domain causal-invariant features from the spatial domain of the graph using causal interventions. Then, to fully capture the structural and feature information of the original graph, we perform enhanced condensation operations. Finally, through spectral-domain enhanced contrastive learning, we inject the causal-invariant features into the condensed graph, ensuring that the compressed graph retains the causal information of the original graph. Experimental results on five public datasets and our novel \textbf{FinReport} dataset demonstrate that TGCC achieves up to a 13.41% improvement in cross-task and cross-domain complex scenarios compared to existing methods, and achieves state-of-the-art performance on 5 out of 6 datasets in the single dataset and task scenario.

Method: Proposes GAT-PEARL framework integrating graph attention networks (GAT) to extract spatial representations and model spatiotemporal relationships, combined with probabilistic embeddings for actor-critic reinforcement learning (PEARL) for rapid adaptation to charging network changes.

Result: Outperforms conventional reinforcement learning baselines in simulations on real-world Chengdu data, showing superior generalization to unseen infrastructure layouts and higher operational efficiency in dynamic settings.

Conclusion: GAT-PEARL effectively bridges the gap between theoretical models and real-world operations by enabling adaptive fleet management in dynamic charging environments without retraining.

Abstract: With the rapid expansion of electric vehicles (EVs) and charging infrastructure, the effective management of Autonomous Electric Taxi (AET) fleets faces a critical challenge in environments with dynamic and uncertain charging availability. While most existing research assumes a static charging network, this simplification creates a significant gap between theoretical models and real-world operations. To bridge this gap, we propose GAT-PEARL, a novel meta-reinforcement learning framework that learns an adaptive operational policy. Our approach integrates a graph attention network (GAT) to effectively extract robust spatial representations under infrastructure layouts and model the complex spatiotemporal relationships of the urban environment, and employs probabilistic embeddings for actor-critic reinforcement learning (PEARL) to enable rapid, inference-based adaptation to changes in charging network layouts without retraining. Through extensive simulations on real-world data in Chengdu, China, we demonstrate that GAT-PEARL significantly outperforms conventional reinforcement learning baselines, showing superior generalization to unseen infrastructure layouts and achieving higher overall operational efficiency in dynamic settings.

Method: Proposes a distributionally robust learning framework modeling uncertainty in both covariate and conditional label distributions, with an efficient algorithm compatible with existing UDA methods.

Result: Extensive experiments show consistent outperformance of strong baselines, especially when target data is extremely scarce across various distribution shift scenarios.

Conclusion: The proposed framework effectively addresses UDA challenges with limited target data and spurious correlations, offering a versatile solution applicable to both single-source and multi-source scenarios.

Abstract: Unsupervised domain adaptation (UDA) is a statistical learning problem when the distribution of training (source) data is different from that of test (target) data. In this setting, one has access to labeled data only from the source domain and unlabeled data from the target domain. The central objective is to leverage the source data and the unlabeled target data to build models that generalize to the target domain. Despite its potential, existing UDA approaches often struggle in practice, particularly in scenarios where the target domain offers only limited unlabeled data or spurious correlations dominate the source domain. To address these challenges, we propose a novel distributionally robust learning framework that models uncertainty in both the covariate distribution and the conditional label distribution. Our approach is motivated by the multi-source domain adaptation setting but is also directly applicable to the single-source scenario, making it versatile in practice. We develop an efficient learning algorithm that can be seamlessly integrated with existing UDA methods. Extensive experiments under various distribution shift scenarios show that our method consistently outperforms strong baselines, especially when target data are extremely scarce.

Method: Proposes a unified optimization model integrating air and ground transportation strategy selection, capturing dynamic multimodal transport networks with real-time traffic conditions and passenger decision-making. Develops Unified Air-Ground Mobility Coordination (UAGMC) framework using deep reinforcement learning and V2X communication to optimize vertiport selection and dynamically plan air taxi routes.

Result: UAGMC achieves 34% reduction in average travel time compared to conventional proportional allocation methods, enhancing overall travel efficiency in multimodal transportation systems.

Conclusion: The work lays a foundation for advancing intelligent urban mobility solutions through air-ground transportation coordination, demonstrating significant efficiency improvements through integrated optimization.

Abstract: Urban Air Mobility (UAM) has emerged as a transformative solution to alleviate urban congestion by utilizing low-altitude airspace, thereby reducing pressure on ground transportation networks. To enable truly efficient and seamless door-to-door travel experiences, UAM requires close integration with existing ground transportation infrastructure. However, current research on optimal integrated routing strategies for passengers in air-ground mobility systems remains limited, with a lack of systematic exploration.To address this gap, we first propose a unified optimization model that integrates strategy selection for both air and ground transportation. This model captures the dynamic characteristics of multimodal transport networks and incorporates real-time traffic conditions alongside passenger decision-making behavior. Building on this model, we propose a Unified Air-Ground Mobility Coordination (UAGMC) framework, which leverages deep reinforcement learning (RL) and Vehicle-to-Everything (V2X) communication to optimize vertiport selection and dynamically plan air taxi routes. Experimental results demonstrate that UAGMC achieves a 34% reduction in average travel time compared to conventional proportional allocation methods, enhancing overall travel efficiency and providing novel insights into the integration and optimization of multimodal transportation systems. This work lays a solid foundation for advancing intelligent urban mobility solutions through the coordination of air and ground transportation modes. The related code can be found at https://github.com/Traffic-Alpha/UAGMC.

Method: Used VGG16 as surrogate to generate FGSM and PGD adversarial examples on CIFAR-10, then tested transfer to four classical classifiers (KNN, Decision Tree, Linear SVM, Kernel SVM) and a shallow neural network across eight HOG configurations.

Result: All classifiers suffered 16.6%-59.1% relative accuracy drops, comparable to neural-to-neural transfer. Surprisingly, FGSM caused greater degradation than PGD in 100% of classical ML cases (attack hierarchy reversal), and block normalization provided only partial mitigation.

Conclusion: Adversarial vulnerability is not an artifact of end-to-end differentiability but a fundamental property of image classification systems, with implications for security-critical deployments across computational paradigms.

Abstract: Deep neural networks are vulnerable to adversarial examples–inputs with imperceptible perturbations causing misclassification. While adversarial transfer within neural networks is well-documented, whether classical ML pipelines using handcrafted features inherit this vulnerability when attacked via neural surrogates remains unexplored. Feature engineering creates information bottlenecks through gradient quantization and spatial binning, potentially filtering high-frequency adversarial signals. We evaluate this hypothesis through the first comprehensive study of adversarial transfer from DNNs to HOG-based classifiers. Using VGG16 as a surrogate, we generate FGSM and PGD adversarial examples and test transfer to four classical classifiers (KNN, Decision Tree, Linear SVM, Kernel SVM) and a shallow neural network across eight HOG configurations on CIFAR-10. Our results strongly refute the protective hypothesis: all classifiers suffer 16.6%-59.1% relative accuracy drops, comparable to neural-to-neural transfer. More surprisingly, we discover attack hierarchy reversal–contrary to patterns where iterative PGD dominates FGSM within neural networks, FGSM causes greater degradation than PGD in 100% of classical ML cases, suggesting iterative attacks overfit to surrogate-specific features that don’t survive feature extraction. Block normalization provides partial but insufficient mitigation. These findings demonstrate that adversarial vulnerability is not an artifact of end-to-end differentiability but a fundamental property of image classification systems, with implications for security-critical deployments across computational paradigms.

Method: Mathematical derivation of dual problems for new convex loss functions for both binary classification and regression SVMs, followed by experimental validation on small datasets due to SVM scalability limitations.

Result: Generalization measures are never worse than standard losses and often better, demonstrating the potential of incorporating pattern correlations in loss functions.

Conclusion: The proposed loss shows promise and warrants further study, particularly when coupled with shallow and deep neural networks, where some novel results have already been obtained.

Abstract: We propose a new convex loss for SVMs, both for the binary classification and for the regression models. Therefore, we show the mathematical derivation of the dual problems and we experiment them with several small data-sets. The minimal dimension of those data-sets is due to the difficult scalability of the SVM method to bigger instances. This preliminary study should prove that using pattern correlations inside the loss function could enhance the generalisation performances. Coherently, results show that generalisation measures are never worse than the standard losses and several times they are better. In our opinion, it should be considered a careful study of this loss, coupled with shallow and deep neural networks. In fact, we present some novel results obtained with those architectures.

Method: Proposes a timestep sampling strategy that explicitly controls where learning occurs along the denoising trajectory. By adjusting the width of the confidence interval, the method provides direct control over the memorization-generalization trade-off. The approach shifts learning emphasis toward later denoising steps to reduce memorization.

Result: Experiments on image and 1D signal generation tasks demonstrate that shifting learning emphasis toward later denoising steps consistently reduces memorization and improves distributional alignment with training data, validating the generality and effectiveness of the approach.

Conclusion: The denoising-centric perspective provides a principled way to control memorization in diffusion models through timestep sampling, offering direct control over the memorization-generalization trade-off without requiring data or model modifications.

Abstract: Controlling memorization in diffusion models is critical for applications that require generated data to closely match the training distribution. Existing approaches mainly focus on data centric or model centric modifications, treating the diffusion model as an isolated predictor. In this paper, we study memorization in diffusion models from a denoising centric perspective. We show that uniform timestep sampling leads to unequal learning contributions across denoising steps due to differences in signal to noise ratio, which biases training toward memorization. To address this, we propose a timestep sampling strategy that explicitly controls where learning occurs along the denoising trajectory. By adjusting the width of the confidence interval, our method provides direct control over the memorization generalization trade off. Experiments on image and 1D signal generation tasks demonstrate that shifting learning emphasis toward later denoising steps consistently reduces memorization and improves distributional alignment with training data, validating the generality and effectiveness of our approach.

Method: Proposes Low-rank & Lipschitz-controlled Routing (L2R) with: 1) Shared low-rank latent routing space for expert assignment, 2) Saturated Inner-Product Scoring (SIPS) to control Lipschitz behavior, 3) Parameter-efficient multi-anchor routing mechanism for enhanced expert expressiveness.

Result: Extensive experiments on large-scale language MoE models and vision MoE on ImageNet show consistent improvements in routing stability, expert specialization, and overall model performance.

Conclusion: L2R provides a unified routing framework that addresses fundamental limitations of current MoE routing systems, leading to more stable and effective expert specialization across both language and vision domains.

Abstract: Mixture-of-Experts (MoE) models scale neural networks by conditionally activating a small subset of experts, where the router plays a central role in determining expert specialization and overall model performance. However, many modern MoE systems still adopt linear routers in raw high-dimensional representation spaces, where representation mismatch, angular concentration, and scale-sensitive scoring can jointly undermine routing discriminability and stable expert specialization. In this work, we propose Low-rank & Lipschitz-controlled Routing (L2R), a unified routing framework that reshapes both the routing space and scoring geometry. L2R performs expert assignment in a shared low-rank latent routing space and introduces Saturated Inner-Product Scoring (SIPS) to explicitly control the Lipschitz behavior of routing functions, yielding smoother and more stable routing geometry. In addition, L2R incorporates a parameter-efficient multi-anchor routing mechanism to enhance expert expressiveness. Extensive experiments on a large-scale language MoE model and a vision MoE setting on ImageNet demonstrate that L2R consistently improves routing stability, expert specialization, and overall model performance.

Method: Proposes a factored representation learning framework that decomposes contextual embeddings into causal factors (sufficient for reward prediction) and non-causal factors (capturing reward-irrelevant attributes). The reward head is constrained to use only causal factors, while an adversarial head tries to predict reward from non-causal factors with gradient reversal to discourage encoding reward-relevant information.

Result: Experiments on mathematical and dialogue tasks show the method learns more robust reward models and consistently improves downstream RLHF performance over state-of-the-art baselines. Analyses confirm effectiveness in mitigating reward hacking behaviors related to length and sycophantic bias.

Conclusion: The causal perspective and factored representation approach effectively address reward hacking in RLHF by separating causal from non-causal factors, leading to more reliable reward models and better alignment with human preferences.

Abstract: A reliable reward model is essential for aligning large language models with human preferences through reinforcement learning from human feedback. However, standard reward models are susceptible to spurious features that are not causally related to human labels. This can lead to reward hacking, where high predicted reward does not translate into better behavior. In this work, we address this problem from a causal perspective by proposing a factored representation learning framework that decomposes the model’s contextual embedding into (1) causal factors that are sufficient for reward prediction and (2) non-causal factors that capture reward-irrelevant attributes such as length or sycophantic bias. The reward head is then constrained to depend only on the causal component. In addition, we introduce an adversarial head trained to predict reward from the non-causal factors, while applying gradient reversal to discourage them from encoding reward-relevant information. Experiments on both mathematical and dialogue tasks demonstrate that our method learns more robust reward models and consistently improves downstream RLHF performance over state-of-the-art baselines. Analyses on length and sycophantic bias further validate the effectiveness of our method in mitigating reward hacking behaviors.

Method: Developed a tractable analytical framework for sizing AFD bundles in rA-1F topology using a probabilistic workload model to derive closed-form rules for optimal A/F ratio that maximizes average throughput.

Result: The theoretical optimal A/F ratio matches simulation-optimal within 10% across workloads and consistently reduces idle time in trace-calibrated AFD simulations.

Conclusion: The analytical framework provides effective closed-form rules for optimal resource allocation in AFD architectures, enabling better throughput and reduced idle time in LLM decoding systems.

Abstract: Attention-FFN disaggregation (AFD) is an emerging architecture for LLM decoding that separates state-heavy, KV-cache-dominated Attention computation from stateless, compute-intensive FFN computation, connected by per-step communication. While AFD enables independent scaling of memory and compute resources, its performance is highly sensitive to the Attention/FFN provisioning ratio: mis-sizing induces step-level blocking and costly device idle time. We develop a tractable analytical framework for sizing AFD bundles in an $r$A-$1$F topology, where the key difficulty is that Attention-side work is nonstationary-token context grows and requests are continuously replenished with random lengths-while FFN work is stable given the aggregated batch. Using a probabilistic workload model, we derive closed-form rules for the optimal A/F ratio that maximize average throughput per instance across the system. A trace-calibrated AFD simulator validates the theory: across workloads, the theoretical optimal A/F ratio matches the simulation-optimal within 10%, and consistently reduces idle time.

Method: EI-GN applies the improvement principle to a gradient-aware auxiliary objective that considers both function values and gradient norms. It uses gradient-enhanced surrogate models to enable principled gradient inference from function evaluations, with a tractable closed-form expression for efficient optimization.

Result: Empirical evaluations on standard BO benchmarks show EI-GN yields consistent improvements against standard baselines. The method is also demonstrated to be applicable to control policy learning problems.

Conclusion: EI-GN provides a novel gradient-aware acquisition function that improves upon standard EI by better balancing exploration and exploitation through consideration of gradient information, with practical applications in optimization and control problems.

Abstract: Bayesian Optimization (BO) is a principled approach for optimizing expensive black-box functions, with Expected Improvement (EI) being one of the most widely used acquisition functions. Despite its empirical success, EI is known to be overly exploitative and can converge to suboptimal stationary points. We propose Expected Improvement via Gradient Norms (EI-GN), a novel acquisition function that applies the improvement principle to a gradient-aware auxiliary objective, thereby promoting sampling in regions that are both high-performing and approaching first-order stationarity. EI-GN relies on gradient observations used to learn gradient-enhanced surrogate models that enable principled gradient inference from function evaluations. We derive a tractable closed-form expression for EI-GN that allows efficient optimization and show that the proposed acquisition is consistent with the improvement-based acquisition framework. Empirical evaluations on standard BO benchmarks demonstrate that EI-GN yields consistent improvements against standard baselines. We further demonstrate applicability of EI-GN to control policy learning problems.

Method: PRISM formulates a class of component-based systems and performs RCA with theoretical guarantees, operating without dependency graphs while addressing fault propagation issues.

Result: On 735 failures across 9 real-world datasets, PRISM achieves 68% Top-1 accuracy, a 258% improvement over the best baseline, while requiring only 8ms per diagnosis.

Conclusion: PRISM provides a simple and efficient framework for RCA without dependency graphs, effectively handling fault propagation scenarios with strong theoretical guarantees and practical performance.

Abstract: Failures in complex systems demand rapid Root Cause Analysis (RCA) to prevent cascading damage. Existing RCA methods that operate without dependency graph typically assume that the root cause having the highest anomaly score. This assumption fails when faults propagate, as a small delay at the root cause can accumulate into a much larger anomaly downstream. In this paper, we propose PRISM, a simple and efficient framework for RCA when the dependency graph is absent. We formulate a class of component-based systems under which PRISM performs RCA with theoretical guarantees. On 735 failures across 9 real-world datasets, PRISM achieves 68% Top-1 accuracy, a 258% improvement over the best baseline, while requiring only 8ms per diagnosis.

Method: Theoretical analysis using mathematical tools from interacting particle systems, gradient flows, and mean-field theory. The approach treats token embeddings as particles on the unit sphere, layers as time steps, and analyzes the system’s dynamics through Wasserstein gradient flows.

Result: Shows that critical points of the system with perceptron blocks are generically atomic (discrete rather than continuous) and localized on subsets of the sphere, providing insight into the geometric structure of Transformer representations.

Conclusion: The perceptron block in Transformers induces specific geometric properties in the representation space, leading to discrete, localized critical points that may have implications for understanding Transformer dynamics and optimization.

Abstract: The forward pass of a Transformer can be seen as an interacting particle system on the unit sphere: time plays the role of layers, particles that of token embeddings, and the unit sphere idealizes layer normalization. In some weight settings the system can even be seen as a gradient flow for an explicit energy, and one can make sense of the infinite context length (mean-field) limit thanks to Wasserstein gradient flows. In this paper we study the effect of the perceptron block in this setting, and show that critical points are generically atomic and localized on subsets of the sphere.

Method: Three key components: (1) Patch-level Alignment for fine-grained correspondence across modalities, (2) Discrete Disentangled Interaction to separate common semantics into discrete latents and synergize unique information, (3) Critical-token Highlighting to emphasize query-relevant signals.

Result: MADI consistently outperforms both general-purpose LLMs and time-series-specialized MLLMs on synthetic and real-world benchmarks.

Conclusion: The proposed approach effectively addresses cross-modal integration challenges in time series understanding, enabling better multimodal reasoning through fine-grained alignment and disentangled interaction.

Abstract: Advances in multi-modal large language models (MLLMs) have inspired time series understanding and reasoning tasks, that enable natural language querying over time series, producing textual analyses of complex temporal dynamics. Recent attempts hybridize numerical time series with their visualized plots, facilitating precise value reasoning and visual structure comprehension for comprehensive time series understanding of MLLMs. However, effective cross-modal integration remains challenging due to fine-grained temporal misalignment across modalities and severe entanglement between shared and modality-specific semantics, which hinder localized interpretation and complementary reasoning. To address these issues, we propose MADI, a multi-modal LLM enhanced with fine-grained alignment and disentangled interaction, featuring (1) Patch-level Alignment, which enforces physically grounded fine-grained correspondence across heterogeneous modalities, (2) Discrete Disentangled Interaction, which separates modality-common semantics into compact discrete latents and adaptively synergizes the purified modality-unique information, and (3) Critical-token Highlighting, which emphasizes informative, query-relevant signals for robust reasoning. Experiments on synthetic and real-world benchmarks show that MADI consistently outperforms general-purpose LLMs and time-series-specialized MLLMs.

Method: Uses unsupervised contrastive learning to align GNNs with frozen pre-trained language models in continuous embedding space, then employs communication-efficient prompt tuning to adapt to downstream tasks without full-parameter fine-tuning.

Result: Outperforms all competitive baselines across multi-domain datasets on multiple tasks with up to 14.37% performance improvement.

Conclusion: FedGALA effectively addresses knowledge loss in federated graph learning by aligning GNNs and PLMs in continuous space and using efficient prompt tuning, demonstrating superior performance in distributed settings.

Abstract: Recent studies of federated graph foundational models (FedGFMs) break the idealized and untenable assumption of having centralized data storage to train graph foundation models, and accommodate the reality of distributed, privacy-restricted data silos. Despite their simplicity and intuition, existing studies that project aligned generalizable knowledge onto a discrete token space via vector-quantized backbones suffer from irreversible knowledge loss during the quantization process. In this context, we argue that reconciling the semantic-structural orthogonality and integrity between pre-trained language models (PLMs) and graph neural networks (GNNs) is paramount for developing effective FedGFMs while simultaneously mitigating the severe data heterogeneity and communication constraints inherent in distributed, resource-limited environments. To address these issues, we propose FedGALA (Federated Graph And Language Alignment), a framework that resolves graph-based semantic-structural orthogonality and integrity in federated settings by employing unsupervised contrastive learning to align GNNs and frozen PLMs within a continuous embedding space, thereby capturing robust, transferable general knowledge. Subsequently, FedGALA leverages a communication-efficient prompt tuning mechanism to steer these pre-aligned encoders and frozen PLMs, facilitating effective adaptation to diverse downstream tasks while circumventing the prohibitive overhead of full-parameter fine-tuning. The comprehensive experiments validate that FedGALA outperforms all competitive baselines across multi-domain datasets on multiple tasks with up to 14.37% performance improvement.

Method: Two-stage approach: 1) Target Variable Processing Stage (TVPS) uses Singular Spectrum Analysis (SSA) to decompose target variable, then LSTM for trend and P-Conv-LSTM with patching strategy for seasonality; 2) Extraneous Variables Processing Stage (EVPS) filters variables using Spearman correlation analysis and analyzes them with L-Attention module (LSTM + attention). Results combined via weighted summation and linear mapping.

Result: Outperforms existing state-of-the-art methods on four public datasets and shows strong performance on a private laptop motherboard test items dataset.

Conclusion: DA-SPS effectively handles multivariate time-series forecasting by separately processing target and extraneous variables with specialized modules, demonstrating superior performance across multiple datasets.

Abstract: Multivariate time-series forecasting, as a typical problem in the field of time series prediction, has a wide range of applications in weather forecasting, traffic flow prediction, and other scenarios. However, existing works do not effectively consider the impact of extraneous variables on the prediction of the target variable. On the other hand, they fail to fully extract complex sequence information based on various time patterns of the sequences. To address these drawbacks, we propose a DA-SPS model, which adopts different modules for feature extraction based on the information characteristics of different variables. DA-SPS mainly consists of two stages: the target variable processing stage (TVPS) and the extraneous variables processing stage (EVPS). In TVPS, the model first uses Singular Spectrum Analysis (SSA) to process the target variable sequence and then uses Long Short-Term Memory (LSTM) and P-Conv-LSTM which deploys a patching strategy to extract features from trend and seasonality components, respectively. In EVPS, the model filters extraneous variables that have a strong correlation with the target variate by using Spearman correlation analysis and further analyses them using the L-Attention module which consists of LSTM and attention mechanism. Finally, the results obtained by TVPS and EVPS are combined through weighted summation and linear mapping to produce the final prediction. The results on four public datasets demonstrate that the DA-SPS model outperforms existing state-of-the-art methods. Additionally, its performance in real-world scenarios is further validated using a private dataset collected by ourselves, which contains the test items’ information on laptop motherboards.

Method: Uses token-level filtering of pretraining data (rather than document-level) to remove specific capabilities. Introduces methodology using sparse autoencoders to label tokens and distill cheap, high-quality classifiers for filtering.

Result: Token filtering is highly effective, robust, and inexpensive at scale. For largest models, token filtering leads to 7000x compute slowdown on forget domain. Filtering effectiveness increases with model scale, and models can still be aligned on forget domains after filtering.

Conclusion: Filtering specific tokens during pretraining is a powerful approach for controlling model capabilities that becomes more effective with scale and can be robust to noisy labels with sufficient compute.

Abstract: Current approaches to reducing undesired capabilities in language models are largely post hoc, and can thus be easily bypassed by adversaries. A natural alternative is to shape capabilities during pretraining itself. On the proxy task of removing medical capabilities, we show that the simple intervention of filtering pretraining data is highly effective, robust, and inexpensive at scale. Inspired by work on data attribution, we show that filtering tokens is more effective than filtering documents, achieving the same hit to undesired capabilities at a lower cost to benign ones. Training models spanning two orders of magnitude, we then demonstrate that filtering gets more effective with scale: for our largest models, token filtering leads to a 7000x compute slowdown on the forget domain. We also show that models trained with token filtering can still be aligned on the forget domain. Along the way, we introduce a methodology for labeling tokens with sparse autoencoders and distilling cheap, high-quality classifiers. We also demonstrate that filtering can be robust to noisy labels with sufficient pretraining compute.

Method: Proposes Sim-MSTNet using sim2real approach: leverages simulator to generate synthetic data, employs domain randomization with bi-level optimization to reduce synthetic-real distribution gap, incorporates attention mechanisms for selective knowledge sharing between tasks, and applies dynamic loss weighting for task balancing.

Result: Extensive experiments on two open-source datasets show Sim-MSTNet consistently outperforms state-of-the-art baselines, achieving enhanced accuracy and generalization.

Conclusion: Sim-MSTNet effectively addresses data scarcity and task imbalance in network traffic forecasting through sim2real approach with domain randomization and attention-based multi-task learning.

Abstract: Network traffic forecasting plays a crucial role in intelligent network operations, but existing techniques often perform poorly when faced with limited data. Additionally, multi-task learning methods struggle with task imbalance and negative transfer, especially when modeling various service types. To overcome these challenges, we propose Sim-MSTNet, a multi-task spatiotemporal network traffic forecasting model based on the sim2real approach. Our method leverages a simulator to generate synthetic data, effectively addressing the issue of poor generalization caused by data scarcity. By employing a domain randomization technique, we reduce the distributional gap between synthetic and real data through bi-level optimization of both sample weighting and model training. Moreover, Sim-MSTNet incorporates attention-based mechanisms to selectively share knowledge between tasks and applies dynamic loss weighting to balance task objectives. Extensive experiments on two open-source datasets show that Sim-MSTNet consistently outperforms state-of-the-art baselines, achieving enhanced accuracy and generalization.

Method: Proposes UP-AAC with asymmetric architecture: actor receives stochastic states while critic gets deterministic states reconstructed in hindsight. Also includes attention-based Uncertainty Perception Model (UPM) to enhance actor’s scheduling decisions.

Result: Extensive experiments show the method outperforms existing approaches in reducing makespan on benchmark instances.

Conclusion: UP-AAC effectively addresses structural uncertainty in JSSP through asymmetric actor-critic architecture and uncertainty perception, leading to more stable learning and better scheduling performance.

Abstract: The Job-Shop Scheduling Problem (JSSP), under various forms of manufacturing uncertainty, has recently attracted considerable research attention. Most existing studies focus on parameter uncertainty, such as variable processing times, and typically adopt the actor-critic framework. In this paper, we explore a different but prevalent form of uncertainty in JSSP: structural uncertainty. Structural uncertainty arises when a job may follow one of several routing paths, and the selection is determined not by policy, but by situational factors (e.g., the quality of intermediate products) that cannot be known in advance. Existing methods struggle to address this challenge due to incorrect credit assignment: a high-quality action may be unfairly penalized if it is followed by a time-consuming path. To address this problem, we propose a novel method named UP-AAC. In contrast to conventional actor-critic methods, UP-AAC employs an asymmetric architecture. While its actor receives a standard stochastic state, the critic is crucially provided with a deterministic state reconstructed in hindsight. This design allows the critic to learn a more accurate value function, which in turn provides a lower-variance policy gradient to the actor, leading to more stable learning. In addition, we design an attention-based Uncertainty Perception Model (UPM) to enhance the actor’s scheduling decisions. Extensive experiments demonstrate that our method outperforms existing approaches in reducing makespan on benchmark instances.

Method: Proposes T2, which uses latent representations to estimate the optimal parallelism level for each sample before decoding, enabling dynamic allocation of computational resources based on sample difficulty.

Result: Experiments show T2 significantly reduces computational costs while maintaining comparable performance to fixed high-parallelism baselines, enabling more efficient parallel thinking.

Conclusion: The overscaling curse is a universal and severe problem in parallel thinking, and T2 provides a lightweight solution that breaks this curse by enabling sample-adaptive parallelism allocation.

Abstract: Parallel thinking enhances LLM reasoning by multi-path sampling and aggregation. In system-level evaluations, a global parallelism level N is allocated to all samples, typically set large to maximize overall dataset accuracy. However, due to sample heterogeneity, some samples can achieve comparable performance with a smaller N’< N, causing budget redundancy. This incompatibility between system-level efficacy and sample-level efficiency constitutes the overscaling curse. In this paper, we formalize and quantify the overscaling curse, showing its universality and severity in practice, and analyze its trigger mechanism. We then propose a lightweight method, T2, to break the overscaling curse, which utilizes latent representations to estimate the optimal parallelism level for each sample before decoding. Experiments show that T2 significantly reduces cost while maintaining comparable performance, enabling more efficient parallel thinking.

Method: Proposes Intrinsic Reward Policy Optimization (IRPO) - a policy optimization framework that leverages multiple intrinsic rewards to directly optimize a policy for extrinsic rewards without pretraining subpolicies. Uses a surrogate policy gradient that provides more informative learning signals than true gradients in sparse-reward environments.

Result: IRPO improves performance and sample efficiency relative to baselines in both discrete and continuous environments. Formal analysis of the optimization problem solved by IRPO is provided.

Conclusion: IRPO effectively addresses exploration challenges in sparse-reward RL by using intrinsic rewards in a novel policy optimization framework, overcoming limitations of existing approaches.

Abstract: Exploration is essential in reinforcement learning as an agent relies on trial and error to learn an optimal policy. However, when rewards are sparse, naive exploration strategies, like noise injection, are often insufficient. Intrinsic rewards can also provide principled guidance for exploration by, for example, combining them with extrinsic rewards to optimize a policy or using them to train subpolicies for hierarchical learning. However, the former approach suffers from unstable credit assignment, while the latter exhibits sample inefficiency and sub-optimality. We propose a policy optimization framework that leverages multiple intrinsic rewards to directly optimize a policy for an extrinsic reward without pretraining subpolicies. Our algorithm – intrinsic reward policy optimization (IRPO) – achieves this by using a surrogate policy gradient that provides a more informative learning signal than the true gradient in sparse-reward environments. We demonstrate that IRPO improves performance and sample efficiency relative to baselines in discrete and continuous environments, and formally analyze the optimization problem solved by IRPO. Our code is available at https://github.com/Mgineer117/IRPO.

Method: Proposes Repository-Centric Learning (RCL) paradigm that prioritizes vertical repository depth over horizontal task breadth. Implements a four-unit Repository-Centric Experience that transforms static codebases into interactive learning signals. Trains SWE-Spot-4B family of compact models (4B parameters) as repository-specialized experts.

Result: SWE-Spot-4B outperforms open-weight models up to larger sizes (including CWM by Meta, Qwen3-Coder-30B) and surpasses/matches efficiency-focused commercial models (GPT-4.1-mini, GPT-5-nano) across multiple software engineering tasks. RCL demonstrates higher training sample efficiency and lower inference costs.

Conclusion: Repository mastery is a distinct and necessary dimension that complements general coding capability for building efficient intelligence. The RCL paradigm enables SLMs to internalize the “physics” of target software environments through parametric knowledge acquisition rather than costly inference-time search.

Abstract: The deployment of coding agents in privacy-sensitive and resource-constrained environments drives the demand for capable open-weight Small Language Models (SLMs). However, they suffer from a fundamental capability gap: unlike frontier large models, they lack the inference-time strong generalization to work with complicated, unfamiliar codebases. We identify that the prevailing Task-Centric Learning (TCL) paradigm, which scales exposure across disparate repositories, fails to address this limitation. In response, we propose Repository-Centric Learning (RCL), a paradigm shift that prioritizes vertical repository depth over horizontal task breadth, suggesting SLMs must internalize the “physics” of a target software environment through parametric knowledge acquisition, rather than attempting to recover it via costly inference-time search. Following this new paradigm, we design a four-unit Repository-Centric Experience, transforming static codebases into interactive learning signals, to train SWE-Spot-4B, a family of highly compact models built as repo-specialized experts that breaks established scaling trends, outperforming open-weight models up to larger (e.g., CWM by Meta, Qwen3-Coder-30B) and surpassing/matching efficiency-focused commercial models (e.g., GPT-4.1-mini, GPT-5-nano) across multiple SWE tasks. Further analysis reveals that RCL yields higher training sample efficiency and lower inference costs, emphasizing that for building efficient intelligence, repository mastery is a distinct and necessary dimension that complements general coding capability.

Method: DiPO uses reinforcement learning to encourage LRMs to model task complexity spontaneously. It includes a difficulty modeling method based on model self-reasoning (reducing manual annotation needs) and a difficulty-signal-enhanced reward function that penalizes lengthy reasoning while considering performance and output format.

Result: DiPO enables models to adjust inference overhead spontaneously, significantly reducing redundant tokens without performance loss due to thought compression.

Conclusion: The framework successfully addresses overthinking in LRMs by integrating task difficulty awareness into reinforcement learning, improving resource efficiency while maintaining reasoning quality.

Abstract: Large Reasoning Models (LRMs) achieve explicit chain-of-thought expansion by imitating deep thinking behaviors of humans, demonstrating excellent performance in complex task scenarios. However, the deep-thinking mode often leads to unnecessarily lengthy reasoning and resource inefficiency when handling simple tasks. This overthinking phenomenon may arise from the generation preference triggered by the reward function during post-training. Existing research attempts to mitigate overthinking from the perspective of prompt design or model training, but generally underestimates the importance of task difficulty awareness, which makes it difficult for LRMs to effectively allocate reasoning resources. In this paper, we propose Difficulty-aware Policy Optimization (DiPO), a reinforcement learning-based LRM training framework. DiPO encourages LRM to spontaneously model task complexity, and integrates them into reinforcement learning framework to adjust the generation preferences introduced by post-training. A difficulty modeling method based on model self-reasoning is proposed, which significantly reduces the dependence on manual annotation and formalize task complexity. We further develop a difficulty-signal-enhanced reward function that incorporates a penalty for lengthy reasoning while considering reasoning performance and output format. Experimental results indicate that DiPO enables the model to spontaneously adjust inference overhead, significantly reducing redundant tokens without losing performance due to thought compression.

Method: Approaches RM through the lens of linear representation hypothesis, identifying one-dimensional representations corresponding to high-level concepts and performing linear operations on these concept vectors within forget-representation space.

Result: Empirical validation across various tasks shows that machine unlearning elicits both controllable side behaviors (truth, sentiment, refusal control) and enhanced capabilities (improved in-context learning), revealing this as either a hidden risk or a mechanism for developing models with stronger capabilities.

Conclusion: The phenomenon of representation manipulation in unlearning can be either a risk if misused or a valuable mechanism for creating models with enhanced capabilities and controllable behaviors, highlighting the dual nature of representation-based unlearning approaches.

Abstract: We consider representation misdirection (RM), a class of LLM unlearning methods that achieves forgetting by manipulating the forget-representations, that is, latent representations of forget samples. Despite being important, the roles of target vectors used in RM, however, remain underexplored. Here, we approach and revisit RM through the lens of the linear representation hypothesis. Specifically, if one can somehow identify a one-dimensional representation corresponding to a high-level concept, the linear representation hypothesis enables linear operations on this concept vector within the forget-representation space. Under this view, we hypothesize that, beyond forgetting, machine unlearning elicits controllable side behaviors and stronger side capabilities corresponding to the high-level concept. Our hypothesis is empirically validated across a wide range of tasks, including behavioral control (e.g., controlling unlearned models’ truth, sentiment, and refusal) and capability enhancement (e.g., improving unlearned models’ in-context learning capability). Our findings reveal that this fairly attractive phenomenon could be either a hidden risk if misused or a mechanism that can be harnessed for developing models that require stronger capabilities and controllable behaviors.

Method: Develops generalized prediction formulation accommodating arbitrary output targets (ε-, v-, x-prediction as special cases). Derives analytical relationship between data geometry and optimal prediction target. Proposes k-Diff framework that learns optimal prediction parameter k directly from data without explicit dimension estimation.

Result: Theory shows x-prediction becomes superior when ambient dimension significantly exceeds data’s intrinsic dimension. k-Diff consistently outperforms fixed-target baselines across varying architectures and data scales in both latent-space and pixel-space image generation.

Conclusion: Provides rigorous justification for target selection in diffusion models based on data dimensionality. k-Diff offers principled, automated approach to enhance generative performance by learning optimal prediction target from data.

Abstract: Recent advances in diffusion and flow matching models have highlighted a shift in the preferred prediction target – moving from noise ($\varepsilon$) and velocity (v) to direct data (x) prediction – particularly in high-dimensional settings. However, a formal explanation of why the optimal target depends on the specific properties of the data remains elusive. In this work, we provide a theoretical framework based on a generalized prediction formulation that accommodates arbitrary output targets, of which $\varepsilon$-, v-, and x-prediction are special cases. We derive the analytical relationship between data’s geometry and the optimal prediction target, offering a rigorous justification for why x-prediction becomes superior when the ambient dimension significantly exceeds the data’s intrinsic dimension. Furthermore, while our theory identifies dimensionality as the governing factor for the optimal prediction target, the intrinsic dimension of manifold-bound data is typically intractable to estimate in practice. To bridge this gap, we propose k-Diff, a framework that employs a data-driven approach to learn the optimal prediction parameter k directly from data, bypassing the need for explicit dimension estimation. Extensive experiments in both latent-space and pixel-space image generation demonstrate that k-Diff consistently outperforms fixed-target baselines across varying architectures and data scales, providing a principled and automated approach to enhancing generative performance.

Method: ConceptMoE uses a learnable chunk module to identify optimal token boundaries based on inter-token similarity, compressing sequences by ratio R. The MoE architecture enables controlled evaluation by reallocating saved computation to match baseline FLOPs and parameters.

Result: ConceptMoE outperforms standard MoE: +0.9 points on language pretraining, +2.3 points on long context understanding, +0.6 points on multimodal benchmarks. Gains reach +5.5 points with pretrained MoE conversion. Reduces attention computation by up to R²× and KV cache by R×, with prefill speedups up to 175% and decoding speedups up to 117% at R=2.

Conclusion: Adaptive concept-level processing fundamentally improves both effectiveness and efficiency of large language models, with minimal architectural modifications enabling straightforward integration into existing MoE systems.

Abstract: Large language models allocate uniform computation across all tokens, ignoring that some sequences are trivially predictable while others require deep reasoning. We introduce ConceptMoE, which dynamically merges semantically similar tokens into concept representations, performing implicit token-level compute allocation. A learnable chunk module identifies optimal boundaries by measuring inter-token similarity, compressing sequences by a target ratio $R$ before they enter the compute-intensive concept model. Crucially, the MoE architecture enables controlled evaluation: we reallocate saved computation to match baseline activated FLOPs (excluding attention map computation) and total parameters, isolating genuine architectural benefits. Under these conditions, ConceptMoE consistently outperforms standard MoE across language and vision-language tasks, achieving +0.9 points on language pretraining, +2.3 points on long context understanding, and +0.6 points on multimodal benchmarks. When converting pretrained MoE during continual training with layer looping, gains reach +5.5 points, demonstrating practical applicability. Beyond performance, ConceptMoE reduces attention computation by up to $R^2\times$ and KV cache by $R\times$. At $R=2$, empirical measurements show prefill speedups reaching 175% and decoding speedups up to 117% on long sequences. The minimal architectural modifications enable straightforward integration into existing MoE, demonstrating that adaptive concept-level processing fundamentally improves both effectiveness and efficiency of large language models.

Method: Develop a learnable three-channel codec that disentangles shared information from task-specific details across multiple vision tasks. Characterize limits through lossy common information concept and propose optimization objective balancing tradeoffs. Compare three codec architectures on two-task scenarios across six vision benchmarks.

Result: The approach substantially reduces redundancy and consistently outperforms independent coding across various vision benchmarks, demonstrating practical value of Gray-Wyner theory in modern machine learning.

Conclusion: The work successfully bridges classic information theory with task-driven representation learning, showing that revisiting Gray-Wyner theory has practical value for creating more efficient vision representations.

Abstract: Many computer vision tasks share substantial overlapping information, yet conventional codecs tend to ignore this, leading to redundant and inefficient representations. The Gray-Wyner network, a classical concept from information theory, offers a principled framework for separating common and task-specific information. Inspired by this idea, we develop a learnable three-channel codec that disentangles shared information from task-specific details across multiple vision tasks. We characterize the limits of this approach through the notion of lossy common information, and propose an optimization objective that balances inherent tradeoffs in learning such representations. Through comparisons of three codec architectures on two-task scenarios spanning six vision benchmarks, we demonstrate that our approach substantially reduces redundancy and consistently outperforms independent coding. These results highlight the practical value of revisiting Gray-Wyner theory in modern machine learning contexts, bridging classic information theory with task-driven representation learning.

Method: Transforms traffic matrices to RGB images for global dependency modeling; uses frozen LLM with trainable adapter for temporal semantics; employs dual-conditioning strategy to guide diffusion model for complex traffic generation

Result: Outperforms all baselines on Abilene (45.2% RMSE reduction) and GEANT (27.3% RMSE reduction) datasets; maintains stable performance from 1-step to 20-step predictions

Conclusion: LEAD effectively addresses TM forecasting challenges by combining vision backbones, LLM adapters, and diffusion models for accurate, uncertainty-aware predictions

Abstract: Driven by the evolution toward 6G and AI-native edge intelligence, network operations increasingly require predictive and risk-aware adaptation under stringent computation and latency constraints. Network Traffic Matrix (TM), which characterizes flow volumes between nodes, is a fundamental signal for proactive traffic engineering. However, accurate TM forecasting remains challenging due to the stochastic, non-linear, and bursty nature of network dynamics. Existing discriminative models often suffer from over-smoothing and provide limited uncertainty awareness, leading to poor fidelity under extreme bursts. To address these limitations, we propose LEAD, a Large Language Model (LLM)-Enhanced Adapter-based conditional Diffusion model. First, LEAD adopts a “Traffic-to-Image” paradigm to transform traffic matrices into RGB images, enabling global dependency modeling via vision backbones. Then, we design a “Frozen LLM with Trainable Adapter” model, which efficiently captures temporal semantics with limited computational cost. Moreover, we propose a Dual-Conditioning Strategy to precisely guide a diffusion model to generate complex, dynamic network traffic matrices. Experiments on the Abilene and GEANT datasets demonstrate that LEAD outperforms all baselines. On the Abilene dataset, LEAD attains a remarkable 45.2% reduction in RMSE against the best baseline, with the error margin rising only marginally from 0.1098 at one-step to 0.1134 at 20-step predictions. Meanwhile, on the GEANT dataset, LEAD achieves a 0.0258 RMSE at 20-step prediction horizon which is 27.3% lower than the best baseline.

Method: Two-phase approach: (1) Generate synthetic samples for 7 illicit activity patterns using parametrized generators, (2) Train separate GAEs (GAE-GCN, GAE-SAGE, GAE-GAT) on each pattern using reconstruction error without labels.

Result: GAE-GCN achieves most consistent reconstruction performance across patterns. GAE-SAGE and GAE-GAT show competitive results only in specific patterns.

Conclusion: Graph-based representation learning on synthetic data provides viable path for AI-driven illicit behavior detection, overcoming financial dataset limitations.

Abstract: Illicit financial activities such as money laundering often manifest through recurrent topological patterns in transaction networks. Detecting these patterns automatically remains challenging due to the scarcity of labeled real-world data and strict privacy constraints. To address this, we investigate whether Graph Autoencoders (GAEs) can effectively learn and distinguish topological patterns that mimic money laundering operations when trained on synthetic data. The analysis consists of two phases: (i) data generation, where synthetic samples are created for seven well-known illicit activity patterns using parametrized generators that preserve structural consistency while introducing realistic variability; and (ii) model training and validation, where separate GAEs are trained on each pattern without explicit labels, relying solely on reconstruction error as an indicator of learned structure. We compare three GAE implementations based on three distinct convolutional layers: Graph Convolutional (GAE-GCN), GraphSAGE (GAE-SAGE), and Graph Attention Network (GAE-GAT). Experimental results show that GAE-GCN achieves the most consistent reconstruction performance across patterns, while GAE-SAGE and GAE-GAT exhibit competitive results only in few specific patterns. These findings suggest that graph-based representation learning on synthetic data provides a viable path toward developing AI-driven tools for detecting illicit behaviors, overcoming the limitations of financial datasets.

Method: Proposes GeoNorm, which replaces standard normalization with geodesic updates on the manifold. Introduces layer-wise update decay for FFN and attention components, analogous to learning rate schedules. The method can be seamlessly integrated into standard Transformer architectures.

Result: Comprehensive experiments show GeoNorm consistently outperforms existing normalization methods in Transformer models. Achieves performance improvements with negligible additional computational cost.

Conclusion: GeoNorm provides a novel optimization-inspired approach to Transformer normalization that improves performance while maintaining computational efficiency and easy integration into existing architectures.

Abstract: The placement of normalization layers, specifically Pre-Norm and Post-Norm, remains an open question in Transformer architecture design. In this work, we rethink these approaches through the lens of manifold optimization, interpreting the outputs of the Feed-Forward Network (FFN) and attention layers as update directions in optimization. Building on this perspective, we introduce GeoNorm, a novel method that replaces standard normalization with geodesic updates on the manifold. Furthermore, analogous to learning rate schedules, we propose a layer-wise update decay for the FFN and attention components. Comprehensive experiments demonstrate that GeoNorm consistently outperforms existing normalization methods in Transformer models. Crucially, GeoNorm can be seamlessly integrated into standard Transformer architectures, achieving performance improvements with negligible additional computational cost.

Method: Proposes SAGE framework with two key innovations: 1) Sequence-level Signal Decoupling using geometric mean importance ratio with decoupled multi-objective advantages to eliminate token-level variance and solve “Reward Collapse”; 2) Asymmetric Adaptive Dynamics with dynamic gradient manifold that applies “Boost Factor” to cold-start items for super-linear updates and “Entropy Aware Penalty” to break information cocoons.

Result: Theoretical analysis and empirical results show SAGE effectively unblocks cold-start traffic and sustains recommendation diversity while maintaining the numerical stability of GBPO.

Conclusion: SAGE provides a unified optimization framework that enables efficient reuse of open-source LLM architectures without separate vocabularies and addresses key limitations in existing generative recommendation systems through adaptive gradient evolution.

Abstract: While works such as OneRec have validated the scaling laws of Large Language Models (LLMs) in recommender systems, they rely on a cumbersome separate vocabulary. This dependency prevents the model architecture from reusing native LLM vocabularies, resulting in high maintenance costs and poor scalability. In response, we aim to efficiently reuse open-source LLM architectures without constructing a separate tokenization vocabulary. Furthermore, we identify that the optimization strategy of OneRec Gradient Bounded Policy Optimization (GBPO),suffers from a “Symmetric Conservatism” problem: its static gradient boundaries structurally suppress the update momentum required for cold-start items and fail to prevent diversity collapse in high-noise environments.To address this issue, we propose SAGE (Sequence-level Adaptive Gradient Evolution), a unified optimization framework tailored for list-wise generative recommendation. SAGE introduces two key innovations:(1) Sequence-level Signal Decoupling: By combining a geometric mean importance ratio with decoupled multi-objective advantages, we eliminate token-level variance and resolve the “Reward Collapse” problem. (2) Asymmetric Adaptive Dynamics: We construct a dynamic gradient manifold that applies a “Boost Factor” to high-potential cold start items to achieve super-linear updates and employs an “Entropy Aware Penalty” to break information cocoons. Theoretical analysis and empirical results demonstrate that SAGE effectively unblocks cold-start traffic and sustains recommendation diversity, all while retaining the numerical stability of GBPO.

Method: Proposes HER framework with dual-layer thinking (first-person character thinking + third-person LLM thinking). Creates reasoning-augmented role-playing data via reverse engineering, constructs human-aligned principles and reward models, and trains models using supervised and reinforcement learning on Qwen3-32B.

Result: Models significantly outperform Qwen3-32B baseline with 30.26% improvement on CoSER benchmark and 14.97% gain on Minimax Role-Play Bench, demonstrating effective cognitive-level persona simulation.

Conclusion: HER framework successfully enables cognitive simulation in LLM role-playing through dual-layer thinking and human-aligned training, advancing beyond surface-level persona simulation to capture inner cognitive processes.

Abstract: LLM role-playing, i.e., using LLMs to simulate specific personas, has emerged as a key capability in various applications, such as companionship, content creation, and digital games. While current models effectively capture character tones and knowledge, simulating the inner thoughts behind their behaviors remains a challenge. Towards cognitive simulation in LLM role-play, previous efforts mainly suffer from two deficiencies: data with high-quality reasoning traces, and reliable reward signals aligned with human preferences. In this paper, we propose HER, a unified framework for cognitive-level persona simulation. HER introduces dual-layer thinking, which distinguishes characters’ first-person thinking from LLMs’ third-person thinking. To bridge these gaps, we curate reasoning-augmented role-playing data via reverse engineering and construct human-aligned principles and reward models. Leveraging these resources, we train \method models based on Qwen3-32B via supervised and reinforcement learning. Extensive experiments validate the effectiveness of our approach. Notably, our models significantly outperform the Qwen3-32B baseline, achieving a 30.26 improvement on the CoSER benchmark and a 14.97 gain on the Minimax Role-Play Bench. Our datasets, principles, and models will be released to facilitate future research.

Method: L³ generalizes embedding tables to decoder layers using static token-based routing to aggregate learned embeddings per token in a context-dependent way. It has two components: 1) a systems-friendly architecture for fast training and CPU-offloaded inference, and 2) an information-theoretic embedding allocation algorithm to balance speed and quality.

Result: Transformers with up to 2.6B active parameters trained with L³ strongly outperform both dense models and iso-sparse MoEs in both language modeling and downstream tasks.

Conclusion: L³ provides an effective new axis of sparsity that balances memory and compute by caching information in embeddings, offering better performance than existing sparse architectures while maintaining hardware efficiency.

Abstract: Modern sparse language models typically achieve sparsity through Mixture-of-Experts (MoE) layers, which dynamically route tokens to dense MLP “experts.” However, dynamic hard routing has a number of drawbacks, such as potentially poor hardware efficiency and needing auxiliary losses for stable training. In contrast, the tokenizer embedding table, which is natively sparse, largely avoids these issues by selecting a single embedding per token at the cost of not having contextual information. In this work, we introduce the Large Lookup Layer (L$^3$), which unlocks a new axis of sparsity by generalizing embedding tables to model decoder layers. L$^3$ layers use static token-based routing to aggregate a set of learned embeddings per token in a context-dependent way, allowing the model to efficiently balance memory and compute by caching information in embeddings. L$^3$ has two main components: (1) a systems-friendly architecture that allows for fast training and CPU-offloaded inference with no overhead, and (2) an information-theoretic embedding allocation algorithm that effectively balances speed and quality. We empirically test L$^3$ by training transformers with up to 2.6B active parameters and find that L$^3$ strongly outperforms both dense models and iso-sparse MoEs in both language modeling and downstream tasks.

Method: Extensive evaluation of over 2,000 models to identify “hidden gems.” Formulates model discovery as a Multi-Armed Bandit problem and accelerates Sequential Halving search algorithm using shared query sets and aggressive elimination schedules.

Result: Found rarely downloaded checkpoints that improve math performance from 83.2% to 96.0% without increasing inference costs. The method retrieves top models with as few as 50 queries per candidate, accelerating discovery by over 50x.

Conclusion: Superior fine-tuned models are systematically overlooked in public repositories, and efficient discovery methods like the proposed Multi-Armed Bandit approach can significantly accelerate finding these “hidden gems.”

Abstract: Public repositories host millions of fine-tuned models, yet community usage remains disproportionately concentrated on a small number of foundation checkpoints. We investigate whether this concentration reflects efficient market selection or if superior models are systematically overlooked. Through an extensive evaluation of over 2,000 models, we show the prevalence of “hidden gems”, unpopular fine-tunes that significantly outperform their popular counterparts. Notably, within the Llama-3.1-8B family, we find rarely downloaded checkpoints that improve math performance from 83.2% to 96.0% without increasing inference costs. However, discovering these models through exhaustive evaluation of every uploaded model is computationally infeasible. We therefore formulate model discovery as a Multi-Armed Bandit problem and accelerate the Sequential Halving search algorithm by using shared query sets and aggressive elimination schedules. Our method retrieves top models with as few as 50 queries per candidate, accelerating discovery by over 50x.

Method: Introduces Partial-Feedback Littlestone dimension (PFLdim) for deterministic learners and Partial-Feedback Measure Shattering dimension (PMSdim) for randomized learners. Uses new collection version space viewpoint and auxiliary dimensions. Analyzes conditions for inseparability between deterministic and randomized learnability.

Result: Complete characterization of minimax regret in set-realizable regime. Shows PFLdim precisely governs learnability for deterministic learners. Identifies conditions ensuring inseparability between deterministic and randomized learnability. Demonstrates sharp separation from weaker realistic and agnostic variants.

Conclusion: Partial-feedback online learning requires new complexity measures beyond standard approaches. Outside set realizability, the problem becomes information-theoretically intractable, highlighting need for noise-sensitive complexity measures for meaningful learnability characterization.

Abstract: We study partial-feedback online learning, where each instance admits a set of correct labels, but the learner only observes one correct label per round; any prediction within the correct set is counted as correct. This model captures settings such as language generation, where multiple responses may be valid but data provide only a single reference. We give a near-complete characterization of minimax regret for both deterministic and randomized learners in the set-realizable regime, i.e., in the regime where sublinear regret is generally attainable. For deterministic learners, we introduce the Partial-Feedback Littlestone dimension (PFLdim) and show it precisely governs learnability and minimax regret; technically, PFLdim cannot be defined via the standard version space, requiring a new collection version space viewpoint and an auxiliary dimension used only in the proof. We further develop the Partial-Feedback Measure Shattering dimension (PMSdim) to obtain tight bounds for randomized learners. We identify broad conditions ensuring inseparability between deterministic and randomized learnability (e.g., finite Helly number or nested-inclusion label structure), and extend the argument to set-valued online learning, resolving an open question of Raman et al. [2024b]. Finally, we show a sharp separation from weaker realistic and agnostic variants: outside set realizability, the problem can become information-theoretically intractable, with linear regret possible even for $|H|=2$. This highlights the need for fundamentally new, noise-sensitive complexity measures to meaningfully characterize learnability beyond set realizability.

Method: Proposes a general BCD framework that includes variable metric proximal gradient updates, proximal Newton updates, and alternated minimization updates. The framework unifies three popular Graphical Lasso solvers: graphical ISTA, Primal GLasso, and QUIC.

Result: The framework provides convergence guarantees for non-convex sparse precision matrix estimation and achieves up to 100-fold reduction in iterations needed to reach state-of-the-art estimation quality.

Conclusion: The proposed BCD framework offers a unified approach with strong theoretical guarantees and practical efficiency for non-convex optimization problems, particularly in sparse precision matrix estimation.

Abstract: Block-coordinate descent (BCD) is the method of choice to solve numerous large scale optimization problems, however their theoretical study for non-convex optimization, has received less attention. In this paper, we present a new block-coordinate descent (BCD) framework to tackle non-convex composite optimization problems, ensuring decrease of the objective function and convergence to a solution. This framework is general enough to include variable metric proximal gradient updates, proximal Newton updates, and alternated minimization updates. This generality allows to encompass three versions of the most used solvers in the sparse precision matrix estimation problem, deemed Graphical Lasso: graphical ISTA, Primal GLasso, and QUIC. We demonstrate the value of this new framework on non-convex sparse precision matrix estimation problems, providing convergence guarantees and up to a $100$-fold reduction in the number of iterations required to reach state-of-the-art estimation quality.

Method: Shows mathematical equivalence between PPI (prediction-powered inference) and SVRG (stochastic variance reduced gradients), both using control variates. Develops PPI-SVRG that combines both approaches, with convergence analysis decomposing into standard SVRG rate plus error floor from prediction uncertainty.

Result: PPI-SVRG reduces MSE by 43-52% under label scarcity on mean estimation benchmarks and improves test accuracy by 2.7-2.9 percentage points on MNIST with only 10% labeled data. When predictions are perfect, recovers SVRG exactly; when predictions degrade, convergence remains stable but reaches larger neighborhood.

Conclusion: PPI-SVRG effectively combines prediction information with variance reduction for semi-supervised optimization, with convergence properties that depend only on loss geometry while predictions affect only neighborhood size, providing stable performance even with imperfect predictions.

Abstract: We study semi-supervised stochastic optimization when labeled data is scarce but predictions from pre-trained models are available. PPI and SVRG both reduce variance through control variates – PPI uses predictions, SVRG uses reference gradients. We show they are mathematically equivalent and develop PPI-SVRG, which combines both. Our convergence bound decomposes into the standard SVRG rate plus an error floor from prediction uncertainty. The rate depends only on loss geometry; predictions affect only the neighborhood size. When predictions are perfect, we recover SVRG exactly. When predictions degrade, convergence remains stable but reaches a larger neighborhood. Experiments confirm the theory: PPI-SVRG reduces MSE by 43–52% under label scarcity on mean estimation benchmarks and improves test accuracy by 2.7–2.9 percentage points on MNIST with only 10% labeled data.

Method: Develops an estimator combining proxy scores with inverse-propensity-weighted residuals from ground-truth audits, constructs anytime-valid confidence sequences, and proposes an adaptive algorithm that concentrates audits on unreliable contexts and close arms using a plug-in Neyman rule.

Result: Theoretical guarantees show the algorithm achieves near-oracle audit efficiency and avoids mis-selection probability that wouldn’t vanish without bias correction. Numerical experiments confirm superior empirical performance compared to baselines.

Conclusion: The proposed framework effectively leverages biased proxy scores with selective ground-truth auditing for efficient best-arm identification, with theoretical guarantees and strong empirical performance.

Abstract: We study fixed-confidence best-arm identification (BAI) where a cheap but potentially biased proxy (e.g., LLM judge) is available for every sample, while an expensive ground-truth label can only be acquired selectively when using a human for auditing. Unlike classical multi-fidelity BAI, the proxy is biased (arm- and context-dependent) and ground truth is selectively observed. Consequently, standard multi-fidelity methods can mis-select the best arm, and uniform auditing, though accurate, wastes scarce resources and is inefficient. We prove that without bias correction and propensity adjustment, mis-selection probability may not vanish (even with unlimited proxy data). We then develop an estimator for the mean of each arm that combines proxy scores with inverse-propensity-weighted residuals and form anytime-valid confidence sequences for that estimator. Based on the estimator and confidence sequence, we propose an algorithm that adaptively selects and audits arms. The algorithm concentrates audits on unreliable contexts and close arms and we prove that a plug-in Neyman rule achieves near-oracle audit efficiency. Numerical experiments confirm the theoretical guarantees and demonstrate the superior empirical performance of the proposed algorithm.

Method: Energy-Guided Test-Time Scaling (ETS) estimates energy term via online Monte Carlo with provable convergence, uses acceleration frameworks and importance sampling to reduce latency while preserving quality

Result: ETS consistently improves generation quality across reasoning, coding, and science benchmarks for MLM models including autoregressive and diffusion language models

Conclusion: ETS provides effective training-free inference method for sampling from optimal RL policy with practical efficiency and quality preservation

Abstract: Reinforcement Learning (RL) post-training alignment for language models is effective, but also costly and unstable in practice, owing to its complicated training process. To address this, we propose a training-free inference method to sample directly from the optimal RL policy. The transition probability applied to Masked Language Modeling (MLM) consists of a reference policy model and an energy term. Based on this, our algorithm, Energy-Guided Test-Time Scaling (ETS), estimates the key energy term via online Monte Carlo, with a provable convergence rate. Moreover, to ensure practical efficiency, ETS leverages modern acceleration frameworks alongside tailored importance sampling estimators, substantially reducing inference latency while provably preserving sampling quality. Experiments on MLM (including autoregressive models and diffusion language models) across reasoning, coding, and science benchmarks show that our ETS consistently improves generation quality, validating its effectiveness and design.

Method: Proposes Visual-Guided Key-Token Regularization (ViKeR) that uses irrelevant visual inputs to predict ideal post-unlearning token distributions, regularizes unlearning with these distributions, identifies key tokens via information entropy, and amplifies gradient updates on key tokens.

Result: Experiments on MLLMU and CLEAR benchmarks show ViKeR effectively performs unlearning while mitigating forgetting and maintaining response coherence compared to existing methods.

Conclusion: ViKeR successfully addresses limitations of existing MLLM unlearning by incorporating visual guidance and token-level prioritization, offering a more effective approach for privacy protection in multimodal question answering.

Abstract: Unlearning in Multimodal Large Language Models (MLLMs) prevents the model from revealing private information when queried about target images. Existing MLLM unlearning methods largely adopt approaches developed for LLMs. They treat all answer tokens uniformly, disregarding their varying importance in the unlearning process. Moreover, these methods focus exclusively on the language modality, disregarding visual cues that indicate key tokens in answers. In this paper, after formulating the problem of unlearning in multimodal question answering for MLLMs, we propose Visual-Guided Key-Token Regularization (ViKeR). We leverage irrelevant visual inputs to predict ideal post-unlearning token-level distributions and use these distributions to regularize the unlearning process, thereby prioritizing key tokens. Further, we define key tokens in unlearning via information entropy and discuss ViKeR’s effectiveness through token-level gradient reweighting, which amplifies updates on key tokens. Experiments on MLLMU and CLEAR benchmarks demonstrate that our method effectively performs unlearning while mitigating forgetting and maintaining response coherence.

Method: Model LLM outputs directly in task-dependent latent structures equipped with dissimilarity measures, compute Bayes-optimal responses by synthesizing new responses in latent space rather than selecting from samples.

Result: Bayes-optimal responses consistently outperform standard decoding methods like beam search across different tasks; uncertainty quantification via Bayesian risk better captures output quality and correctness.

Conclusion: The decision-theoretic framework enables reliable task-aware LLM predictions for any problem admitting latent response structures, improving both response quality and uncertainty estimation.

Abstract: In many applications of LLMs, natural language responses often have an underlying structure such as representing discrete labels, numerical values, or graphs. Yet, existing decoding and uncertainty estimation methods operate only in language space and largely disregard structural information. We address this by modeling LLM outputs directly in a task-dependent latent structure. By equipping this structure with a dissimilarity measure, we can compute Bayes-optimal responses. These are not selected from sampled generations but are newly synthesized by combining individual responses in the latent space. Across different tasks, Bayes-optimal responses consistently outperform standard decoding methods like beam search. Moreover, quantifying uncertainty via the induced Bayesian risk captures variations in terms of the latent structure and improves alignment with output quality and correctness. Our decision-theoretic framework is applicable to any problem that admits a latent response structure and enables reliable task-aware LLM predictions.

Method: Proposes cascaded transfer learning organized as a rooted tree where information cascades hierarchically through tasks. Uses a minimum spanning tree structure based on task distance measures to connect tasks and allocates training budget along branches.

Result: Experiments on synthetic and real many-task settings demonstrate that the method enables more accurate and cost-effective adaptation across large task collections compared to alternative approaches.

Conclusion: Cascaded Transfer Learning provides an effective paradigm for many-task learning that respects budget constraints while improving accuracy through hierarchical knowledge transfer.

Abstract: Many-Task Learning refers to the setting where a large number of related tasks need to be learned, the exact relationships between tasks are not known. We introduce the Cascaded Transfer Learning, a novel many-task transfer learning paradigm where information (e.g. model parameters) cascades hierarchically through tasks that are learned by individual models of the same class, while respecting given budget constraints. The cascade is organized as a rooted tree that specifies the order in which tasks are learned and refined. We design a cascaded transfer mechanism deployed over a minimum spanning tree structure that connects the tasks according to a suitable distance measure, and allocates the available training budget along its branches. Experiments on synthetic and real many-task settings show that the resulting method enables more accurate and cost effective adaptation across large task collections compared to alternative approaches.

Method: Formal analysis establishing three theoretical results: 1) BWSPD’s gradient conditioning advantages, 2) Embedding-Space Batch Normalization approximation, 3) bi-Lipschitz bounds. Validation via unified Transformer framework comparing BWSPD, Log-Euclidean, and Euclidean embeddings across 1,500+ runs on three EEG paradigms.

Result: Log-Euclidean Transformer achieves state-of-the-art performance on all EEG datasets, substantially outperforming classical Riemannian classifiers and recent SPD baselines. BWSPD offers competitive accuracy with similar training time.

Conclusion: Theoretical analysis reveals embedding choice impacts gradient conditioning and numerical stability for SPD manifolds, with practical validation showing Log-Euclidean embeddings perform best for EEG classification tasks.

Abstract: Spatial covariance matrices of EEG signals are Symmetric Positive Definite (SPD) and lie on a Riemannian manifold, yet the theoretical connection between embedding geometry and optimization dynamics remains unexplored. We provide a formal analysis linking embedding choice to gradient conditioning and numerical stability for SPD manifolds, establishing three theoretical results: (1) BWSPD’s $\sqrtκ$ gradient conditioning (vs $κ$ for Log-Euclidean) via Daleckii-Kreĭn matrices provides better gradient conditioning on high-dimensional inputs ($d \geq 22$), with this advantage reducing on low-dimensional inputs ($d \leq 8$) where eigendecomposition overhead dominates; (2) Embedding-Space Batch Normalization (BN-Embed) approximates Riemannian normalization up to $O(\varepsilon^2)$ error, yielding $+26%$ accuracy on 56-channel ERP data but negligible effect on 8-channel SSVEP data, matching the channel-count-dependent prediction; (3) bi-Lipschitz bounds prove BWSPD tokens preserve manifold distances with distortion governed solely by the condition ratio $κ$. We validate these predictions via a unified Transformer framework comparing BWSPD, Log-Euclidean, and Euclidean embeddings within identical architecture across 1,500+ runs on three EEG paradigms (motor imagery, ERP, SSVEP; 36 subjects). Our Log-Euclidean Transformer achieves state-of-the-art performance on all datasets, substantially outperforming classical Riemannian classifiers and recent SPD baselines, while BWSPD offers competitive accuracy with similar training time.

Method: Proposes Reset-and-Discard (ReD) query method that resets the sampling process after each successful answer and discards repeated attempts on already-solved problems. This increases coverage@cost regardless of pass@k form. Also provides mathematical framework to connect pass@k and coverage@cost, and can infer power-law exponents if pass@k is unknown.

Result: Experiments on three LLMs using HumanEval show ReD substantially reduces required attempts, tokens, and USD cost to reach desired coverage. Also provides efficient way to measure inference power-laws.

Conclusion: ReD is an effective method to improve LLM query efficiency for verifiable tasks, addressing diminishing returns in coverage growth and offering practical cost savings while providing insights into model inference behavior.

Abstract: The performance of large language models (LLMs) on verifiable tasks is usually measured by pass@k, the probability of answering a question correctly at least once in k trials. At a fixed budget, a more suitable metric is coverage@cost, the average number of unique questions answered as a function of the total number of attempts. We connect the two metrics and show that the empirically-observed power-law behavior in pass@k leads to a sublinear growth of the coverage@cost (diminishing returns). To solve this problem, we propose Reset-and-Discard (ReD), a query method of LLMs that increases coverage@cost for any given budget, regardless of the pass@k form. Moreover, given a pass@k, we can quantitatively predict the savings in the total number of attempts using ReD. If pass@k is not available for the model, ReD can infer its power-law exponent. Experiments on three LLMs using HumanEval demonstrate that ReD substantially reduces the required attempts, tokens, and USD cost to reach a desired coverage, while also offering an efficient way to measure inference power-laws.

Method: Proposes using an interaction graph between agents to discern individual contributions more finely than global rewards, while alleviating cooperation problems of local rewards. Also introduces a practical approach for approximating such graphs.

Result: Experiments demonstrate the flexibility of the approach, showing improvements over traditional local and global reward settings in multi-agent reinforcement learning.

Conclusion: The proposed graph-based method effectively combines the merits of both global and local reward approaches in multi-agent RL, enabling better cooperation and credit assignment.

Abstract: To promote cooperation in Multi-Agent Reinforcement Learning, the reward signals of all agents can be aggregated together, forming global rewards that are commonly known as the fully cooperative setting. However, global rewards are usually noisy because they contain the contributions of all agents, which have to be resolved in the credit assignment process. On the other hand, using local reward benefits from faster learning due to the separation of agents’ contributions, but can be suboptimal as agents myopically optimize their own reward while disregarding the global optimality. In this work, we propose a method that combines the merits of both approaches. By using a graph of interaction between agents, our method discerns the individual agent contribution in a more fine-grained manner than a global reward, while alleviating the cooperation problem with agents’ local reward. We also introduce a practical approach for approximating such a graph. Our experiments demonstrate the flexibility of the approach, enabling improvements over the traditional local and global reward settings.

Method: Introduces new Lorentz linear layers based on “distance-to-hyperplane” formulation, along with Lorentzian activation functions and caching strategies to maintain hyperbolic geometry while improving computational efficiency.

Result: The new formulation achieves linear scaling of output hyperbolic norms with gradient descent steps, bridging the computation gap with Euclidean neural networks while preserving hyperbolic geometry properties.

Conclusion: Proposed hyperbolic neural network formulation maintains key hyperbolic geometry advantages while achieving computational efficiency comparable to Euclidean networks, enabling practical hyperbolic representation learning.

Abstract: Hyperbolic space is quickly gaining traction as a promising geometry for hierarchical and robust representation learning. A core open challenge is the development of a mathematical formulation of hyperbolic neural networks that is both efficient and captures the key properties of hyperbolic space. The Lorentz model of hyperbolic space has been shown to enable both fast forward and backward propagation. However, we prove that, with the current formulation of Lorentz linear layers, the hyperbolic norms of the outputs scale logarithmically with the number of gradient descent steps, nullifying the key advantage of hyperbolic geometry. We propose a new Lorentz linear layer grounded in the well-known ``distance-to-hyperplane” formulation. We prove that our formulation results in the usual linear scaling of output hyperbolic norms with respect to the number of gradient descent steps. Our new formulation, together with further algorithmic efficiencies through Lorentzian activation functions and a new caching strategy results in neural networks fully abiding by hyperbolic geometry while simultaneously bridging the computation gap to Euclidean neural networks. Code available at: https://github.com/robertdvdk/hyperbolic-fully-connected.

Method: Proposes Expert Modulation, a paradigm that conditions both routing and expert computation in Mixture-of-Experts architectures on textual signals, enabling direct and efficient cross-modal control over expert behavior without token-level fusion.

Result: The method demonstrates substantial improvements in multi-modal time series prediction through comprehensive theoretical analysis and experiments.

Conclusion: Expert Modulation provides a more effective approach for multi-modal time series forecasting by enabling direct textual conditioning of expert behavior, addressing limitations of token-level fusion methods.

Abstract: Real-world time series exhibit complex and evolving dynamics, making accurate forecasting extremely challenging. Recent multi-modal forecasting methods leverage textual information such as news reports to improve prediction, but most rely on token-level fusion that mixes temporal patches with language tokens in a shared embedding space. However, such fusion can be ill-suited when high-quality time-text pairs are scarce and when time series exhibit substantial variation in scale and characteristics, thus complicating cross-modal alignment. In parallel, Mixture-of-Experts (MoE) architectures have proven effective for both time series modeling and multi-modal learning, yet many existing MoE-based modality integration methods still depend on token-level fusion. To address this, we propose Expert Modulation, a new paradigm for multi-modal time series prediction that conditions both routing and expert computation on textual signals, enabling direct and efficient cross-modal control over expert behavior. Through comprehensive theoretical analysis and experiments, our proposed method demonstrates substantial improvements in multi-modal time series prediction. The current code is available at https://github.com/BruceZhangReve/MoME

Method: HistoPrism uses an efficient transformer-based architecture designed for pan-cancer prediction of gene expression from H&E histology. The approach introduces a pathway-level benchmark to shift evaluation from isolated gene-level variance to coherent functional pathways.

Result: HistoPrism surpasses prior state-of-the-art models on highly variable genes and achieves substantial gains on pathway-level prediction, demonstrating its ability to recover biologically coherent transcriptomic patterns with strong pan-cancer generalization and improved efficiency.

Conclusion: HistoPrism establishes a new standard for clinically relevant transcriptomic modeling from routinely available histology by focusing on biologically meaningful pathway-level predictions and demonstrating strong generalization across cancer types.

Abstract: Predicting spatial gene expression from H&E histology offers a scalable and clinically accessible alternative to sequencing, but realizing clinical impact requires models that generalize across cancer types and capture biologically coherent signals. Prior work is often limited to per-cancer settings and variance-based evaluation, leaving functional relevance underexplored. We introduce HistoPrism, an efficient transformer-based architecture for pan-cancer prediction of gene expression from histology. To evaluate biological meaning, we introduce a pathway-level benchmark, shifting assessment from isolated gene-level variance to coherent functional pathways. HistoPrism not only surpasses prior state-of-the-art models on highly variable genes , but also more importantly, achieves substantial gains on pathway-level prediction, demonstrating its ability to recover biologically coherent transcriptomic patterns. With strong pan-cancer generalization and improved efficiency, HistoPrism establishes a new standard for clinically relevant transcriptomic modeling from routinely available histology.

Method: Proposes Selective Adaptive Learning (SAL) that decomposes parameter space into mutually exclusive, sample-dependent regions, combining selective parameter activation with adaptive area partitioning and refined feedback alignment.

Result: SAL demonstrates competitive convergence rates, improved classification performance across 10 benchmarks, numerical consistency in deep regimes (up to 128 layers), and competitive accuracy in large-scale models (up to 1B parameters).

Conclusion: SAL offers a biologically plausible alternative to backpropagation that addresses key limitations and contributes to scalable neural network training research.

Abstract: Standard deep learning relies on Backpropagation (BP), which is constrained by biologically implausible weight symmetry and suffers from significant gradient interference within dense representations. To mitigate these bottlenecks, we propose Selective Adaptive Learning (SAL), a training method that combines selective parameter activation with adaptive area partitioning. Specifically, SAL decomposes the parameter space into mutually exclusive, sample-dependent regions. This decoupling mitigates gradient interference across divergent semantic patterns and addresses explicit weight symmetry requirements through our refined feedback alignment. Empirically, SAL demonstrates competitive convergence rates, leading to improved classification performance across 10 standard benchmarks. Additionally, SAL achieves numerical consistency and competitive accuracy even in deep regimes (up to 128 layers) and large-scale models (up to 1B parameters). Our approach is loosely inspired by biological learning mechanisms, offering a plausible alternative that contributes to the study of scalable neural network training.

Method: Instead of modifying model parameters, the method learns a transformation over representations that imposes an information bottleneck: maximizing mutual information with retained data while suppressing information about data to be forgotten. Uses variational surrogates to make the objective tractable, working in two regimes: when both retain and forget data are available, and in a zero-shot setting where only forget data can be accessed.

Result: Experiments across several benchmarks demonstrate that Representation Unlearning achieves more reliable forgetting, better utility retention, and greater computational efficiency than parameter-centric baselines.

Conclusion: Representation Unlearning provides a more effective framework for machine unlearning by operating in representation space rather than modifying parameters directly, offering improved stability, efficiency, and performance.

Abstract: Machine unlearning seeks to remove the influence of specific training data from a model, a need driven by privacy regulations and robustness concerns. Existing approaches typically modify model parameters, but such updates can be unstable, computationally costly, and limited by local approximations. We introduce Representation Unlearning, a framework that performs unlearning directly in the model’s representation space. Instead of modifying model parameters, we learn a transformation over representations that imposes an information bottleneck: maximizing mutual information with retained data while suppressing information about data to be forgotten. We derive variational surrogates that make this objective tractable and show how they can be instantiated in two practical regimes: when both retain and forget data are available, and in a zero-shot setting where only forget data can be accessed. Experiments across several benchmarks demonstrate that Representation Unlearning achieves more reliable forgetting, better utility retention, and greater computational efficiency than parameter-centric baselines.

Method: Proposes FlexCausal framework based on block-diagonal covariance VAE with Factorized Flow-based Prior to model complex densities of exogenous noise, integrates supervised alignment objectives with counterfactual consistency constraints, and uses manifold-aware relative intervention strategy for high-fidelity generation.

Result: Experimental results on both synthetic and real-world datasets demonstrate that FlexCausal significantly outperforms other methods.

Conclusion: FlexCausal effectively addresses limitations of existing CDRL methods by better modeling complex non-Gaussian noise distributions and ensuring precise structural correspondence between learned latent subspaces and ground-truth causal relations.

Abstract: Causal Disentangled Representation Learning(CDRL) aims to learn and disentangle low dimensional representations and their underlying causal structure from observations. However, existing disentanglement methods rely on a standard mean-field approximation with a diagonal posterior covariance, which decorrelates all latent dimensions. Additionally, these methods often assume isotropic Gaussian priors for exogenous noise, failing to capture the complex, non-Gaussian statistical properties prevalent in real-world causal factors. Therefore, we propose FlexCausal, a novel CDRL framework based on a block-diagonal covariance VAE. FlexCausal utilizes a Factorized Flow-based Prior to realistically model the complex densities of exogenous noise, effectively decoupling the learning of causal mechanisms from distributional statistics. By integrating supervised alignment objectives with counterfactual consistency constraints, our framework ensures a precise structural correspondence between the learned latent subspaces and the ground-truth causal relations. Finally, we introduce a manifold-aware relative intervention strategy to ensure high-fidelity generation. Experimental results on both synthetic and real-world datasets demonstrate that FlexCausal significantly outperforms other methods.

Method: Develops a unified information-theoretic framework using usable information theory. Establishes formal links between stitching performance and conditional mutual information, analyzes reconstruction-based metrics as estimators of usable information, and introduces a task-granularity hierarchy to unify concepts.

Result: Shows stitching is inherently asymmetric requiring bidirectional analysis, proves reconstruction metrics estimate usable information under constraints, demonstrates similarity depends on predictive family capacity, and establishes representational similarity as sufficient but not necessary for functional similarity.

Conclusion: Provides a comprehensive theoretical framework for representation similarity analysis that unifies functional and representational perspectives through information theory, offering principled guidance for comparing neural representations across models.

Abstract: We present a unified framework for quantifying the similarity between representations through the lens of \textit{usable information}, offering a rigorous theoretical and empirical synthesis across three key dimensions. First, addressing functional similarity, we establish a formal link between stitching performance and conditional mutual information. We further reveal that stitching is inherently asymmetric, demonstrating that robust functional comparison necessitates a bidirectional analysis rather than a unidirectional mapping. Second, concerning representational similarity, we prove that reconstruction-based metrics and standard tools (e.g., CKA, RSA) act as estimators of usable information under specific constraints. Crucially, we show that similarity is relative to the capacity of the predictive family: representations that appear distinct to a rigid observer may be identical to a more expressive one. Third, we demonstrate that representational similarity is sufficient but not necessary for functional similarity. We unify these concepts through a task-granularity hierarchy: similarity on a complex task guarantees similarity on any coarser derivative, establishing representational similarity as the limit of maximum granularity: input reconstruction.

Method: Proposes Signal-Adaptive Trust Regions (SATR), a distributional update rule that constrains relative policy changes by bounding KL divergence normalized by estimated signal energy. Automatically expands trust region under strong signals and contracts it when updates are noise-dominated. Instantiated for Bernoulli connectivity distributions and implemented with bitset optimization for binary spiking/weights.

Result: SATR improves stability under limited populations and achieves competitive returns across high-dimensional continuous-control benchmarks against strong baselines including PPO-LSTM. Bitset implementation substantially reduces wall-clock training time.

Conclusion: SATR provides an effective method for training RSNNs in RL by addressing gradient estimation variance through adaptive trust regions, making RSNN policy search practical at scale.

Abstract: Recurrent spiking neural networks (RSNNs) are a promising substrate for energy-efficient control policies, but training them for high-dimensional, long-horizon reinforcement learning remains challenging. Population-based, gradient-free optimization circumvents backpropagation through non-differentiable spike dynamics by estimating gradients. However, with finite populations, high variance of these estimates can induce harmful and overly aggressive update steps. Inspired by trust-region methods in reinforcement learning that constrain policy updates in distribution space, we propose \textbf{Signal-Adaptive Trust Regions (SATR)}, a distributional update rule that constrains relative change by bounding KL divergence normalized by an estimated signal energy. SATR automatically expands the trust region under strong signals and contracts it when updates are noise-dominated. We instantiate SATR for Bernoulli connectivity distributions, which have shown strong empirical performance for RSNN optimization. Across a suite of high-dimensional continuous-control benchmarks, SATR improves stability under limited populations and reaches competitive returns against strong baselines including PPO-LSTM. In addition, to make SATR practical at scale, we introduce a bitset implementation for binary spiking and binary weights, substantially reducing wall-clock training time and enabling fast RSNN policy search.

Method: Established gradient-based theoretical framework to explain forgetting, identified strongly negative gradient similarity as fundamental cause, categorized neurons into conflicting (cause forgetting) and collaborative (mitigate forgetting), then proposed CNL method that freezes conflicting neurons and updates only collaborative neurons.

Result: Experiments on five LLMs, four datasets, and four optimizers show CNL achieves zero forgetting in in-set settings and reduces forgetting by 59.1%-81.7% in out-of-set settings.

Conclusion: Theoretical framework explains catastrophic forgetting through gradient similarity, and CNL effectively mitigates forgetting by selectively updating collaborative neurons while freezing conflicting ones.

Abstract: Catastrophic forgetting during knowledge injection severely undermines the continual learning capability of large language models (LLMs). Although existing methods attempt to mitigate this issue, they often lack a foundational theoretical explanation. We establish a gradient-based theoretical framework to explain catastrophic forgetting. We first prove that strongly negative gradient similarity is a fundamental cause of forgetting. We then use gradient similarity to identify two types of neurons: conflicting neurons that induce forgetting and account for 50%-75% of neurons, and collaborative neurons that mitigate forgetting and account for 25%-50%. Based on this analysis, we propose a knowledge injection method, Collaborative Neural Learning (CNL). By freezing conflicting neurons and updating only collaborative neurons, CNL theoretically eliminates catastrophic forgetting under an infinitesimal learning rate eta and an exactly known mastered set. Experiments on five LLMs, four datasets, and four optimizers show that CNL achieves zero forgetting in in-set settings and reduces forgetting by 59.1%-81.7% in out-of-set settings.

Method: Proposes BatchEnsemble as a general uncertainty estimation method, and introduces GRUBE (BatchEnsemble GRU cell) for sequential modeling. Compares against Monte Carlo dropout and deep ensembles.

Result: BatchEnsemble matches deep ensemble uncertainty performance while outperforming Monte Carlo dropout. GRUBE achieves similar or better prediction and uncertainty estimation with fewer parameters and reduced computational costs.

Conclusion: BatchEnsemble and GRUBE offer efficient uncertainty estimation that matches traditional ensemble performance with computational advantages, making uncertainty estimation more practical for real-world applications.

Abstract: Deep learning models struggle with uncertainty estimation. Many approaches are either computationally infeasible or underestimate uncertainty. We investigate \textit{BatchEnsemble} as a general and scalable method for uncertainty estimation across both tabular and time series tasks. To extend BatchEnsemble to sequential modeling, we introduce GRUBE, a novel BatchEnsemble GRU cell. We compare the BatchEnsemble to Monte Carlo dropout and deep ensemble models. Our results show that BatchEnsemble matches the uncertainty estimation performance of deep ensembles, and clearly outperforms Monte Carlo dropout. GRUBE achieves similar or better performance in both prediction and uncertainty estimation. These findings show that BatchEnsemble and GRUBE achieve similar performance with fewer parameters and reduced training and inference time compared to traditional ensembles.

Method: CORDS provides an invertible mapping that transforms sets of spatial objects into continuous fields: a density field encoding object locations and count, and a feature field carrying attributes over the same support. Models operate in field space while remaining exactly decodable to discrete sets.

Result: Evaluated across molecular generation/regression, object detection, simulation-based inference, and mathematical task of recovering local maxima. Demonstrates robust handling of unknown set sizes with competitive accuracy.

Conclusion: CORDS offers a novel strategy for variable-sized set prediction by casting it as a continuous inference problem with invertible field representations, addressing limitations of existing methods.

Abstract: Many learning problems require predicting sets of objects when the number of objects is not known beforehand. Examples include object detection, molecular modeling, and scientific inference tasks such as astrophysical source detection. Existing methods often rely on padded representations or must explicitly infer the set size, which often poses challenges. We present a novel strategy for addressing this challenge by casting prediction of variable-sized sets as a continuous inference problem. Our approach, CORDS (Continuous Representations of Discrete Structures), provides an invertible mapping that transforms a set of spatial objects into continuous fields: a density field that encodes object locations and count, and a feature field that carries their attributes over the same support. Because the mapping is invertible, models operate entirely in field space while remaining exactly decodable to discrete sets. We evaluate CORDS across molecular generation and regression, object detection, simulation-based inference, and a mathematical task involving recovery of local maxima, demonstrating robust handling of unknown set sizes with competitive accuracy.

Method: Proposes FedSSA with two components: 1) For node feature heterogeneity, uses variational models to infer class-wise node distributions, clusters clients based on distributions, and minimizes divergence between local and cluster-level distributions. 2) For structural heterogeneity, employs spectral GNNs with spectral energy measures to characterize structural information, clusters clients based on spectral energy, and aligns spectral characteristics of local and cluster-level GNNs.

Result: Experiments on six homophilic and five heterophilic graph datasets under both non-overlapping and overlapping partitioning settings show FedSSA consistently outperforms eleven state-of-the-art methods.

Conclusion: FedSSA effectively addresses both semantic and structural heterogeneity in graph federated learning through distribution-based clustering and spectral alignment, achieving superior performance across diverse graph datasets.

Abstract: Graph Federated Learning (GFL) enables distributed graph representation learning while protecting the privacy of graph data. However, GFL suffers from heterogeneity arising from diverse node features and structural topologies across multiple clients. To address both types of heterogeneity, we propose a novel graph Federated learning method via Semantic and Structural Alignment (FedSSA), which shares the knowledge of both node features and structural topologies. For node feature heterogeneity, we propose a novel variational model to infer class-wise node distributions, so that we can cluster clients based on inferred distributions and construct cluster-level representative distributions. We then minimize the divergence between local and cluster-level distributions to facilitate semantic knowledge sharing. For structural heterogeneity, we employ spectral Graph Neural Networks (GNNs) and propose a spectral energy measure to characterize structural information, so that we can cluster clients based on spectral energy and build cluster-level spectral GNNs. We then align the spectral characteristics of local spectral GNNs with those of cluster-level spectral GNNs to enable structural knowledge sharing. Experiments on six homophilic and five heterophilic graph datasets under both non-overlapping and overlapping partitioning settings demonstrate that FedSSA consistently outperforms eleven state-of-the-art methods.

Method: Derives theoretical formulation showing global power distribution can be approximated by token-level scaled low-temperature distributions. Introduces training-free, verifier-free algorithm that sharpens base model’s generative distribution autoregressively without iterative MCMC.

Result: Method matches or surpasses one-shot GRPO on math, QA, and code tasks across four LLMs without external rewards, while reducing inference latency by over 10x compared to MCMC-based sampling.

Conclusion: Provides efficient alternative to RL post-training and MCMC sampling for improving LLM reasoning through distribution sharpening, with practical advantages in computational efficiency.

Abstract: Reinforcement learning (RL) post-training is a dominant approach for improving the reasoning performance of large language models (LLMs), yet growing evidence suggests that its gains arise primarily from distribution sharpening rather than the acquisition of new capabilities. Recent work has shown that sampling from the power distribution of LLMs using Markov chain Monte Carlo (MCMC) can recover performance comparable to RL post-training without relying on external rewards; however, the high computational cost of MCMC makes such approaches impractical for widespread adoption. In this work, we propose a theoretically grounded alternative that eliminates the need for iterative MCMC. We derive a novel formulation showing that the global power distribution can be approximated by a token-level scaled low-temperature one, where the scaling factor captures future trajectory quality. Leveraging this insight, we introduce a training-free and verifier-free algorithm that sharpens the base model’s generative distribution autoregressively. Empirically, we evaluate our method on math, QA, and code tasks across four LLMs, and show that our method matches or surpasses one-shot GRPO without relying on any external rewards, while reducing inference latency by over 10x compared to MCMC-based sampling.

Method: Uses relation algebra to study three core operations on relations: negation, converse, and composition. Proves theoretical results about tensor factorization requirements for negation/converse and fundamental obstruction for composition due to bilinearity incompatibility.

Result: For negation and converse, direction-agnostic first-order propagation requires tensor factorization separating entity-pair context from relation content. For composition, shows it reduces to conjunction and proves any conjunction on linear features must be bilinear, which is incompatible with negation, forcing feature map collapse.

Conclusion: Failures in knowledge editing, the reversal curse, and multi-hop reasoning may stem from common structural limitations inherent to the Linear Propagation Assumption, suggesting fundamental constraints in how neural networks handle logical operations.

Abstract: Neural networks adapt through first-order parameter updates, yet it remains unclear whether such updates preserve logical coherence. We investigate the geometric limits of the Linear Propagation Assumption (LPA), the premise that local updates coherently propagate to logical consequences. To formalize this, we adopt relation algebra and study three core operations on relations: negation flips truth values, converse swaps argument order, and composition chains relations. For negation and converse, we prove that guaranteeing direction-agnostic first-order propagation necessitates a tensor factorization separating entity-pair context from relation content. However, for composition, we identify a fundamental obstruction. We show that composition reduces to conjunction, and prove that any conjunction well-defined on linear features must be bilinear. Since bilinearity is incompatible with negation, this forces the feature map to collapse. These results suggest that failures in knowledge editing, the reversal curse, and multi-hop reasoning may stem from common structural limitations inherent to the LPA.

Method: Three key innovations: (1) uncertainty-guided node selection for targeted interventions, (2) low-rank representation interventions to preserve pre-trained knowledge, and (3) intervention-aware masked autoencoder that dynamically adjusts masking strategy based on node selection.

Result: Extensive experiments across five benchmark datasets demonstrate consistent and superior performance compared to existing approaches, with theoretical guarantees established for out-of-distribution settings.

Conclusion: TTReFT establishes representation fine-tuning as a new paradigm for graph test-time training, offering both theoretical grounding and immediate practical utility for real-world deployment.

Abstract: Graph Neural Networks frequently exhibit significant performance degradation in the out-of-distribution test scenario. While test-time training (TTT) offers a promising solution, existing Parameter Finetuning (PaFT) paradigm suffer from catastrophic forgetting, hindering their real-world applicability. We propose TTReFT, a novel Test-Time Representation FineTuning framework that transitions the adaptation target from model parameters to latent representations. Specifically, TTReFT achieves this through three key innovations: (1) uncertainty-guided node selection for specific interventions, (2) low-rank representation interventions that preserve pre-trained knowledge, and (3) an intervention-aware masked autoencoder that dynamically adjust masking strategy to accommodate the node selection scheme. Theoretically, we establish guarantees for TTReFT in OOD settings. Empirically, extensive experiments across five benchmark datasets demonstrate that TTReFT achieves consistent and superior performance. Our work establishes representation finetuning as a new paradigm for graph TTT, offering both theoretical grounding and immediate practical utility for real-world deployment.

Method: Developed an adaptive strategy based on rounding error analysis of function compositions f(g(x)) that identifies and selectively recomputes a small subset of critical components of g(x) with higher precision, while allowing most computations to use lower precision. Applied this strategy to different compositions within transformer architectures.

Result: Demonstrated on GPT-2 models that very low recomputation rates (small subset of components) can achieve improvements of up to two orders of magnitude (100x) in accuracy while maintaining computational efficiency.

Conclusion: The proposed mixed-precision adaptive recomputation strategy enables significantly more accurate transformer inference with minimal computational overhead, facilitating efficient local deployment of large language models.

Abstract: Mixed-precision computations are a hallmark of the current stage of AI, driving the progress in large language models towards efficient, locally deployable solutions. This article addresses the floating-point computation of compositionally-rich functions, concentrating on transformer inference. Based on the rounding error analysis of a composition $f(g(\mathrm{x}))$, we provide an adaptive strategy that selects a small subset of components of $g(\mathrm{x})$ to be computed more accurately while all other computations can be carried out with lower accuracy. We then explain how this strategy can be applied to different compositions within a transformer and illustrate its overall effect on transformer inference. We study the effectiveness of this algorithm numerically on GPT-2 models and demonstrate that already very low recomputation rates allow for improvements of up to two orders of magnitude in accuracy.

Method: Organizes training mechanisms by source, lifetime, and visibility; introduces seed-paired function-space causal estimands; develops portable perturbation primitives for momentum/Adam/EMA/BatchNorm; creates reporting checklist with audit artifacts like order hashes and RNG contracts.

Result: Provides a comprehensive framework for understanding training history effects, with practical tools for causal measurement and standardized reporting of training artifacts.

Conclusion: Proposes a protocol for portable, causal, uncertainty-aware measurement that attributes how much training history matters across different models, data, and training regimes.

Abstract: Modern deep-learning training is not memoryless. Updates depend on optimizer moments and averaging, data-order policies (random reshuffling vs with-replacement, staged augmentations and replay), the nonconvex path, and auxiliary state (teacher EMA/SWA, contrastive queues, BatchNorm statistics). This survey organizes mechanisms by source, lifetime, and visibility. It introduces seed-paired, function-space causal estimands; portable perturbation primitives (carry/reset of momentum/Adam/EMA/BN, order-window swaps, queue/teacher tweaks); and a reporting checklist with audit artifacts (order hashes, buffer/BN checksums, RNG contracts). The conclusion is a protocol for portable, causal, uncertainty-aware measurement that attributes how much training history matters across models, data, and regimes.

Method: Proposes Hessian Robust Quantization (HeRo-Q) algorithm that applies a lightweight, learnable rotation-compression matrix to weight space before quantization. This joint framework reshapes loss landscape by reducing largest Hessian eigenvalue and max eigenvalue, enhancing robustness to quantization noise without architectural changes.

Result: HeRo-Q consistently outperforms state-of-the-art methods (GPTQ, AWQ, SpinQuant) on Llama and Qwen models. Achieves superior performance in standard W4A8 settings and excels in challenging W3A16 ultra-low bit regime, boosting GSM8K accuracy on Llama3 8B to 70.15% while avoiding logical collapse seen in aggressive quantization.

Conclusion: HeRo-Q provides an effective solution to the PTQ paradox by addressing Hessian sensitivity, requires no architectural modifications, has negligible computational overhead, and integrates seamlessly into existing PTQ pipelines for robust quantization across various bit-widths.

Abstract: Post Training Quantization (PTQ), a mainstream model compression technique, often leads to the paradoxical ’low error, high loss’ phenomenon because it focuses solely on minimizing quantization error. The root cause lies in the Hessian matrix of the LLM loss landscape: a few high curvature directions are extremely sensitive to perturbations. To address this, we propose the Hessian Robust Quantization (HeRo Q) algorithm, which applies a lightweight, learnable rotation-compression matrix to the weight space prior to quantization. This joint framework reshapes the loss landscape by reducing the largest Hessian eigenvalue and reducing its max eigenvalue, thereby significantly enhancing robustness to quantization noise. HeRo-Q requires no architectural modifications, incurs negligible computational overhead, and integrates seamlessly into existing PTQ pipelines. Experiments on Llama and Qwen models show that HeRo Q consistently outperforms state of the art methods including GPTQ, AWQ, and SpinQuant not only achieving superior performance under standard W4A8 settings, but also excelling in the highly challenging W3A16 ultra low bit regime, where it boosts GSM8K accuracy on Llama3 8B to 70.15% and effectively avoids the logical collapse commonly seen in aggressive quantization.

Method: The authors develop two complementary approaches: 1) Rényi divergence-based bounds computed efficiently via dynamic programming, and 2) conditional composition for stronger privacy guarantees with small ε where Rényi divergence leads to over-approximation. The framework works for both banded and non-banded matrices.

Result: The proposed sampling-free bounds outperform existing Monte Carlo approaches, providing stronger privacy guarantees without requiring random abstention or large sample sizes. Numerical comparisons demonstrate efficacy across various matrix mechanisms used in research and practice.

Conclusion: The paper presents a robust framework for privacy amplification in differentially private matrix factorization that overcomes limitations of existing sampling-based methods, offering reliable bounds for both banded and non-banded matrices.

Abstract: We study privacy amplification for differentially private model training with matrix factorization under random allocation (also known as the balls-in-bins model). Recent work by Choquette-Choo et al. (2025) proposes a sampling-based Monte Carlo approach to compute amplification parameters in this setting. However, their guarantees either only hold with some high probability or require random abstention by the mechanism. Furthermore, the required number of samples for ensuring $(ε,δ)$-DP is inversely proportional to $δ$. In contrast, we develop sampling-free bounds based on Rényi divergence and conditional composition. The former is facilitated by a dynamic programming formulation to efficiently compute the bounds. The latter complements it by offering stronger privacy guarantees for small $ε$, where Rényi divergence bounds inherently lead to an over-approximation. Our framework applies to arbitrary banded and non-banded matrices. Through numerical comparisons, we demonstrate the efficacy of our approach across a broad range of matrix mechanisms used in research and practice.

Method: Conditional flow matching establishes bidirectional mapping between design parameters and simulated noise conditioned on performance labels; uses vortex lattice method for simulation data; proposes data augmentation with pseudo-labels from forward surrogate models.

Result: Demonstrates generation of distinct propeller geometries with nearly identical performance characteristics; analyzes trade-off between model accuracy and data availability; shows pseudo-label augmentation can improve performance.

Conclusion: GenAI with conditional flow matching shows versatility and potential for engineering design by enabling multiple valid design solutions from performance targets.

Abstract: In this paper, we explore the use of generative artificial intelligence (GenAI) for ship propeller design. While traditional forward machine learning models predict the performance of mechanical components based on given design parameters, GenAI models aim to generate designs that achieve specified performance targets. In particular, we employ conditional flow matching to establish a bidirectional mapping between design parameters and simulated noise that is conditioned on performance labels. This approach enables the generation of multiple valid designs corresponding to the same performance targets by sampling over the noise vector. To support model training, we generate data using a vortex lattice method for numerical simulation and analyze the trade-off between model accuracy and the amount of available data. We further propose data augmentation using pseudo-labels derived from less data-intensive forward surrogate models, which can often improve overall model performance. Finally, we present examples of distinct propeller geometries that exhibit nearly identical performance characteristics, illustrating the versatility and potential of GenAI in engineering design.

Method: Introduces Seg-MoE, a sparse MoE design that routes and processes contiguous time-step segments rather than making independent expert decisions. Each expert models intra-segment interactions directly, aligning with inherent temporal patterns. Integrated into a time-series Transformer architecture.

Result: Seg-MoE consistently achieves state-of-the-art forecasting accuracy across almost all prediction horizons on multiple multivariate long-term forecasting benchmarks, outperforming both dense Transformers and prior token-wise MoE models.

Conclusion: Segment-level routing is the key factor driving performance gains. Aligning MoE routing granularity with the inherent structure of time series provides a powerful inductive bias, opening new avenues for conditionally sparse architectures in sequential data modeling.

Abstract: Transformer-based models have recently made significant advances in accurate time-series forecasting, but even these architectures struggle to scale efficiently while capturing long-term temporal dynamics. Mixture-of-Experts (MoE) layers are a proven solution to scaling problems in natural language processing. However, existing MoE approaches for time-series forecasting rely on token-wise routing mechanisms, which may fail to exploit the natural locality and continuity of temporal data. In this work, we introduce Seg-MoE, a sparse MoE design that routes and processes contiguous time-step segments rather than making independent expert decisions. Token segments allow each expert to model intra-segment interactions directly, naturally aligning with inherent temporal patterns. We integrate Seg-MoE layers into a time-series Transformer and evaluate it on multiple multivariate long-term forecasting benchmarks. Seg-MoE consistently achieves state-of-the-art forecasting accuracy across almost all prediction horizons, outperforming both dense Transformers and prior token-wise MoE models. Comprehensive ablation studies confirm that segment-level routing is the key factor driving these gains. Our results show that aligning the MoE routing granularity with the inherent structure of time series provides a powerful, yet previously underexplored, inductive bias, opening new avenues for conditionally sparse architectures in sequential data modeling.

Method: Develops an abstract, architecture-agnostic formalism to prove that for networks with identifiable parameters, end-to-end equivariance implies existence of parameter choices yielding layerwise equivariance with respect to group actions on latent spaces.

Result: Proves that networks with end-to-end equivariance can be parameterized to have layerwise equivariance, assuming parameter identifiability (established for many network classes, conjectural for others).

Conclusion: Provides mathematical foundation explaining emergence of equivariant structures in neural network weights during training, bridging theory and empirical observations.

Abstract: We investigate the relation between end-to-end equivariance and layerwise equivariance in deep neural networks. We prove the following: For a network whose end-to-end function is equivariant with respect to group actions on the input and output spaces, there is a parameter choice yielding the same end-to-end function such that its layers are equivariant with respect to some group actions on the latent spaces. Our result assumes that the parameters of the model are identifiable in an appropriate sense. This identifiability property has been established in the literature for a large class of networks, to which our results apply immediately, while it is conjectural for others. The theory we develop is grounded in an abstract formalism, and is therefore architecture-agnostic. Overall, our results provide a mathematical explanation for the emergence of equivariant structures in the weights of neural networks during training – a phenomenon that is consistently observed in practice.

Method: Introduces representation holonomy, a gauge-invariant statistic that measures path dependence by quantifying “twist” accumulated when features are parallel-transported around small loops in input space. Uses global whitening for gauge fixing, aligns neighborhoods with shared subspaces and rotation-only Procrustes, then embeds back to full feature space.

Result: Holonomy increases with loop radius, separates models that appear similar under CKA, correlates with adversarial and corruption robustness, and tracks training dynamics as features form and stabilize. Proves invariance to orthogonal/affine transformations and establishes linear null for affine layers.

Conclusion: Representation holonomy serves as a practical, scalable diagnostic for probing geometric structure of learned representations beyond pointwise similarity, revealing hidden curvature that affects model robustness.

Abstract: Deep networks learn internal representations whose geometry–how features bend, rotate, and evolve–affects both generalization and robustness. Existing similarity measures such as CKA or SVCCA capture pointwise overlap between activation sets, but miss how representations change along input paths. Two models may appear nearly identical under these metrics yet respond very differently to perturbations or adversarial stress. We introduce representation holonomy, a gauge-invariant statistic that measures this path dependence. Conceptually, holonomy quantifies the “twist” accumulated when features are parallel-transported around a small loop in input space: flat representations yield zero holonomy, while nonzero values reveal hidden curvature. Our estimator fixes gauge through global whitening, aligns neighborhoods using shared subspaces and rotation-only Procrustes, and embeds the result back to the full feature space. We prove invariance to orthogonal (and affine, post-whitening) transformations, establish a linear null for affine layers, and show that holonomy vanishes at small radii. Empirically, holonomy increases with loop radius, separates models that appear similar under CKA, and correlates with adversarial and corruption robustness. It also tracks training dynamics as features form and stabilize. Together, these results position representation holonomy as a practical and scalable diagnostic for probing the geometric structure of learned representations beyond pointwise similarity.

Method: TabClustPFN is a prior-fitted network pretrained on synthetic datasets drawn from a flexible clustering prior. It performs amortized Bayesian inference over both cluster assignments and cluster cardinality, handling heterogeneous numerical and categorical features. The model clusters unseen datasets in a single forward pass without dataset-specific retraining or hyperparameter tuning.

Result: Experiments on synthetic data and curated real-world tabular benchmarks show that TabClustPFN outperforms classical, deep, and amortized clustering baselines, while exhibiting strong robustness in out-of-the-box exploratory settings.

Conclusion: TabClustPFN demonstrates effective amortized Bayesian inference for tabular clustering, providing strong generalization across diverse datasets without requiring dataset-specific tuning, making it suitable for exploratory data analysis.

Abstract: Clustering tabular data is a fundamental yet challenging problem due to heterogeneous feature types, diverse data-generating mechanisms, and the absence of transferable inductive biases across datasets. Prior-fitted networks (PFNs) have recently demonstrated strong generalization in supervised tabular learning by amortizing Bayesian inference under a broad synthetic prior. Extending this paradigm to clustering is nontrivial: clustering is unsupervised, admits a combinatorial and permutation-invariant output space, and requires inferring the number of clusters. We introduce TabClustPFN, a prior-fitted network for tabular data clustering that performs amortized Bayesian inference over both cluster assignments and cluster cardinality. Pretrained on synthetic datasets drawn from a flexible clustering prior, TabClustPFN clusters unseen datasets in a single forward pass, without dataset-specific retraining or hyperparameter tuning. The model naturally handles heterogeneous numerical and categorical features and adapts to a wide range of clustering structures. Experiments on synthetic data and curated real-world tabular benchmarks show that TabClustPFN outperforms classical, deep, and amortized clustering baselines, while exhibiting strong robustness in out-of-the-box exploratory settings. Code is available at https://github.com/Tianqi-Zhao/TabClustPFN.

Method: Proposes REPVLM which uses negative log-density of embeddings as proxy for epistemic uncertainty, computing probability density on hyperspherical manifold of VLM embeddings using Riemannian Flow Matching.

Result: Achieves near-perfect correlation between uncertainty and prediction error, significantly outperforming existing baselines. Also provides scalable metric for out-of-distribution detection and automated data curation.

Conclusion: REPVLM effectively quantifies epistemic uncertainty in VLMs, enabling more reliable deployment and additional applications like OOD detection and data curation.

Abstract: Vision-Language Models (VLMs) are typically deterministic in nature and lack intrinsic mechanisms to quantify epistemic uncertainty, which reflects the model’s lack of knowledge or ignorance of its own representations. We theoretically motivate negative log-density of an embedding as a proxy for the epistemic uncertainty, where low-density regions signify model ignorance. The proposed method REPVLM computes the probability density on the hyperspherical manifold of the VLM embeddings using Riemannian Flow Matching. We empirically demonstrate that REPVLM achieves near-perfect correlation between uncertainty and prediction error, significantly outperforming existing baselines. Beyond classification, we also demonstrate that the model also provides a scalable metric for out-of-distribution detection and automated data curation.

Method: Hierarchical framework combining simulation-derived priors with learned discrepancy corrections to reconstruct full spatial states from hyper-sparse sensor observations. Applied to satellite remote sensing for MODIS vegetation index reconstruction across six global sites.

Result: Outperforms established baselines requiring denser observations: 185% SSIM improvement over traditional baselines and 36% improvement over recent high-frequency methods. Particularly effective for landscapes with sharp boundaries and sub-seasonal dynamics, preserving diagnostically relevant structures like field topologies and spatial gradients.

Conclusion: SENDAI provides a lightweight, operationally viable framework for sparse-measurement reconstruction applicable to physically grounded inference, resource-limited deployment, and real-time monitoring and control.

Abstract: Bridging the gap between data-rich training regimes and observation-sparse deployment conditions remains a central challenge in spatiotemporal field reconstruction, particularly when target domains exhibit distributional shifts, heterogeneous structure, and multi-scale dynamics absent from available training data. We present SENDAI, a hierarchical Sparse-measurement, EfficieNt Data AssImilation Framework that reconstructs full spatial states from hyper sparse sensor observations by combining simulation-derived priors with learned discrepancy corrections. We demonstrate the performance on satellite remote sensing, reconstructing MODIS (Moderate Resolution Imaging Spectroradiometer) derived vegetation index fields across six globally distributed sites. Using seasonal periods as a proxy for domain shift, the framework consistently outperforms established baselines that require substantially denser observations – SENDAI achieves a maximum SSIM improvement of 185% over traditional baselines and a 36% improvement over recent high-frequency-based methods. These gains are particularly pronounced for landscapes with sharp boundaries and sub-seasonal dynamics; more importantly, the framework effectively preserves diagnostically relevant structures – such as field topologies, land cover discontinuities, and spatial gradients. By yielding corrections that are more structurally and spectrally separable, the reconstructed fields are better suited for downstream inference of indirectly observed variables. The results therefore highlight a lightweight and operationally viable framework for sparse-measurement reconstruction that is applicable to physically grounded inference, resource-limited deployment, and real-time monitor and control.

Method: The authors show mathematically that under idealized learning dynamics, the log-probability ratio between outcomes evolves linearly in their reward difference, causing exponential divergence and inevitable collapse. They propose inverse probability scaling (IPS) to remove outcome-frequency amplification from the learning signal. This is instantiated as IPS-GRPO (Group Relative Policy Optimization), a drop-in modification requiring no auxiliary models or architectural changes.

Result: Across different reasoning and molecular generation tasks, IPS-GRPO consistently reduces outcome-level mode collapse while matching or exceeding baseline performance. The method demonstrates that correcting the objective rather than adding exploration heuristics is key to reliable multimodal policy optimization.

Conclusion: Mode collapse in multimodal RL is a fundamental consequence of the expected-return objective, not just an exploration issue. Inverse probability scaling provides a principled solution that yields reward-proportional terminal distributions and prevents collapse, offering a more reliable approach to multimodal policy optimization.

Abstract: Many reinforcement learning (RL) problems admit multiple terminal solutions of comparable quality, where the goal is not to identify a single optimum but to represent a diverse set of high-quality outcomes. Nevertheless, policies trained by standard expected return maximization routinely collapse onto a small subset of outcomes, a phenomenon commonly attributed to insufficient exploration or weak regularization. We show that this explanation is incomplete: outcome level mode collapse is a structural consequence of the expected-return objective itself. Under idealized learning dynamics, the log-probability ratio between any two outcomes evolves linearly in their reward difference, implying exponential ratio divergence and inevitable collapse independent of the exploration strategy, entropy regularization, or optimization algorithm. We identify the source of this pathology as the probability multiplier inside the expectation and propose a minimal correction: inverse probability scaling, which removes outcome-frequency amplification from the learning signal, fundamentally changes the learning dynamics, and provably yields reward-proportional terminal distributions, preventing collapse in multimodal settings. We instantiate this principle in Group Relative Policy Optimization (GRPO) as a drop-in modification, IPS-GRPO, requiring no auxiliary models or architectural changes. Across different reasoning and molecular generation tasks, IPS-GRPO consistently reduces outcome-level mode collapse while matching or exceeding baseline performance, suggesting that correcting the objective rather than adding exploration heuristics is key to reliable multimodal policy optimization.

Method: Proposes a weakly-supervised VAE framework that decomposes representation into factor-specific subspaces and a residual subspace. Uses contrastive InfoNCE loss to encode target factors in assigned subspaces (pulling together same-factor latents, pushing apart mismatched pairs) and KL regularization to impose Gaussian structure without additional supervision for non-targeted factors.

Result: Achieves state-of-the-art disentanglement scores across multiple datasets with constant hyperparameters, shows consistent qualitative factor alignment, enables controlled factor swapping via latent replacement, scales correctly with increasing latent capacity, and works on real-world CelebA dataset.

Conclusion: XFactors provides an effective weakly-supervised approach for disentangling and controlling specific factors without adversarial training or classifiers, addressing scalability and stability issues of previous methods.

Abstract: Disentangled representation learning aims to map independent factors of variation to independent representation components. On one hand, purely unsupervised approaches have proven successful on fully disentangled synthetic data, but fail to recover semantic factors from real data without strong inductive biases. On the other hand, supervised approaches are unstable and hard to scale to large attribute sets because they rely on adversarial objectives or auxiliary classifiers. We introduce \textsc{XFactors}, a weakly-supervised VAE framework that disentangles and provides explicit control over a chosen set of factors. Building on the Disentangled Information Bottleneck perspective, we decompose the representation into a residual subspace $\mathcal{S}$ and factor-specific subspaces $\mathcal{T}_1,\ldots,\mathcal{T}_K$ and a residual subspace $\mathcal{S}$. Each target factor is encoded in its assigned $\mathcal{T}_i$ through contrastive supervision: an InfoNCE loss pulls together latents sharing the same factor value and pushes apart mismatched pairs. In parallel, KL regularization imposes a Gaussian structure on both $\mathcal{S}$ and the aggregated factor subspaces, organizing the geometry without additional supervision for non-targeted factors and avoiding adversarial training and classifiers. Across multiple datasets, with constant hyperparameters, \textsc{XFactors} achieves state-of-the-art disentanglement scores and yields consistent qualitative factor alignment in the corresponding subspaces, enabling controlled factor swapping via latent replacement. We further demonstrate that our method scales correctly with increasing latent capacity and evaluate it on the real-world dataset CelebA. Our code is available at \href{https://github.com/ICML26-anon/XFactors}{github.com/ICML26-anon/XFactors}.

Method: 1) Compress high-dimensional flow fields into compact latent space via reduced-order modeling with physics-informed disentanglement; 2) Use pretrained LLM as temporal processor with autoregressive prediction; 3) Implement modality alignment strategy to bridge gap between prompts and physical sequences.

Result: LLM4Fluid achieves state-of-the-art accuracy across diverse flow scenarios without retraining, demonstrating robust generalization, powerful zero-shot learning, and in-context learning capabilities.

Conclusion: LLMs can serve as effective generalizable neural solvers for spatio-temporal fluid dynamics prediction when combined with appropriate physics-informed compression and modality alignment techniques.

Abstract: Deep learning has emerged as a promising paradigm for spatio-temporal modeling of fluid dynamics. However, existing approaches often suffer from limited generalization to unseen flow conditions and typically require retraining when applied to new scenarios. In this paper, we present LLM4Fluid, a spatio-temporal prediction framework that leverages Large Language Models (LLMs) as generalizable neural solvers for fluid dynamics. The framework first compresses high-dimensional flow fields into a compact latent space via reduced-order modeling enhanced with a physics-informed disentanglement mechanism, effectively mitigating spatial feature entanglement while preserving essential flow structures. A pretrained LLM then serves as a temporal processor, autoregressively predicting the dynamics of physical sequences with time series prompts. To bridge the modality gap between prompts and physical sequences, which can otherwise degrade prediction accuracy, we propose a dedicated modality alignment strategy that resolves representational mismatch and stabilizes long-term prediction. Extensive experiments across diverse flow scenarios demonstrate that LLM4Fluid functions as a robust and generalizable neural solver without retraining, achieving state-of-the-art accuracy while exhibiting powerful zero-shot and in-context learning capabilities. Code and datasets are publicly available at https://github.com/qisongxiao/LLM4Fluid.

Method: Trained Pythia models (14M-410M parameters) for 300B tokens using three linguistically motivated curricula (Age-of-Acquisition, word frequency, Verb Variation) compared against Random ordering; also compared Random vs VV at 1B parameters. Analyzed training dynamics, gradient noise, output-head spectral saturation, and formalized link between difficulty pacing and optimization stability through gradient-variance control analysis.

Result: Across orderings, training follows a shared sequence of latent phases; curricula mainly change within-phase data exposure. Smaller models (up to 160M) with Random ordering show higher gradient noise and stronger late-training output-head spectral saturation with lower final accuracy; curricula reduce both effects. At larger scales, saturation differences are smaller and curriculum gains shrink.

Conclusion: Curriculum learning helps by stabilizing within-phase optimization rather than by creating new learning phases, with practical benefits more pronounced for smaller models where optimization stability is more critical.

Abstract: Curriculum learning changes the order of pre-training data, but it remains unclear whether it changes the learning trajectory or mainly reorders exposure over a fixed trajectory. We train Pythia models (14M-410M parameters) for 300B tokens under three linguistically motivated curricula-Age-of-Acquisition, word frequency, and Verb Variation (VV)-and compare each against Random ordering; at 1B parameters we compare Random and VV. Across orderings, training follows a shared sequence of latent phases, while curricula mainly change within-phase data exposure. In smaller models (up to 160M parameters), Random ordering exhibits higher gradient noise and stronger late-training output-head spectral saturation, alongside lower final accuracy; curricula reduce both effects at matched compute. At larger scales, saturation differences are smaller and curriculum gains shrink. We formalize the link between difficulty pacing and optimization stability in an idealized analysis based on gradient-variance control, and our results point to a practical takeaway: curricula help by stabilizing within-phase optimization rather than by creating new phases.

Method: First developed theory for deep linear networks to identify conditions where local-SSL algorithms (Forward-forward, CLAPP) implement exactly the same weight update as global BP-SSL. Then developed novel variants of local-SSL algorithms to approximate global BP-SSL in deep non-linear convolutional neural networks.

Result: Variants that improve similarity between local-SSL and global BP-SSL gradient updates show better performance on image datasets (CIFAR-10, STL-10, Tiny ImageNet). Best local-SSL rule with CLAPP loss matches performance of comparable global BP-SSL with InfoNCE or CPC-like loss functions, improving state-of-the-art for local SSL on these benchmarks.

Conclusion: Local self-supervised learning algorithms can be designed to match global backpropagation performance, establishing a theoretical and practical link between these two learning paradigms in deep neural networks.

Abstract: While end-to-end self-supervised learning with backpropagation (global BP-SSL) has become central for training modern AI systems, theories of local self-supervised learning (local-SSL) have struggled to build functional representations in deep neural networks. To establish a link between global and local rules, we first develop a theory for deep linear networks: we identify conditions for local-SSL algorithms (like Forward-forward or CLAPP) to implement exactly the same weight update as a global BP-SSL. Starting from the theoretical insights, we then develop novel variants of local-SSL algorithms to approximate global BP-SSL in deep non-linear convolutional neural networks. Variants that improve the similarity between gradient updates of local-SSL with those of global BP-SSL also show better performance on image datasets (CIFAR-10, STL-10, and Tiny ImageNet). The best local-SSL rule with the CLAPP loss function matches the performance of a comparable global BP-SSL with InfoNCE or CPC-like loss functions, and improves upon state-of-the-art for local SSL on these benchmarks.

Method: Learns orthonormal projection bases by directly minimizing decoder-layer output reconstruction error (not just KV matrix approximation). Precomputes error-rank profiles for each layer to enable flexible layer-wise rank allocation under user-specified error budgets.

Result: On Llama3-8B, outperforms EigenAttention by 11.9 points on C4 perplexity and 5.4% on 0-shot MMLU accuracy at iso-compression, with lower relative error and higher cosine similarity to original decoder-layer outputs.

Conclusion: StiefAttention provides superior KV-cache compression by directly optimizing for end-to-end decoder output reconstruction rather than proxy objectives, enabling efficient long-context inference with flexible error control.

Abstract: Key–value (KV) caching enables fast autoregressive decoding but at long contexts becomes a dominant bottleneck in High Bandwidth Memory (HBM) capacity and bandwidth. A common mitigation is to compress cached keys and values by projecting per-head matrixes to a lower rank, storing only the projections in the HBM. However, existing post-training approaches typically fit these projections using SVD-style proxy objectives, which may poorly reflect end-to-end reconstruction after softmax, value mixing, and subsequent decoder-layer transformations. For these reasons, we introduce StiefAttention, a post-training KV-cache compression method that learns \emph{orthonormal} projection bases by directly minimizing \emph{decoder-layer output reconstruction error}. StiefAttention additionally precomputes, for each layer, an error-rank profile over candidate ranks, enabling flexible layer-wise rank allocation under a user-specified error budget. Noteworthy, on Llama3-8B under the same conditions, StiefAttention outperforms EigenAttention by $11.9$ points on C4 perplexity and $5.4%$ on 0-shot MMLU accuracy at iso-compression, yielding lower relative error and higher cosine similarity with respect to the original decoder-layer outputs.

Method: Theoretical analysis of PIDM’s bias-variance tradeoff: predicting future states introduces bias but conditioning inverse dynamics models on predictions reduces variance. Establishes conditions on state predictor bias for PIDM to outperform BC. Empirical validation in 2D navigation tasks and complex 3D video game environments with high-dimensional visual inputs.

Result: BC requires up to 5 times (3 times on average) more demonstrations than PIDM in 2D navigation tasks to reach comparable performance. In complex 3D video game environments with high-dimensional visual inputs and stochastic transitions, BC requires over 66% more samples than PIDM.

Conclusion: PIDM introduces a beneficial bias-variance tradeoff that makes it more sample-efficient than behavior cloning, especially when expert demonstrations are limited. The theoretical framework explains why PIDM outperforms BC and provides conditions for its advantages.

Abstract: Behavior cloning (BC) is a practical offline imitation learning method, but it often fails when expert demonstrations are limited. Recent works have introduced a class of architectures named predictive inverse dynamics models (PIDM) that combine a future state predictor with an inverse dynamics model (IDM). While PIDM often outperforms BC, the reasons behind its benefits remain unclear. In this paper, we provide a theoretical explanation: PIDM introduces a bias-variance tradeoff. While predicting the future state introduces bias, conditioning the IDM on the prediction can significantly reduce variance. We establish conditions on the state predictor bias for PIDM to achieve lower prediction error and higher sample efficiency than BC, with the gap widening when additional data sources are available. We validate the theoretical insights empirically in 2D navigation tasks, where BC requires up to five times (three times on average) more demonstrations than PIDM to reach comparable performance; and in a complex 3D environment in a modern video game with high-dimensional visual inputs and stochastic transitions, where BC requires over 66% more samples than PIDM.

Method: Employ L2-Stability theory under heterogeneous hyperparameter environments to analyze generalization of merged models. Provides theoretical framework explaining existing merging algorithms and offers practical recommendations for fine-tuning.

Result: Extensive experiments on ResNet/ViT across 20/8 visual classification tasks with thousands of fine-tuned models confirm theoretical predictions about hyperparameter impact on merged model generalization.

Conclusion: Theoretical framework explains existing merging algorithms and provides actionable guidance for practitioners to strategically fine-tune expert models for better merging outcomes.

Abstract: Model merging efficiently aggregates capabilities from multiple fine-tuned models into a single one, operating purely in parameter space without original data or expensive re-computation. Despite empirical successes, a unified theory for its effectiveness under heterogeneous finetuning hyperparameters (e.g., varying learning rates, batch sizes) remains missing. Moreover, the lack of hyperparameter transparency in open-source fine-tuned models makes it difficult to predict merged-model performance, leaving practitioners without guidance on how to fine-tune merge-friendly experts. To address those two challenges, we employ $L_2$-Stability theory under heterogeneous hyperparameter environments to analyze the generalization of the merged model $\boldsymbol{x}{avg}$. This pioneering analysis yields two key contributions: (i) \textit{A unified theoretical framework} is provided to explain existing merging algorithms, revealing how they optimize specific terms in our bound, thus offering a strong theoretical foundation for empirical observations. (ii) \textit{Actionable recommendations} are proposed for practitioners to strategically fine-tune expert models, enabling the construction of merge-friendly models within the pretraining-to-finetuning pipeline. Extensive experiments on the ResNet/Vit family across 20/8 visual classification tasks, involving thousands of finetuning models, robustly confirm the impact of different hyperparameters on the generalization of $\boldsymbol{x}{avg}$ predicted by our theoretical results.

Method: Formalize the concept of gradient scale invariance, prove that Adam becomes gradient scale invariant of first order if and only if β₁=β₂, and conduct experiments across vision and language tasks with different architectures

Result: When β₁=β₂, gradient rescaling has a markedly smoother effect on updates, and this balanced regime aligns with design principles of recent scale-robust optimizers

Conclusion: The β₁=β₂ condition provides gradient scale invariance, explaining the empirical success of balanced Adam and offering a principle for future optimizer design

Abstract: Adam has been at the core of large-scale training for almost a decade, yet a simple empirical fact remains unaccounted for: both validation scores and the qualitative behaviour of the training runs improve when the momentum parameters satisfy $β_{1}=β_{2}$. Some recent studies have reported this pattern, but there is still no explanation for why this choice helps. We show that this choice is closely tied to a structural property that we refer to as \textit{gradient scale invariance}. We formalize this notion and prove that Adam becomes gradient scale invariant of first order if and only if $β_{1}=β_{2}$. This perspective places the balanced regime of Adam in direct alignment with the design principles underlying several recent optimizers that explicitly enforce scale-robust updates. The theory is supported by experiments across vision and language tasks, and across different architectural families, in which rescaling the gradient has a markedly smoother effect on the update when $β_{1}=β_{2}$. Overall, our results offer a coherent explanation for an open question in the behavior of Adam and provide a simple principle that helps guide the design of future optimizers.

Method: Proposes a flow matching model for conditional generation of high-dimensional time series data (monthly smart meter data at 15 min resolution). Different generative tasks are unified by treating them as partial data observations that are injected into the generation process, allowing a single model to handle multiple tasks without retraining.

Result: The model generates data consistent with given observations while remaining realistic, outperforming interpolation methods and other ML baselines dedicated to specific tasks.

Conclusion: Flow matching models provide a powerful unified approach for diverse smart meter data generative tasks, eliminating the need for task-specific models and retraining while maintaining data consistency and realism.

Abstract: Smart meter data is the foundation for planning and operating the distribution network. Unfortunately, such data are not always available due to privacy regulations. Meanwhile, the collected data may be corrupted due to sensor or transmission failure, or it may not have sufficient resolution for downstream tasks. A wide range of generative tasks is formulated to address these issues, including synthetic data generation, missing data imputation, and super-resolution. Despite the success of machine learning models on these tasks, dedicated models need to be designed and trained for each task, leading to redundancy and inefficiency. In this paper, by recognizing the powerful modeling capability of flow matching models, we propose a new approach to unify diverse smart meter data generative tasks with a single model trained for conditional generation. The proposed flow matching models are trained to generate challenging, high-dimensional time series data, specifically monthly smart meter data at a 15 min resolution. By viewing different generative tasks as distinct forms of partial data observations and injecting them into the generation process, we unify tasks such as imputation and super-resolution with a single model, eliminating the need for re-training. The data generated by our model not only are consistent with the given observations but also remain realistic, showing better performance against interpolation and other machine learning based baselines dedicated to the tasks.

Method: 1) Represent temporal EMR data as multivariate patient logs in relational schema, 2) Apply propositionalisation (relational aggregation/selection functions) to create interpretable features and “flatten” data, 3) Use selective naive Bayesian classifier for classification, 4) Provide four types of interpretation: univariate, global, local, and counterfactual.

Result: Experimental validation demonstrates the approach’s relevance and extreme interpretability while maintaining predictive performance comparable to deep learning methods.

Conclusion: The relational framework offers an interpretable alternative to black-box deep learning for sepsis prediction, enabling better understanding of patient sub-phenotypes and clinical decision-making through multiple interpretation perspectives.

Abstract: Sepsis remains one of the most complex and heterogeneous syndromes in intensive care, characterized by diverse physiological trajectories and variable responses to treatment. While deep learning models perform well in the early prediction of sepsis, they often lack interpretability and ignore latent patient sub-phenotypes. In this work, we propose a machine learning framework by opening up a new avenue for addressing this issue: a relational approach. Temporal data from electronic medical records (EMRs) are viewed as multivariate patient logs and represented in a relational data schema. Then, a propositionalisation technique (based on classic aggregation/selection functions from the field of relational data) is applied to construct interpretable features to “flatten” the data. Finally, the flattened data is classified using a selective naive Bayesian classifier. Experimental validation demonstrates the relevance of the suggested approach as well as its extreme interpretability. The interpretation is fourfold: univariate, global, local, and counterfactual.

Method: Proposes the Wishart projection mechanism: S ↦ M f(S) where M ∼ W_d(1/r I_d, r). Analyzes differential privacy properties for vector-valued and matrix-valued queries. For matrix queries, adds noise and analyzes privacy amplification from randomness and low-rank projection. Shows LoRA updates are an instance of this mechanism.

Result: For vector queries: non-asymptotic DP guarantees without additive noise. For matrix queries: noise-free version is not DP (AUC > 0.99 for membership inference attacks). Noisy variant shows privacy amplification from randomness and low-rank projection. LoRA is not inherently private but low-rank fine-tuning can be more private than full fine-tuning at same noise level.

Conclusion: Wishart randomness alone provides DP for vector queries but not for matrix queries. Noise combined with low-rank projection amplifies privacy. LoRA-style updates require careful privacy accounting as they’re not inherently private despite built-in randomness.

Abstract: We introduce the (Wishart) projection mechanism, a randomized map of the form $S \mapsto M f(S)$ with $M \sim W_d(1/r I_d, r)$ and study its differential privacy properties. For vector-valued queries $f$, we prove non-asymptotic DP guarantees without any additive noise, showing that Wishart randomness alone can suffice. For matrix-valued queries, however, we establish a sharp negative result: in the noise-free setting, the mechanism is not DP, and we demonstrate its vulnerability by implementing a near perfect membership inference attack (AUC $> 0.99$). We then analyze a noisy variant and prove privacy amplification due to randomness and low rank projection, in both large- and small-rank regimes, yielding stronger privacy guarantees than additive noise alone. Finally, we show that LoRA-style updates are an instance of the matrix-valued mechanism, implying that LoRA is not inherently private despite its built-in randomness, but that low-rank fine-tuning can be more private than full fine-tuning at the same noise level. Preliminary experiments suggest that tighter accounting enables lower noise and improved accuracy in practice.

Method: Mechanistic analysis identifies attention latent output as key intermediary linking function data to spectral structure. Propose decoder architectures that map PFN latents to explicit spectral density estimates and stationary kernels via Bochner’s theorem.

Result: Decoders recover complex multi-peak spectral mixtures and produce explicit kernels supporting Gaussian process regression with accuracy comparable to PFNs and optimization baselines, with orders-of-magnitude faster inference.

Conclusion: Framework enables interpretable extraction of spectral information from PFNs, providing transparent covariance models for downstream tasks while maintaining efficiency of amortized inference.

Abstract: Prior-Data Fitted Networks (PFNs) enable efficient amortized inference but lack transparent access to their learned priors and kernels. This opacity hinders their use in downstream tasks, such as surrogate-based optimization, that require explicit covariance models. We introduce an interpretability-driven framework for amortized spectral discovery from pre-trained PFNs with decoupled attention. We perform a mechanistic analysis on a trained PFN that identifies attention latent output as the key intermediary, linking observed function data to spectral structure. Building on this insight, we propose decoder architectures that map PFN latents to explicit spectral density estimates and corresponding stationary kernels via Bochner’s theorem. We study this pipeline in both single-realization and multi-realization regimes, contextualizing theoretical limits on spectral identifiability and proving consistency when multiple function samples are available. Empirically, the proposed decoders recover complex multi-peak spectral mixtures and produce explicit kernels that support Gaussian process regression with accuracy comparable to PFNs and optimization-based baselines, while requiring only a single forward pass. This yields orders-of-magnitude reductions in inference time compared to optimization-based baselines.

Method: Proposes a mixed-precision training and compilation framework using reinforcement learning to search for optimal quantization configurations that balance latency and accuracy in the massive search space.

Result: Achieves up to 2.48x speedup over state-of-the-art solutions with only 0.086% accuracy loss in the best case.

Conclusion: The RL-based approach effectively optimizes quantization for CIM accelerators, significantly improving performance while maintaining high accuracy.

Abstract: Computing-in-Memory (CIM) accelerators are a promising solution for accelerating Machine Learning (ML) workloads, as they perform Matrix-Vector Multiplications (MVMs) on crossbar arrays directly in memory. Although the bit widths of the crossbar inputs and cells are very limited, most CIM compilers do not support quantization below 8 bit. As a result, a single MVM requires many compute cycles, and weights cannot be efficiently stored in a single crossbar cell. To address this problem, we propose a mixed-precision training and compilation framework for CIM architectures. The biggest challenge is the massive search space, that makes it difficult to find good quantization parameters. This is why we introduce a reinforcement learning-based strategy to find suitable quantization configurations that balance latency and accuracy. In the best case, our approach achieves up to a 2.48x speedup over existing state-of-the-art solutions, with an accuracy loss of only 0.086 %.

Method: ECSEL learns formal expressions in the form of signomial equations. It directly constructs structural, closed-form expressions that serve as both classifiers and explanations. The method is designed to be computationally efficient while recovering target equations from symbolic regression benchmarks.

Result: On symbolic regression benchmarks, ECSEL recovers a larger fraction of target equations than state-of-the-art approaches with substantially less computation. It achieves classification accuracy competitive with established ML models while maintaining interpretability. The learned equations expose dataset biases, support counterfactual reasoning, and yield actionable insights in real-world applications like e-commerce and fraud detection.

Conclusion: ECSEL demonstrates that signomial equations can serve as effective, interpretable classifiers that provide meaningful explanations and insights into dataset characteristics, making them valuable for applications requiring both accuracy and transparency.

Abstract: We introduce ECSEL, an explainable classification method that learns formal expressions in the form of signomial equations, motivated by the observation that many symbolic regression benchmarks admit compact signomial structure. ECSEL directly constructs a structural, closed-form expression that serves as both a classifier and an explanation. On standard symbolic regression benchmarks, our method recovers a larger fraction of target equations than competing state-of-the-art approaches while requiring substantially less computation. Leveraging this efficiency, ECSEL achieves classification accuracy competitive with established machine learning models without sacrificing interpretability. Further, we show that ECSEL satisfies some desirable properties regarding global feature behavior, decision-boundary analysis, and local feature attributions. Experiments on benchmark datasets and two real-world case studies i.e., e-commerce and fraud detection, demonstrate that the learned equations expose dataset biases, support counterfactual reasoning, and yield actionable insights.

Method: Generalizes isotropic updates to incorporate anisotropic curvature through Fisher information geometry. Reformulates optimizer update as trust-region problem constrained by Kronecker-factored Fisher metric for structured preconditioning.

Result: Establishes convergence guarantees with O(1/√T) rate for expected squared gradient norm. Empirical evaluation shows superior training efficiency and final performance on image classification and language modeling benchmarks.

Conclusion: FISMO achieves structured preconditioning that adapts to local loss landscape geometry while maintaining computational tractability, outperforming established baselines.

Abstract: Training large-scale neural networks requires solving nonconvex optimization where the choice of optimizer fundamentally determines both convergence behavior and computational efficiency. While adaptive methods like Adam have long dominated practice, the recently proposed Muon optimizer achieves superior performance through orthogonalized momentum updates that enforce isotropic geometry with uniform singular values. However, this strict isotropy discards potentially valuable curvature information encoded in gradient spectra, motivating optimization methods that balance geometric structure with adaptivity. We introduce FISMO (Fisher-Structured Momentum-Orthogonalized) optimizer, which generalizes isotropic updates to incorporate anisotropic curvature information through Fisher information geometry. By reformulating the optimizer update as a trust-region problem constrained by a Kronecker-factored Fisher metric, FISMO achieves structured preconditioning that adapts to local loss landscape geometry while maintaining computational tractability. We establish convergence guarantees for FISMO in stochastic nonconvex settings, proving an $\mathcal{O}(1/\sqrt{T})$ rate for the expected squared gradient norm with explicit characterization of variance reduction through mini-batching. Empirical evaluation on image classification and language modeling benchmarks demonstrates that FISMO achieves superior training efficiency and final performance compared to established baselines.

Method: LORAUTER routes queries via task embeddings derived from small validation sets, operating at the task level rather than mapping queries directly to adapters. This enables efficient routing that scales with the number of tasks rather than adapters.

Result: LORAUTER consistently outperforms baseline routing approaches, matches Oracle performance (101.2%) when task-aligned adapters exist, achieves state-of-the-art results on unseen tasks (+5.2 points), and scales robustly to over 1500 adapters.

Conclusion: LORAUTER provides an effective task-level routing framework for LoRA adapters that scales efficiently and performs well across diverse tasks, including unseen ones, while being robust to large, noisy adapter pools.

Abstract: Low-rank adaptation (LoRA) enables parameter efficient specialization of large language models (LLMs) through modular adapters, resulting in rapidly growing public adapter pools spanning diverse tasks. Effectively using these adapters requires routing: selecting and composing the appropriate adapters for a query. We introduce LORAUTER, a novel routing framework that selects and composes LoRA adapters using task representations rather than adapter characteristics. Unlike existing approaches that map queries directly to adapters, LORAUTER routes queries via task embeddings derived from small validation sets and does not require adapter training data. By operating at the task level, LORAUTER achieves efficient routing that scales with the number of tasks rather than the number of adapters. Experiments across multiple tasks show that LORAUTER consistently outperforms baseline routing approaches, matching Oracle performance (101.2%) when task-aligned adapters exist and achieving state-of-the-art results on unseen tasks (+5.2 points). We further demonstrate the robustness of LORAUTER to very large, noisy adapter pools by scaling it to over 1500 adapters.

Method: Proposes a unified framework casting these operators as dynamic programs, deriving differentiable relaxations by smoothing the underlying recursions. Develops efficient parallel algorithms for deterministic/stochastic forward passes and vector-Jacobian products for backward passes.

Result: Proves Shannon entropy is the unique regularization choice yielding permutation-equivariant operators, characterizes regularizers inducing sparse selections. Demonstrates framework on decision-focused learning benchmark, constrained dynamic assortment RL problem, and discrete VAEs extension.

Conclusion: Provides a practical framework for integrating discrete subset selection operators into neural networks through differentiable relaxations, with theoretical guarantees and efficient algorithms.

Abstract: Knapsack and Top-k operators are useful for selecting discrete subsets of variables. However, their integration into neural networks is challenging as they are piecewise constant, yielding gradients that are zero almost everywhere. In this paper, we propose a unified framework casting these operators as dynamic programs, and derive differentiable relaxations by smoothing the underlying recursions. On the algorithmic side, we develop efficient parallel algorithms supporting both deterministic and stochastic forward passes, and vector-Jacobian products for the backward pass. On the theoretical side, we prove that Shannon entropy is the unique regularization choice yielding permutation-equivariant operators, and characterize regularizers inducing sparse selections. Finally, on the experimental side, we demonstrate our framework on a decision-focused learning benchmark, a constrained dynamic assortment RL problem, and an extension of discrete VAEs.

Method: Proposes Quantum LEGO Learning framework where pre-trained classical neural networks serve as frozen feature blocks and variational quantum circuits act as trainable adaptive modules. Develops block-wise generalization theory decomposing learning error into approximation and estimation components. Validates through systematic block-swap experiments across different feature extractors and adaptive heads.

Result: Experiments on quantum dot classification demonstrate stable optimization, reduced sensitivity to qubit count, and robustness to realistic noise. The framework enables efficient learning under constrained quantum resources and provides principled abstraction for analyzing hybrid models.

Conclusion: Quantum LEGO Learning offers a modular, theoretically grounded framework for hybrid quantum-classical learning that separates feature extraction from adaptive processing, enabling better analysis and more efficient use of quantum resources while maintaining performance.

Abstract: Hybrid quantum-classical learning models increasingly integrate neural networks with variational quantum circuits (VQCs) to exploit complementary inductive biases. However, many existing approaches rely on tightly coupled architectures or task-specific encoders, limiting conceptual clarity, generality, and transferability across learning settings. In this work, we introduce Quantum LEGO Learning, a modular and architecture-agnostic learning framework that treats classical and quantum components as reusable, composable learning blocks with well-defined roles. Within this framework, a pre-trained classical neural network serves as a frozen feature block, while a VQC acts as a trainable adaptive module that operates on structured representations rather than raw inputs. This separation enables efficient learning under constrained quantum resources and provides a principled abstraction for analyzing hybrid models. We develop a block-wise generalization theory that decomposes learning error into approximation and estimation components, explicitly characterizing how the complexity and training status of each block influence overall performance. Our analysis generalizes prior tensor-network-specific results and identifies conditions under which quantum modules provide representational advantages over comparably sized classical heads. Empirically, we validate the framework through systematic block-swap experiments across frozen feature extractors and both quantum and classical adaptive heads. Experiments on quantum dot classification demonstrate stable optimization, reduced sensitivity to qubit count, and robustness to realistic noise.

Method: Three key innovations: (1) efficient architecture using Mamba and Flash Attention mechanisms, (2) multimodal traffic representation preserving essential information while eliminating biases, and (3) label distribution-aware fine-tuning strategy.

Result: Superior classification performance with up to 6.44% F1 score improvement over SOTA baselines, 1.7x higher inference throughput, excellent few-shot learning abilities, and real-world throughput of 261.87 Mb/s in online system.

Conclusion: NetMamba+ is the first framework to adapt Mamba architecture for network traffic classification, enabling efficient and accurate traffic analysis in complex network environments with robust real-world performance.

Abstract: With the rapid growth of encrypted network traffic, effective traffic classification has become essential for network security and quality of service management. Current machine learning and deep learning approaches for traffic classification face three critical challenges: computational inefficiency of Transformer architectures, inadequate traffic representations with loss of crucial byte-level features while retaining detrimental biases, and poor handling of long-tail distributions in real-world data. We propose NetMamba+, a framework that addresses these challenges through three key innovations: (1) an efficient architecture considering Mamba and Flash Attention mechanisms, (2) a multimodal traffic representation scheme that preserves essential traffic information while eliminating biases, and (3) a label distribution-aware fine-tuning strategy. Evaluation experiments on massive datasets encompassing four main classification tasks showcase NetMamba+’s superior classification performance compared to state-of-the-art baselines, with improvements of up to 6.44% in F1 score. Moreover, NetMamba+ demonstrates excellent efficiency, achieving 1.7x higher inference throughput than the best baseline while maintaining comparably low memory usage. Furthermore, NetMamba+ exhibits superior few-shot learning abilities, achieving better classification performance with fewer labeled data. Additionally, we implement an online traffic classification system that demonstrates robust real-world performance with a throughput of 261.87 Mb/s. As the first framework to adapt Mamba architecture for network traffic classification, NetMamba+ opens new possibilities for efficient and accurate traffic analysis in complex network environments.

Method: Knowledge Vector Weakening (KVW) identifies knowledge vectors activated during model output generation on forget sets and progressively weakens their contributions without gradient computation, directly intervening in the full model.

Result: KVW achieves stable forget-retain trade-off on MLLMU and CLEAR benchmarks while significantly improving computational efficiency over gradient-based and LoRA-based unlearning methods.

Conclusion: KVW provides an efficient, training-free solution for unlearning in Large Vision-Language Models, addressing computational bottlenecks while maintaining effective knowledge removal.

Abstract: Large Vision-Language Models (LVLMs) are widely adopted for their strong multimodal capabilities, yet they raise serious concerns such as privacy leakage and harmful content generation. Machine unlearning has emerged as a promising solution for removing the influence of specific data from trained models. However, existing approaches largely rely on gradient-based optimization, incurring substantial computational costs for large-scale LVLMs. To address this limitation, we propose Knowledge Vector Weakening (KVW), a training-free unlearning method that directly intervenes in the full model without gradient computation. KVW identifies knowledge vectors that are activated during the model’s output generation on the forget set and progressively weakens their contributions, thereby preventing the model from exploiting undesirable knowledge. Experiments on the MLLMU and CLEAR benchmarks demonstrate that KVW achieves a stable forget-retain trade-off while significantly improving computational efficiency over gradient-based and LoRA-based unlearning methods.

Method: Encoder-only Transformer with sparse Mixture-of-Heterogeneous-Experts layers that route temporal patches to specialized experts: shared depthwise-convolution expert for sequence-level continuity and routed Fourier-based experts for patch-level periodic structures. Incorporates exogenous information via cross-attention and uses lightweight convolutional patch decoder instead of parameter-heavy linear heads.

Result: State-of-the-art performance across seven multivariate benchmarks and multiple horizons, reducing average MSE by 12% compared to strong recent baselines. Demonstrates effective heterogeneous specialization for long-term forecasting.

Conclusion: MoHETS effectively addresses the limitations of homogeneous MoE approaches by introducing heterogeneous experts specialized for different temporal patterns, improving both performance and parameter efficiency for multivariate time series forecasting.

Abstract: Real-world multivariate time series can exhibit intricate multi-scale structures, including global trends, local periodicities, and non-stationary regimes, which makes long-horizon forecasting challenging. Although sparse Mixture-of-Experts (MoE) approaches improve scalability and specialization, they typically rely on homogeneous MLP experts that poorly capture the diverse temporal dynamics of time series data. We address these limitations with MoHETS, an encoder-only Transformer that integrates sparse Mixture-of-Heterogeneous-Experts (MoHE) layers. MoHE routes temporal patches to a small subset of expert networks, combining a shared depthwise-convolution expert for sequence-level continuity with routed Fourier-based experts for patch-level periodic structures. MoHETS further improves robustness to non-stationary dynamics by incorporating exogenous information via cross-attention over covariate patch embeddings. Finally, we replace parameter-heavy linear projection heads with a lightweight convolutional patch decoder, improving parameter efficiency, reducing training instability, and allowing a single model to generalize across arbitrary forecast horizons. We validate across seven multivariate benchmarks and multiple horizons, with MoHETS consistently achieving state-of-the-art performance, reducing the average MSE by $12%$ compared to strong recent baselines, demonstrating effective heterogeneous specialization for long-term forecasting.