Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs) by integrating them with an external knowledge base to improve the answer relevance and accuracy. In real-world scenarios, beyond pure text, a substantial amount of knowledge is stored in tables, and user questions often require retrieving answers that are distributed across multiple tables. Retrieving knowledge from a table corpora (i.e., various individual tables) for a question remains nascent, for (i) how to understand intra- and inter-table knowledge effectively, (ii) how to filter unnecessary tables and retrieve the most relevant tables efficiently, (iii) how to organize complex retrieved contexts for LLMs’ reasoning, and (iv) how to evaluate the corresponding performance in a realistic setting. Facing the above challenges, in this paper, we first propose a table-corpora-aware RAG framework, named T-RAG, which consists of the hierarchical memory index, multi-stage retrieval, and graph-aware context organization for effective and efficient table knowledge retrieval and inference. Then, we develop a multi-table question answering benchmark named MultiTableQA, which spans 3 different task types, 57,193 tables, and 23,758 questions in total, and the sources are all from real-world scenarios. Based on MultiTableQA, we perform a comprehensive comparison of table retrieval methods, RAG-based approaches, and table-to-graph representation learning methods. T-RAG consistently achieves state-of-the-art accuracy, recall, and runtime performance, with improvements of up to 9.4%. Moreover, T-RAG yields an average inference gain of 11.8% across different downstream backbone LLMs. Our code and data are available at https://github.com/jiaruzouu/T-RAG.
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Decoupled Reasoning with Implicit Fact Tokens (DRIFT): A Dual-Model Framework for Efficient Long-Context Inference
PDF ↗The integration of extensive, dynamic knowledge into Large Language Models (LLMs) remains a significant challenge due to the inherent entanglement of factual data and reasoning patterns. Existing solutions, ranging from non-parametric Retrieval-Augmented Generation (RAG) to parametric knowledge editing, are often constrained in practice by finite context windows, retriever noise, or the risk of catastrophic forgetting. In this paper, we propose DRIFT, a novel dual-model architecture designed to explicitly decouple knowledge extraction from the reasoning process. Unlike static prompt compression, DRIFT employs a lightweight knowledge model to dynamically compress document chunks into implicit fact tokens conditioned on the query. These dense representations are projected into the reasoning model’s embedding space, replacing raw, redundant text while maintaining inference accuracy. Extensive experiments show that DRIFT significantly improves performance on long-context tasks, outperforming strong baselines among comparably sized models. Our approach provides a scalable and efficient paradigm for extending the effective context window and reasoning capabilities of LLMs. Our code and data will be made public upon publication.
Med-SRAF: A Multi-Agent Framework for Medical Reasoning via Semantic Routing and Agentic Fusion
PDF ↗While Retrieval-Augmented Generation (RAG) has become a standard paradigm for mitigating hallucinations in Large Language Models (LLMs), its effectiveness in complex medical reasoning remains limited. Existing RAG methods suffer from two main challenges: First, **Semantic Drift**: without explicit domain constraints, LLM-driven query decomposition often deviates from the original clinical intent, introducing substantial noise that degrades retrieval relevance. Second, **Concatenation Fallacy**: retrieved evidence from different semantic aspects is aggregated in a naive, unstructured manner, without modeling their inter-dependencies and potential conflicts, which ultimately undermines downstream reasoning. To address these challenges, we propose **Med-SRAF**, a multi-agent retrieval augmentation framework guided by medical domain knowledge. This framework reconstructs the traditional RAG process through two core mechanisms: (1) Intent-driven Semantic Routing, where a UMLS-based NavigationAgent dynamically maps queries to medical dimensions for strategic search space pruning; and (2) Evidence-based Agentic Fusion, where a FusionAgent resolves conflicts among dimension-specific evidence to build logically consistent reasoning chains. Extensive experiments on five widely used medical benchmarks show that Med-SRAF consistently outperforms existing general RAG baselines, achieving an average accuracy improvement of over **4.9%**, highlighting its effectiveness in robust and interpretable medical reasoning. Our code is at https://anonymous.4open.science/r/MultiAgent_RAG-F6DC.
DANCE: Diversity-attended Dynamic Caching with Asymmetric Quantization for Test-time Adaptation of Vision-Language Models
PDF ↗This paper studies the problem of test-time adaptation for vision-language models (VLMs). Recent approaches typically measure the prediction entropy to store a confident cache for logit refinement. However, these confident samples tend to approach prototypes with limited coverage of data distribution, which could result in biased predictions as the distribution evolves. Towards this end, we propose a novel approach named Diversity-attended Dynamic Caching with Asymmetric Quantization (DANCE) for test-time adaptation of VLMs. The core of our DANCE is to maintain a dynamic cache to store diversity-aware test samples, which support efficient logit adjustment via asymmetric quantization. In particular, we first generate multiple augmented views of each sample and aggregate their outputs from pre-trained VLMs via a consistency-aware mechanism. More importantly, we construct a dynamic cache, which stores the most reliable and diverse samples to cover evolving test distributions. To measure the diversity efficiently, we quantize cached samples and compute the asymmetric similarity across query samples and memory samples, which guide the cache updating via replacing samples with the lowest scores iteratively. Finally, we leverage the asymmetric similarity between the quantized prototype representations from the dynamic cache to update logits under distribution shifts. Extensive experiments on various benchmark datasets validate the superiority of the proposed DANCE in different settings.
AI Agents for the Science of Science: A Survey of Tasks, Architectures, Evaluations, and Challenges
PDF ↗The Science of Science (SciSci) examines how scientific knowledge is generated, evaluated, and transformed by utilizing large-scale scholarly and bibliometric data. As these data grow in scale and complexity, analysis has increasingly relied on statistical, network-based, machine learning methods, and is now seeing growing involvement of AI agents. This emerging class of such agents, ranging from multi-agent simulations of scientific behavior to tool-augmented systems for empirical analysis, is beginning to reshape how SciSci research is conducted. In this survey, we propose a task-centered taxonomy, distinguishing *agents as simulations*, which model citation, collaboration, and community dynamics, from *agents as tools*, which assist empirical analysis and scientific workflows. We review agent architectures, learning mechanisms, evaluation, and SciSci benchmarks, and examine open challenges related to reliability, data quality, and bias. Our survey aims to clarify the landscape of AI agents in SciSci and to support the development of reliable and scientifically useful AI systems for studying science and scientific communities.
Large Language Models (LLMs) have demonstrated remarkable capabilities in code generation, but their proficiency in producing secure code remains a critical, under-explored area. Existing benchmarks often fall short by relying on synthetic vulnerabilities or evaluating functional correctness in isolation, failing to capture the complex interplay between functionality and security found in real-world software. To address this gap, we introduce RealSec-bench, a new benchmark for secure code generation meticulously constructed from real-world, high-risk Java repositories. Our methodology employs a multi-stage pipeline that combines systematic SAST scanning with CodeQL, LLM-based false positive elimination, and rigorous human expert validation. The resulting benchmark contains 105 instances grounded in real-word repository contexts, spanning 19 Common Weakness Enumeration (CWE) types and exhibiting a wide diversity of data flow complexities, including vulnerabilities with up to 34-hop inter-procedural dependencies. Using RealSec-bench, we conduct an extensive empirical study on 5 popular LLMs. We introduce a novel composite metric, SecurePass@K, to assess both functional correctness and security simultaneously. We find that while Retrieval-Augmented Generation (RAG) techniques can improve functional correctness, they provide negligible benefits to security. Furthermore, explicitly prompting models with general security guidelines often leads to compilation failures, harming functional correctness without reliably preventing vulnerabilities. Our work highlights the gap between functional and secure code generation in current LLMs. Our code and data are available at https://github.com/DeepSoftwareAnalytics/Realsec-code-Bench.
Large language models (LLMs) are transforming natural language processing across diverse linguistic communities. However, they can reproduce and amplify toxic content, including hate speech, harassment, and bias, posing significant risks to multilingual applications. We provide the first comprehensive survey of the many detoxification methods specifically tailored to multilingual LLMs. First, we define toxicity its measurement, then we provide a brief review of monolingual mitigation strategies, including data filtering, style transfer, expert-based logit steering, retrieval augmentation, and alignment with human feedback. We then present an in-depth taxonomy of multilingual approaches spanning (1) training methods, (2) post-hoc editing and decoding strategies, (3) alignment and reinforcement-learning techniques, and (4) data-centric innovations, such as parallel detox corpora and synthetic data generation. Finally, we discuss open challenges in multilingual detoxification, including data scarcity, evaluation inconsistencies, cultural nuances and biases. Overall, we produce a needed overview of the state of multi-lingual toxicity detection and mitigation on which the community can ground to build globally safe and equitable LLMs.
The generation of high-fidelity synthetic data is a cornerstone of modern machine learning, yet Large Language Models (LLMs) frequently suffer from hallucinations, logical inconsistencies, and mode collapse when tasked with structured generation. Existing approaches, such s prompting or retrieval-augmented generaon, lack the mechanisms to balance linguistic expressivity with formal guarantees regarding validity and coverage. To address this, we propose CircuitSynth, a novel neuro-symbolic framework that decouples semantic reasoning from surface realization. By distilling the reasoning capabilities of a Teacher LLM into a Probabilistic Sentential Decision Diagram (PSDD), CircuitSynth creates a tractable semantic prior that structurally enforces hard logical constraints. Furthermore, we introduce a convex optimization mechanism to rigorously satisfy soft distributional goals. Empirical evaluations across diverse benchmarks demonstrate that CircuitSynth achieves 100% Schema Validity even in complex logic puzzles where unconstrained baselines fail (12.4%) while significantly outperforming state-of-the-art methods in rare-combination coverage.
We present PictoEduca, the first large-scale Spanish text-to-pictogram dataset for augmentative and alternative communication (AAC), derived from primary educational materials and grounded in the ARASAAC pictogram repository. The dataset is released with a reproducible pipeline that combines automatic annotation with targeted expert correction, supporting scalable and high-quality corpus construction. We benchmark a rule-based system (ARAWORD) and neural models (T5, LLaMA) under direct text-to-pictogram and two-stage text-to-concept-to-pictogram settings. Results show that the rule-based system remains a strong baseline, while neural models benefit from explicit semantic abstraction, with the two-stage approach improving semantic coherence and reducing ambiguity. We further explore data selection strategies, demonstrating that combining domain similarity with a quality signal yields higher-quality silver data, reduces annotation effort, and improves model performance in low-resource regimes. PictoEduca enables reproducible evaluation and advances Spanish text-to-pictogram research.
Retrieval-Augmented Generation (RAG) provides external knowledge support for large language models (LLMs) in medical applications, but retrieved contexts often contain noisy or conflicting evidence that can degrade reasoning. We observe that when internal and external knowledge disagree, models systematically prefer external citations, inadvertently injecting retrieval noise. Our analyses further show that only a subset of retrieved citations consistently improves outcomes; these effective citations exhibit markedly lower token-level entropy, linking citation entropy to model accuracy. Building on these findings, we propose a complete pipeline consisting of a training-free multi-turn reasoning framework and a post-training methodology. The training-free framework elicits internal thought, external thought, and fusion thought, and applies conflict detection and explicit denoising for complex queries. For post-training, we distill structured supervised fine-tuning (SFT) data and apply GRPO with an entropy-based citation reward that encourages selective citation of beneficial external knowledge while penalizing noisy citations. Experiments across diverse benchmarks demonstrate consistent gains in noise-resistant medical reasoning, with larger improvements on harder cases.
Large Multimodal Models (LMMs) have achieved remarkable success in visual understanding, yet they struggle with knowledge-intensive queries involving long-tail entities or evolving information due to static parametric knowledge. Recent search-augmented approaches attempt to address this limitation, but existing methods rely on indiscriminate whole-image retrieval that introduces substantial visual redundancy and noise, and lack deep iterative reflection, limiting their effectiveness on complex visual queries. To overcome these challenges, we propose Glance-or-Gaze (GoG), a fully autonomous framework that shifts from passive perception to active visual planning. GoG introduces a Selective Gaze mechanism that dynamically chooses whether to glance at global context or gaze into high-value regions, filtering irrelevant information before retrieval. We design a dual-stage training strategy: Reflective GoG Behavior Alignment via supervised fine-tuning instills the fundamental GoG paradigm, while Complexity-Adaptive Reinforcement Learning further enhances the model’s capability to handle complex queries through iterative reasoning. Experiments across six benchmarks demonstrate state-of-the-art performance. Ablation studies confirm that both Selective Gaze and complexity-aware RL are essential for effective visual search. We will release our data and models for further exploration soon.
MedCoach: Enhancing Medical Reasoning in LLMs via Knowledge Graph-Augmented Chain-of-Thought Distillation
PDF ↗Despite the advanced capabilities of Large Language Models (LLMs), training specialized reasoning models for the medical domain remains a significant challenge due to the scarcity of high-quality, large-scale Chain-of-Thought (CoT) data. Moreover, the intermediate reasoning steps in teacher-generated CoT data can be redundant and noisy, leading models to acquire spurious patterns and resulting in suboptimal performance. To address these issues, we propose MedCoach, a novel framework that introduces a dedicated coach role to guide the student model through question decomposition, thereby smoothing its learning curve in medical reasoning. The framework employs a curriculum-oriented warm-up on simplified sub-questions, facilitating domain adaptation before advancing to complex long-chain reasoning. To ensure the fidelity of the intermediate chain-of-thought signals, we augment this phase with medical knowledge graphs to suppress factual drift and mitigate reasoning noise at a granular level.Subsequently, we introduce a targeted factual perturbation mechanism to foster fine-grained discrimination between valid fact utilization and subtle factual misapplications. Extensive experiments across diverse benchmarks demonstrate notable improvements over existing methods, validating the effectiveness of MedCoach.
High-quality mathematical and logical datasets with verifiable answers are essential for strengthening the reasoning capabilities of large language models (LLMs). While recent data augmentation techniques have facilitated the creation of large-scale benchmarks, existing LLM-generated datasets often suffer from limited reliability, diversity, and scalability. To address these challenges, we introduce PuzzleClone, a formal framework for synthesizing verifiable data at scale using a novel DSL-driven approach. Our approach features three key innovations: (1) encoding seed puzzles into structured logical specifications, (2) generating scalable variants through systematic variable and constraint randomization, and (3) ensuring validity via a reproduction mechanism. Applying PuzzleClone, we construct PC-83K, a benchmark comprising over 83K diverse and programmatically validated puzzles. The generated puzzles span a wide spectrum of difficulty and formats, posing significant challenges to current state-of-the-art models. Experimental results show that post training (SFT and RL) on PC-83K yields substantial improvements not only on the testset but also on various logic and mathematical benchmarks. Post training raises average performance on PC-83K from 14.5 to 66.0 and delivers consistent improvements across 7 logic and mathematical benchmarks up to 18.4 absolute percentage points (SATBench from 51.6 to 70.0). Our code and data are available at https://github.com/HiThink-Research/PuzzleClone.
Detecting missing foreign keys (FKs) requires accurately modeling semantic dependencies across database schemas, which conventional heuristic-based methods are fundamentally limited in capturing. We propose LLM-FK, the first fully automated multi-agent framework for FK detection, designed to address three core challenges that hinder naive LLM-based solutions in large-scale complex databases: combinatorial search space explosion, ambiguous inference under limited context, and global inconsistency arising from isolated local predictions. LLM-FK coordinates four specialized agents: a Profiler that decomposes the FK detection problem into the task of validating FK candidate column pairs and prunes the search space via a unique-key-driven schema decomposition strategy; an Interpreter that injects self-augmented domain knowledge; a Refiner that constructs compact structural representations and performs multi-perspective chain-of-thought reasoning; and a Verifier that enforces schema-wide consistency through a holistic conflict resolution strategy. Experiments on five benchmark datasets demonstrate that LLM-FK consistently achieves F1-scores above 93%, surpassing existing baselines by 15% on the large-scale MusicBrainz database, while reducing the candidate search space by two to three orders of magnitude without losing true FKs and maintaining robustness under challenging conditions like missing data. These results demonstrate the effectiveness and scalability of LLM-FK in real-world databases.
Despite significant advances in Large Reasoning Models (LRMs) driven by reinforcement learning with verifiable rewards (RLVR), this paradigm is fundamentally limited in specialized or novel domains where such supervision is prohibitively expensive or unavailable, posing a key challenge for test-time adaptation. While existing test-time methods offer a potential solution, they are constrained by learning from static query sets, risking overfitting to textual patterns. To address this gap, we introduce Test-Time Variational Synthesis (TTVS), a novel framework that enables LRMs to self-evolve by dynamically augmenting the training stream from unlabeled test queries. TTVS comprises two synergistic modules: (1) Online Variational Synthesis, which transforms static test queries into a dynamic stream of diverse, semantically-equivalent variations, enforcing the model to learn underlying problem logic rather than superficial patterns; (2) Test-time Hybrid Exploration, which balances accuracy-driven exploitation with consistency-driven exploration across synthetic variants. Extensive experiments show TTVS yields superior performance across eight model architectures. Notably, using only unlabeled test-time data, TTVS not only surpasses other test-time adaptation methods but also outperforms state-of-the-art supervised RL-based techniques trained on vast, high-quality labeled data.
Large language models (LLMs) have demonstrated that explicitly performing step-by-step thinking before producing final outputs can substantially improve performance on complex tasks, as exemplified by recent reasoning-oriented models such as OpenAI O1 and DeepSeek R1. Inspired by these advancements, we propose the O1 Embedder, a novel approach aiming to endow retrieval models with similar capabilities to address challenges like multi-task retrieval, zero-shot retrieval, and tasks requiring intensive reasoning of complex relationships. The O1 Embedder generates preliminary thoughts for input queries before document retrieval. To realize this objective, we address two fundamental challenges in integrating thinking mechanisms into dense retrieval. First, retrieval tasks lack explicit supervision for intermediate thinking processes, making it difficult to define thoughts that are truly useful for retrieval. We address this challenge with a data synthesis framework following an “Exploration-Refinement” process, ensuring alignment with retrieval utility. Second, effectively integrating thought generation with representation learning requires a unified modeling framework that can jointly support generation and embedding within a single model. O1 Embedder addresses this challenge by jointly optimizing thought generation and dense retrieval in an end-to-end manner, enhancing retrieval accuracy while reducing complexity through a single deployable model. Extensive evaluations across diverse datasets demonstrate significant performance improvements, highlighting the effectiveness and generalization capability of O1 Embedder.
Leveraging Label Semantics and Entity Description Generation for LLM-based Fine-grained Entity Typing
PDF ↗Fine-grained entity typing (FET) aims to assign semantically rich and contextually appropriate types to entity mentions. While recent studies have explored the use of large language models (LLMs) for this task, two key challenges persist. First, FET typically involves a large number of entity types, making it difficult for LLMs to perform accurate classification. Second, the presence of label noise in the training data introduced by automatic supervision methods hinders effective fine-tuning. To address these challenges, we propose DR-FET, a descriptor-based retrieval-augmented framework that reduces the effective label space and constructs high-precision training data under noisy supervision. Our method introduces natural language descriptors as an intermediate semantic representation for both entity mentions and types. During inference, entity descriptors are used to retrieve a small set of semantically relevant candidate types, enabling the LLM to perform fine-grained classification under explicit candidate constraints. During training, the same descriptor and retrieval mechanism is reused to identify high-confidence instances from distantly supervised data, prioritizing label precision for efficient fine-tuning. Experiments on two widely used benchmarks demonstrate that the proposed method consistently outperforms existing fine-grained entity typing approaches under noisy supervision.
Retrieval-Augmented Generation (RAG) systems often fail to maintain contextual faithfulness, generating responses that conflict with the provided context. Existing methods attempt to improve faithfulness through external interventions, such as specialized prompting, decoding-based calibration, or preference optimization. However, since these approaches treat the LLM as a black box, they lack a reliable mechanism to assess how these conflicts occur. Consequently, they tend to be brittle, data-intensive, and agnostic to the model’s internal reasoning process. In this paper, we move beyond black-box interventions to analyze the model’s internal reasoning process. We discover that conflicting and aligned knowledge states are linearly separable in the model’s latent space, and contextual noise systematically increases the entropy of these representations. Based on these findings, we propose ProbeRAG, a novel framework for faithful RAG that operates in three stages: (i) fine-grained knowledge pruning to filter irrelevant context, (ii) latent conflict probing to identify hard conflicts in the model’s latent space, and (iii) conflict-aware attention to modulate attention heads toward faithful context integration. Extensive experiments demonstrate that ProbeRAG substantially improves both accuracy and contextual faithfulness. The related resources are available at https://github.com/XMUDeepLIT/ProbeRAG.
Cognitive-Uncertainty Guided Knowledge Distillation for Accurate Classification of Student Misconceptions
PDF ↗Accurately identifying student misconceptions is crucial for personalized education but faces three challenges: (1) data scarcity with long-tail distribution, where authentic student reasoning is difficult to synthesize; (2) fuzzy boundaries between error categories with high annotation noise; (3) deployment paradox—large models overlook unconventional approaches due to pretraining bias and cannot be deployed on edge, while small models overfit to noise. Unlike traditional methods that increase diversity through large-scale data synthesis, we propose a two-stage knowledge distillation framework that mines high-value samples from existing data. The first stage performs standard distillation to transfer task capabilities. The second stage introduces a dual-layer marginal selection mechanism based on cognitive uncertainty, identifying four types of critical samples based on teacher model uncertainty and confidence differences. For different data subsets, we design difficulty-adaptive mechanism to balance hard/soft label contributions, enabling student models to inherit inter-class relationships from teacher soft labels while distinguishing ambiguous error types. Experiments show that with augmented training on only 10.30% of filtered samples, we achieve MAP@3 of 0.9585 (+17.8%) on the MAP-Charting dataset, and using only a 4B parameter model, we attain 84.38% accuracy on cross-topic tests of middle school algebra misconception benchmarks, significantly outperforming sota LLM (67.73%) and standard fine-tuned 72B models (81.25%). Our code is available at https://anonymous.4open.science/r/acl2026_map-5847/.
IREASONER: Trajectory-Aware Intrinsic Reasoning Supervision for Self-Evolving Large Multimodal Models
PDF ↗Recent work shows that large multimodal models (LMMs) can self-improve from unlabeled data via self-play and intrinsic feedback. Yet existing self-evolving frameworks mainly reward final outcomes, leaving intermediate reasoning weakly constrained despite its importance for visually grounded decision making. We propose IREASONER, a self-evolving framework that improves an LMM’s implicit reasoning by explicitly eliciting chain-of-thought (CoT) and rewarding its internal agreement. In a Proposer–Solver loop over unlabeled images, IREASONER augments outcome-level intrinsic rewards with a trajectory-aware signal defined over intermediate reasoning steps, providing learning signals that distinguish reasoning paths leading to the same answer without ground-truth labels or external judges. Starting from Qwen2.5-VL-7B, IREASONER yields up to +2.1 points across diverse multimodal reasoning benchmarks under fully unsupervised post-training. We hope this work serves as a starting point for reasoning-aware self-improvement in LMMs in purely unsupervised settings.