💬 ACL2026 Accepted Papers

1416 ACL2026 paper notes covering LLM Safety (115), LLM Evaluation (97), LLM Agent (82), LLM Reasoning (82), Multimodal VLM (82), Information Retrieval & RAG (73), Audio & Speech (71), Multilingual & Translation (64) and other 38 areas. Each note has TL;DR, motivation, method, experiments, highlights, and limitations — 5-minute reads of core ideas.

---

💡 LLM Reasoning (82)

Logical Phase Transitions: Understanding Collapse in LLM Logical Reasoning

This paper discovers the "logical phase transition" phenomenon in LLM logical reasoning—performance collapses abruptly at specific complexity thresholds rather than degrading smoothly. It proposes the Logical Complexity Measure (LoCM) to quantify this phenomenon and designs the Neuro-Symbolic Curriculum Tuning (NSCT) framework. Through adaptive neuro-symbolic alignment and complexity-aware curriculum optimization, NSCT improves accuracy by an average of +1.26 over naive prompting and +3.95 over CoT across five benchmarks.

Semantic-Aware Logical Reasoning via a Semiotic Framework

Proposes LogicAgent, a logical reasoning framework based on the Greimas Semiotic Square, achieving SOTA logical reasoning performance under the dual challenges of semantic and logical complexity through multi-perspective semantic analysis and reflective verification.

DRP: Distilled Reasoning Pruning with Skill-aware Step Decomposition for Efficient Large Reasoning Models

DRP allows a "Short-CoT teacher (GPT-4o)" to perform skill-level step decomposition and pruning/rewriting on the "Long-CoT student's (R1-Distill-Qwen)" own reasoning trajectories. By distilling these trajectories—which remove redundancy while preserving the student's speaking style—back into the student, DRP reduces the tokens of a 7B model on GSM8K from 917 to 328 (−64%) while increasing Pass@1 from 91.7% to 94.1%. It simultaneously reduces token counts and improves accuracy on OOD tasks like AIME/AMC/MATH500.

TemplateRL: Structured Template-Guided Reinforcement Learning for LLM Reasoning

TemplateRL abstracts structured reasoning templates from a small seed set using MCTS and introduces these templates as explicit guidance during reinforcement learning training. This significantly improves the efficiency and stability of multi-step reasoning in LLMs, achieving a 99% improvement over GRPO on AIME.

Is Chain-of-Thought Really Not Explainability? Chain-of-Thought Can Be Faithful without Hint Verbalization

This paper systematically refutes the popular recent conclusion that "CoT does not count as explainability." Using four complementary metrics—Filler Tokens, FUR, faithful@k, and Causal Mediation Analysis—it demonstrates that over half of CoT samples judged "unfaithful" by Biasing Features (hint verbalization) actually reflect model reasoning "in other ways." Unfaithfulness primarily stems from "incompleteness" due to lossy natural language compression rather than true divergence—increasing the sampling budget can raise hint verbalization probability to 90%, and even non-verbalized hints can causally transmit influence through the CoT.

Render-of-Thought: Rendering Textual Chain-of-Thought as Images for Visual Latent Reasoning

Ours proposes Render-of-Thought (RoT), the first to render textual CoT reasoning steps into images. By utilizing a pre-trained visual encoder as a semantic anchor to align LLM hidden states with the visual embedding space, it achieves 3-4x token compression and significant inference acceleration while maintaining the analyzability of reasoning chains.

LLM Reasoning as Trajectories: Step-Specific Representation Geometry and Correctness Signals

This paper models LLM chain-of-thought reasoning as geometric trajectories in the representation space. It discovers that (a) each reasoning step occupies a linearly separable subspace that becomes clearer in deeper layers, and (b) correct and incorrect solutions overlap in early stages but diverge systematically later. This allows predicting the final correctness with an ROC-AUC of 0.87 before the answer is output, leading to a proposed "trajectory steering" method for reasoning correction and length control.

Revisiting Entropy in Reinforcement Learning for Large Reasoning Models

This work systematically investigates the entropy dynamics of LLMs during RLVR training, revealing that positive-advantage tokens are the primary drivers of entropy collapse. It introduces Positive-Advantage Reweighting to effectively regulate model entropy by dynamically adjusting the loss weights of these tokens.

MTR-Bench: A Comprehensive Benchmark for Multi-Turn Reasoning Evaluation

MTR-Bench constructs an automated multi-turn reasoning evaluation framework comprising 4 categories, 40 tasks, and 3,600 instances, demonstrating that current frontier reasoning models remain unreliable in interactive and dynamic feedback environments.

Parallel Test-Time Scaling for Latent Reasoning Models

This paper introduces parallel test-time scaling (parallel TTS) to latent reasoning models for the first time. It proposes two stochastic sampling strategies based on uncertainty theory (MC-Dropout and Additive Gaussian Noise) and a Latent Reward Model (LatentRM) trained with step-level contrastive learning. This enables models reasoning in continuous vector spaces to achieve stable performance gains through parallel sampling and aggregation.

Browse all 82 LLM Reasoning papers →

---

🦾 LLM Agent (82)

OctoTools: An Agentic Framework with Extensible Tools for Complex Reasoning

OctoTools is a training-free, user-friendly, and extensible multi-agent framework. By utilizing standardized Tool Cards to encapsulate heterogeneous tools, a Planner-Executor separation paradigm, and a task-specific toolset optimization algorithm, it achieves an average accuracy improvement of +9.3% over GPT-4o and up to +10.6% over frameworks like AutoGen and LangChain across 16 diverse benchmarks.

Mem^p: Exploring Agent Procedural Memory

This paper proposes the Mem^p framework to systematically study how to build learnable, updatable, and lifelong evolving procedural memory for LLM Agents. By distilling past task trajectories into fine-grained step-by-step instructions and high-level script abstractions, combined with a dynamic update mechanism (addition/validation/reflection/elimination), the authors achieve continuous success rate improvements and significant reductions in execution steps on TravelPlanner and ALFWorld.

MAGMA: A Multi-Graph based Agentic Memory Architecture for AI Agents

MAGMA decouples the memory of LLM agents into four orthogonal relation graphs: semantic, temporal, causal, and entity. It employs intent routing and adaptive beam search for policy-guided traversal across the appropriate graphs, complemented by a dual-stream writing mechanism ("Fast Path" for synchronous ingestion and "Slow Path" for asynchronous LLM consolidation). On LoCoMo, it achieves a Judge score of 0.700, comprehensively outperforming A-MEM, Nemori, and MemoryOS, while maintaining a query latency of only 1.47s (40% faster than the runner-up).

OPeRA: A Dataset of Observation, Persona, Rationale, and Action for Evaluating LLMs on Human Online Shopping Behavior Simulation

OPeRA is a user behavior dataset collected from real Amazon shopping processes. It aligns personas, web observations, fine-grained actions, and real-time rationales on the same timeline to evaluate whether LLMs can truly simulate a specific user's next shopping behavior.

ExpSeek: Self-Triggered Experience Seeking for Web Agents

ExpSeek proposes a proactive experience-seeking framework based on step-level entropy self-triggering, allowing Web Agents to determine when and what guidance is needed based on internal signals during interaction. It achieves absolute improvements of 9.3% and 7.5% on Qwen3-8B/32B respectively.

From Storage to Experience: A Survey on the Evolution of LLM Agent Memory Mechanisms

This paper provides a systematic survey of LLM Agent memory mechanisms using an evolutionary framework of "Storage → Reflection → Experience." It utilizes formal definitions to map these three stages to three functional signatures: "Trajectory Retention → Trajectory Refinement → Cross-Trajectory Abstraction." The storyline is structured around three RQs (Why-How-What), with a deep dive into two transformative mechanisms of the Experience stage: Active Exploration and Cross-Trajectory Abstraction.

MemSearcher: Training LLMs to Reason, Search and Manage Memory via End-to-End RL

MemSearcher replaces the "history concatenation" of search agents with "LLM-managed compact memory"—where only the (question, memory) is processed each round instead of (question, t₁, a₁, o₁, …). Utilizing multi-context GRPO, it propagates the advantage of the entire trajectory to each round for independent optimization. MemSearcher outperforms same-sized ReAct baselines across 3B/7B/14B scales on 7 QA benchmarks (the 7B model even surpasses the 32B ReSearch) while maintaining a constant context length of <4K tokens.

AgencyBench: Benchmarking the Frontiers of Autonomous Agents in 1M-Token Real-World Contexts

AgencyBench is proposed as a comprehensive benchmark comprising 138 real-world tasks to evaluate 6 core agent capabilities. Each scenario averages 90 tool calls and 1 million tokens, achieving fully automated evaluation via user simulation agents and Docker sandboxes.

TiMem: Temporal-Hierarchical Memory Consolidation for Long-Horizon Conversational Agents

TiMem organizes long-range conversational memory into a five-layer Temporal Memory Tree with explicit temporal containment. By employing complexity-aware retrieval to dynamically balance fine-grained facts and high-level personas, it improves accuracy on LoCoMo and LongMemEval-S while significantly reducing recalled context length.

Verified Critical Step Optimization for LLM Agents

CSO identifies "verified critical steps" from an agent's own failed trajectories where "changing a single action leads to task success." It constructs DPO preference pairs only at these critical decision points, enhancing the post-training performance of long-horizon LLM agents with fewer and more reliable supervisory signals.

Browse all 82 LLM Agent papers →

---

👥 Multi-Agent (40)

LLM-Based Human-Agent Collaboration and Interaction Systems: A Survey

This paper provides the first systematic review of "LLM-based Human-Agent Collaboration and Interaction Systems (LLM-HAS)"—reintegrating humans into the agent loop. It establishes a unified taxonomy across five dimensions (Environment/Profiling, Human Feedback, Interaction Type, Orchestration, and Communication) and introduces a Human Agency Scale (A1–A5) to quantify the necessary depth of human involvement in tasks.

AutoReproduce: Automatic AI Experiment Reproduction with Paper Lineage

AutoReproduce proposes a multi-agent framework that utilizes a "Paper Lineage" algorithm to mine implicit domain knowledge from referenced literature. This enables end-to-end automatic reproduction of paper experiments, achieving a code execution rate of 94.87% and a performance gap of only 19.72% on the self-constructed ReproduceBench.

Seeing the Whole Elephant: A Benchmark for Failure Attribution in LLM-based Multi-Agent Systems

TraceElephant advocates that failure attribution in multi-agent systems should be evaluated under full execution traces visible to developers. It provides 220 failed traces with annotations for responsible agents and critical failure steps, demonstrating that full observability improves step-level attribution from 16% (output-only) to over 28%-30%.

When Identity Skews Debate: Anonymization for Bias-Reduced Multi-Agent Reasoning

This paper points out that LLMs in multi-agent debates change their stances based on "who said it" rather than "what was said," and quantifies and mitigates this identity-driven bias through response anonymization and the Identity Bias Coefficient (IBC).

Collaborative Multi-Agent Scripts Generation for Enhancing Imperfect-Information Reasoning in Murder Mystery Games

Ours proposes a collaborative multi-agent framework for the automated generation of high-quality Murder Mystery game scripts and training data. Through a two-stage training strategy (CoT fine-tuning + GRPO reinforcement learning with ScoreAgent reward shaping), the multi-hop reasoning capability of VLMs under imperfect information is enhanced. This significantly improves VLM narrative reasoning, fact extraction, and deception resistance on WhodunitBench.

SILO-BENCH: A Scalable Environment for Evaluating Distributed Coordination in Multi-Agent LLM Systems

This paper proposes SILO-BENCH, a role-agnostic benchmark for evaluating distributed coordination in multi-agent LLM systems. Comprising 30 algorithmic tasks across three communication complexity levels and 1620 experiments over 54 configurations, it reveals a critical "communication-reasoning gap": while agents can spontaneously form rational communication topologies and actively exchange information, they systematically fail to integrate distributed states into correct answers.

PosterForest: Hierarchical Multi-Agent Collaboration for Scientific Poster Generation

PosterForest utilizes a Poster Tree, which simultaneously encodes the hierarchical semantics of a paper and the spatial layout of a poster, as an intermediate representation. It employs recursive collaborative optimization between Content, Layout, and Feedback agents to generate scientific posters in a training-free manner. In human evaluations, it achieved a 59.2% overall preference, significantly outperforming P2P and Paper2Poster.

AgenticEval: Toward Agentic and Self-Evolving Safety Evaluation of Large Language Models

AgenticEval redefines LLM safety evaluation as a "continuous, self-evolving red-teaming process": the Specialist decomposes unstructured regulatory text into an atomic rule knowledge base; the Generator creates multimodal and multi-format Question Groups centered around each rule; the Evaluator + Analyst continuously transform failures from the current round into more aggressive attack strategies for the next. After three iterations, the compliance rate of GPT-5 under the EU AI Act plummeted from 72.50% to 36.36%, revealing that static benchmarks significantly overestimate the safety levels of large models.

ATLAS: Adaptive Trading with LLM AgentS Through Dynamic Prompt Optimization and Multi-Agent Coordination

This paper proposes the ATLAS multi-agent financial trading framework and the Adaptive-OPRO prompt optimization method. By utilizing specialized analyst agents to prepare heterogeneous market information and dynamically optimizing the instruction prompts of the central trading agent based on delayed noisy feedback, the system significantly outperforms baselines across diverse volatile market environments.

ConSensus: Multi-Agent Collaboration for Multimodal Sensing

ConSensus is a training-free multi-agent sensor fusion framework that assigns specialized agents to independently interpret different sensing modalities. By utilizing semantic fusion, statistical consensus, and hybrid arbitration, it achieves an average 7.1% accuracy improvement over single-agent methods across five multimodal sensing benchmarks, while reducing fusion token costs to approximately 1/12.7 of multi-round debate methods.

Browse all 40 Multi-Agent papers →

---

⚖️ Alignment & RLHF (38)

WildFeedback: Aligning LLMs With In-situ User Interactions And Feedback

WildFeedback automatically identifies satisfied/dissatisfied feedback from real multi-turn ChatGPT conversations. It transforms naturally occurring user preferences into preference training samples and instance-specific checklist evaluation standards. This enables small open-source instruction models to align more closely with real user needs than those trained on UltraFeedback, both on general benchmarks and in real-world user preference tests.

AgentV-RL: Scaling Reward Modeling with Agentic Verifier

The reward model is reshaped from a "single-turn scoring" mechanism into a multi-turn deliberation process featuring "forward + backward dual agents + tool calls." Through SFT+GRPO, these multi-agent capabilities are distilled into a single 4B model, which outperforms 70B-scale ORMs by 25.2% in Best-of-N (BoN) selection.

RbtAct: Rebuttal as Supervision for Actionable Review Feedback Generation

RbtAct treats author rebuttals as implicit supervision for "which review comments actually prompt modifications," constructs a dataset of 75,000 review-rebuttal segment-level mappings, and employs SFT+DPO to train an 8B model to generate more specific and actionable paper review feedback.

Why Supervised Fine-Tuning Fails to Learn: A Systematic Study of Incomplete Learning in Large Language Models

This paper provides the first systematic study of "Incomplete Learning Phenomenon" (ILP) in SFT—where models fail to correctly reproduce part of the training data despite convergence. It identifies five recurring causes (Knowledge Absence, Knowledge Conflict, Internal Data Contradiction, Left-side Forgetting, Insufficient Optimization) and proposes a diagnostic framework along with targeted mitigation strategies.

Aligning Agents via Planning: A Benchmark for Trajectory-Level Reward Modeling

Plan-RewardBench is proposed as a trajectory-level preference benchmark for complex tool-augmented scenarios, designed to evaluate the capability of reward models in distinguishing superior from inferior agent trajectories across multi-step planning, tool usage, and error recovery.

ModeX: Evaluator-Free Best-of-N Selection for Open-Ended Generation

ModeX models Best-of-N selection for open-ended text generation as a problem of "finding modal clusters on a generated text similarity graph." By using n-gram Jaccard graph construction, recursive spectral clustering with Fiedler vectors, and centrality-based centroid selection, it generalizes self-consistency to tasks without standard answers (e.g., summarization, code, math) without requiring any reward models or LLM-judges.

SFTMix: Elevating Language Model Instruction Tuning with Mixup Recipe

This paper proposes SFTMix, a Mixup-based instruction tuning method. By partitioning SFT datasets into high-confidence and low-confidence subsets through training dynamics, it performs linear interpolation in the hidden representation space and applies Mixup regularization. SFTMix consistently improves instruction-following capabilities across different LLM families and dataset scales without relying on high-quality dataset curation.

P-Check: Advancing Personalized Reward Model via Learning to Generate Dynamic Checklist

P-Check transforms personalized reward modeling from "cramming user history into the judge" to "first generating a weighted dynamic evaluation checklist for the current user and current query, then using it to guide reward scoring." It significantly outperforms persona, memory retrieval, and fine-tuned reward model baselines on personalized preference prediction and downstream generation tasks in PRISM, Arena, and BESPOKE.

PERSA: Reinforcement Learning for Professor-Style Personalized Feedback with LLMs

PERSA utilizes "professor demonstrations + professor preference rewards + PPO updating only high-level LoRA" to tune general LLMs into specific teacher programming feedback styles. It significantly improves style consistency across APPS, PyFiXV, and CodeReviewQA while maintaining nearly 100% diagnostic accuracy.

Alignment Data Map for Efficient Preference Data Selection and Diagnosis

This paper proposes the Alignment Data Map, an analytical tool that visualizes, selects, and diagnoses preference data by jointly considering response quality and variability. It achieves the alignment performance of full-set training using only 33% of the data.

Browse all 38 Alignment & RLHF papers →

---

🔒 LLM Safety (115)

SafeMERGE: Preserving Safety Alignment in Fine-Tuned Large Language Models via Selective Layer-Wise Model Merging

Ours proposes SafeMERGE, a lightweight post-fine-tuning framework that identifies fine-tuned layers deviating from safe behavior via cosine similarity and merges only these layers with corresponding layers of a safety model. This significantly reduces harmful outputs across four LLMs while maintaining or even improving task performance.

A Survey on the Safety and Security Threats of Computer-Using Agents: JARVIS or Ultron?

This paper provides the first systematic review of safety research for "Computer-Using Agents (CUA)," organizing 124 relevant papers into a four-dimensional framework of "Internal Threats × External Threats × Defense × Evaluation," and highlighting that the primary gaps in existing CUAs are UI grounding robustness and cross-platform adversarial evaluation.

How Should We Enhance the Safety of Large Reasoning Models: An Empirical Study

This paper systematically investigates how to enhance the safety of Large Reasoning Models (LRMs) through SFT. It identifies that the root cause of the limited effectiveness of direct safety response distillation is five risk reasoning patterns (especially "weak vacillation"). The authors propose targeted distillation strategies that reduce the PAIR attack success rate from 63% to 13%, and find that short reasoning chains and template reasoning perform comparably to long reasoning chains in terms of safety.

Robustness via Referencing: Defending against Prompt Injection Attacks by Referencing the Executed Instruction

This paper proposes a defense method against prompt injection based on instruction referencing. Instead of suppressing the instruction-following capability of LLMs, it requires the model to reference the instruction currently being executed within its response. Responses unrelated to the original instruction are then removed through label filtering, reducing the Attack Success Rate (ASR) to nearly 0% in certain scenarios.

Understanding and Mitigating Bias Inheritance in LLM-based Data Augmentation on Downstream Tasks

This paper systematically investigates how biased augmented data generated by LLMs is inherited and amplified during supervised fine-tuning (SFT), impacting downstream tasks. Using six types of bias generation frameworks across ten tasks and three categories of mitigation methods, it reveals the complex phenomenon that "more synthetic data does not necessarily mean higher safety."

Lying with Truths: Open-Channel Multi-Agent Collusion for Belief Manipulation via Generative Montage

This paper identifies the security threat of cognitive collusion: multiple agents can publicly release only truthful but narratively orchestrated evidence fragments to induce false causal beliefs in a victim LLM agent, which then continues to propagate through downstream verification layers.

Rethinking Jailbreak Detection of Large Vision Language Models with Representational Contrastive Scoring

This paper proposes the Representational Contrastive Scoring (RCS) framework, which achieves SOTA jailbreak detection performance under rigorous cross-attack evaluation protocols. By analyzing the geometric structure of internal intermediate layer representations in LVLMs, RCS utilizes lightweight projection and contrastive scoring to distinguish malicious intent from benign distribution shifts.

Topic-Based Watermarks for Large Language Models

This paper proposes TBW, a lightweight topic-based watermarking scheme that clusters the vocabulary into "green lists" based on semantic topics rather than random partitioning. By selecting a semantically aligned topic list for logit biasing based on the input prompt, it maintains perplexity comparable to unwatermarked text while significantly enhancing robustness against paraphrasing and lexical perturbation attacks.

Instant Personalized Large Language Model Adaptation via Hypernetwork

Profile-to-PEFT (P2P) utilizes a hypernetwork to directly map user profiles to personalized LoRA parameters. This avoids the need for OPPU to retrain adapters for each user, achieving faster, more scalable LLM personalization that generalizes to unseen users.

PIArena: A Platform for Prompt Injection Evaluation

This paper proposes PIArena, a unified and extensible evaluation platform for Prompt Injection. It integrates multiple SOTA attack and defense methods, supports plug-and-play evaluation, and introduces a policy-based adaptive attack method. It systematically reveals key limitations of existing defenses in terms of generalization, adaptive attacks, and task alignment scenarios.

Browse all 115 LLM Safety papers →

---

👻 Hallucination Detection (28)

Hallucination Detection in LLMs with Topological Divergence on Attention Graphs

TOHA treats the LLM attention matrix as a weighted graph, utilizes Manifold Topology Divergence from topological data analysis (TDA) to measure the "topological novelty of the response subgraph relative to the prompt subgraph," and discovers "hallucination-aware heads" that are stable across datasets. Averaging only 10 such heads achieves a training-free solution in RAG scenarios that is 70× faster than SelfCheckGPT with significantly leading ROC-AUC.

The Reasoning Trap: How Enhancing LLM Reasoning Amplifies Tool Hallucination

This work systematically reveals the "Reasoning Trap" paradox: enhancing LLM reasoning capabilities (whether through RL, distillation, or switchable reasoning modes) systematically amplifies tool hallucinations. This effect is inherently associated with the reasoning process itself rather than RL training, and existing mitigation strategies (Prompt Engineering, DPO) face an inevitable reliability-capability trade-off.

Through the Magnifying Glass: Adaptive Perception Magnification for Hallucination-Free VLM Decoding

This paper proposes Perception Magnifier (PM), a visual decoding method that iteratively identifies key visual regions using multi-layer attention during each autoregressive decoding step and adaptively magnifies them. By enhancing the effective resolution of key regions, it mitigates VLM visual hallucinations while maintaining spatial structural integrity and reasoning capabilities.

Mechanisms of Prompt-Induced Hallucination in Vision–Language Models

In controlled object counting tasks, prompt-induced hallucination (PIH)—where the model follows the prompt rather than the image—is localized to 3–10 attention heads in the early layers (\(L0-1\)) of LLaVA-OneVision, Qwen-VL, and Janus-Pro. Mean ablation of these heads, requiring no retraining, reduces prompt-following from 42–64% to <11% and restores true counting rates to 70–78%, while zero-shot transferring to color identification (PIH suppression of 40–95%).

FaithLens: Detecting and Explaining Faithfulness Hallucination

This paper proposes FaithLens, an 8B parameter faithfulness hallucination detection model. It undergoes cold-start SFT using high-quality data synthesis combined with three-dimensional filtering (label correctness, explanation quality, and data diversity), followed by further optimization via rule-based reinforcement learning (prediction correctness reward + explanation quality reward). It surpasses GPT-5.2 and o3 across 12 tasks while providing high-quality explanatory outputs.

HalluAudio: A Comprehensive Benchmark for Hallucination Detection in Large Audio-Language Models

This paper introduces HalluAudio, the first large-scale cross-domain (speech/ambient/music) benchmark for audio hallucination detection. It features 5,000+ human-verified QA pairs and systematic adversarial prompt designs. By evaluating mainstream LALMs using multi-dimensional metrics (Accuracy, Hallucination Rate, Yes-No Bias, Refusal Rate, and Error Types), the study reveals significant deficiencies in current models regarding acoustic anchoring, temporal reasoning, and music attribute understanding.

Stable-RAG: Mitigating Retrieval-Permutation-Induced Hallucinations in Retrieval-Augmented Generation

This work reveals the high sensitivity of RAG systems to the permutation order of retrieved documents and proposes Stable-RAG. By applying spectral clustering to hidden states generated by document permutations to identify dominant reasoning patterns, and subsequently employing DPO alignment to guide hallucinatory outputs toward correct answers, Stable-RAG achieves dual improvements in accuracy and reasoning consistency across three QA datasets.

FinGround: Detecting and Grounding Financial Hallucinations via Atomic Claim Verification

FinGround is a three-stage "verify-then-ground" pipeline for financial document QA: (1) finance-aware hybrid retrieval; (2) decomposing answers into atomic claims and verifying them using a type-routed strategy across a six-category taxonomy (Numerical, Temporal, Entity Property, Comparative, Regulatory, Computational—where computational claims use formula reconstruction and arithmetic re-verification); (3) grounded rewriting of unsupported claims with paragraph/cell-level citations. By distilling GPT-4o into an 8B detector, it achieves a 91.4% F1 score with 18× acceleration, reducing the hallucination rate by 78% compared to GPT-4o+CoT.

Mitigating Hallucinations in Large Vision-Language Models without Performance Degradation

This paper proposes the MPD framework, which decouples hallucination components through semantic-aware orthogonal subspace projection and selectively updates a small number of parameters most relevant to hallucinations. While reducing hallucinations by 23.4%, it maintains 97.4% of general generation capability without introducing additional inference overhead.

Two Pathways to Truthfulness: On the Intrinsic Encoding of LLM Hallucinations

This paper discovers that LLMs encode truthfulness signals through two distinct information pathways: Question-Anchored (dependent on information flow from question to answer) and Answer-Anchored (extracting self-contained evidence from the generated answer itself). These pathways are closely linked to knowledge boundaries. Based on this, the authors propose Mixture-of-Probes and Pathway Reweighting, achieving up to a 10% improvement in AUC for hallucination detection.

Browse all 28 Hallucination Detection papers →

---

📊 LLM Evaluation (97)

Challenging the Boundaries of Reasoning: An Olympiad-Level Math Benchmark for Large Language Models

OlymMATH is proposed as the first Olympiad-level mathematical benchmark that unifies natural language evaluation and formal theorem proving. It contains 350 bilingual (Chinese and English) problems, covering OlymMATH-EASY/HARD (200 problems with numerical answers) and OlymMATH-LEAN (150 Lean 4 formalized problems), revealing that the strongest models achieve only 58.4% accuracy on the HARD subset.

ResearchBench: Benchmarking LLMs in Scientific Discovery via Inspiration-Based Task Decomposition

ResearchBench is proposed as the first large-scale benchmark to evaluate the scientific discovery capabilities of LLMs. Based on the theoretical decomposition of "inspiration-driven hypothesis generation," it covers 1386 papers across 12 disciplines. By decomposing scientific discovery into three sufficient subtasks—inspiration retrieval, hypothesis composition, and hypothesis ranking—the study finds that LLMs perform exceptionally well in cross-disciplinary inspiration retrieval.

Attribution, Citation, and Quotation: A Survey of Evidence-based Text Generation with Large Language Models

This paper systematically reviews 134 papers on evidence-based text generation for LLMs. It proposes the first unified taxonomy (Attribution Mechanism × Citation Features × Task), analyzes 300 evaluation metrics categorized into seven dimensions and six methods, and provides a panoramic reference framework for this fragmented field.

EngiBench: A Benchmark for Evaluating Large Language Models on Engineering Problem Solving

This paper proposes EngiBench—the first multi-level LLM evaluation benchmark for real-world engineering problem solving. Tasks are organized into three difficulty levels (Basic Knowledge Retrieval → Contextual Reasoning → Open-ended Modeling) and accompanied by three controlled variants (Perturbation / Knowledge Enhancement / Math Abstraction). Covering 1,760 problems across three engineering sub-domains, it reveals that even GPT-4.1 and Claude 3.7 Sonnet lag significantly behind human experts on Level 3 open-ended engineering tasks.

WildIFEval: Instruction Following in the Wild

WildIFEval is a single-turn constraint generation benchmark extracted from real-world user conversations, comprising 7,523 tasks and 24,731 constraints. It automatically decomposes each user instruction into fine-grained constraints categorized into 8 major classes and employs an LLM-as-judge for "strict/soft" dual scoring. This work characterizes the distribution and co-occurrence of constraints in real-world instructions for the first time and reveals a capacity bottleneck where the overall success rate drops sharply as the number of constraints increases, while the success rate per individual constraint remains nearly unchanged.

Can We Predict Before Executing Machine Learning Agents?

This paper demonstrates that LLMs can serve as implicit "world models" to predict the quality of ML solutions based solely on task descriptions, verified data reports, and code snippets (DeepSeek-V3.2-Thinking achieves 61.5% accuracy). Based on this, the authors develop ForeAgent, which transforms the traditional "Generate-Execute-Feedback" loop of AIDE into a "Predict-then-Verify" loop, achieving a 6× speedup, 3.2× expanded search space, and a +6% Beat Ratio on MLE-Bench.

Finch: Benchmarking Finance & Accounting across Spreadsheet-Centric Enterprise Workflows

This paper introduces Finch (FinWorkBench), a benchmark for financial and accounting (F&A) workflows constructed from authentic enterprise environments (e.g., the Enron dataset). It comprises 172 composite workflows and 1,710 spreadsheets (27 million cells). Even the most advanced Agent, GPT 5.1 Pro, achieves only a 38.4% success rate despite an average execution time of 16.8 minutes, highlighting significant deficiencies of state-of-the-art AI Agents in real-world corporate scenarios.

NovBench: Evaluating Large Language Models on Academic Paper Novelty Assessment

NovBench pairs "novelty claims in paper introductions" with "textual novelty evaluations from reviewers" to create a benchmark of 1,684 samples. Using four dimensions—Relevance, Correctness, Coverage, and Clarity—it systematically reveals that while current general-purpose and specialized LLMs can generate fluent evaluations, they still struggle to truly understand and comprehensively judge academic novelty.

arXiv2Table: Toward Realistic Benchmarking and Evaluation for LLM-Based Literature-Review Table Generation

The authors present the arXiv2Table benchmark (1,957 tables, 7,158 papers), which achieves a more realistic evaluation of LLM-based literature-review table generation by introducing distractor papers, schema-agnostic user demands, and a QA-based reference-free evaluation framework, alongside an iterative batch generation method.

BadScientist: Can a Research Agent Write Convincing but Unsound Papers that Fool LLM Reviewers?

The authors developed a "BadScientist" pipeline: a generation agent that conducts no real experiments uses five "performative fraud" strategies to write seemingly rigorous but fundamentally unsound papers. These are then fed to a multi-model reviewer agent composed of o3 / o4-mini / GPT-4.1. Results show that the acceptance rate for fraudulent papers reaches up to 82%. Furthermore, reviewers often point out integrity issues in their text comments while still assigning acceptance scores (concern-acceptance conflict), and existing mitigation methods perform barely better than random guessing.

Browse all 97 LLM Evaluation papers →

---

⚡ LLM Efficiency (23)

Saber: Efficient Sampling with Adaptive Acceleration and Backtracking Enhanced Remasking for DLMs

This paper proposes Saber, a training-free sampling algorithm for Diffusion Language Models (DLMs). By utilizing adaptive acceleration (dynamically adjusting the volume of parallel decoding based on the established context) and backtracking-enhanced remasking (undoing tokens invalidated by new context), it achieves an average Pass@1 improvement of 1.9% while attaining a 251.4% inference speedup in code generation.

CreditDecoding: Accelerating Parallel Decoding in Diffusion Large Language Models with Trace Credit

This paper proposes CreditDecoding, a training-free parallel decoding acceleration method that enhances correct but low-confidence tokens by accumulating token-level historical evidence (trace credit), achieving up to a 5.48x speedup and a 0.48 accuracy improvement on LLaDA-8B-Instruct.

Native Hybrid Attention for Efficient Sequence Modeling

This paper proposes Native Hybrid Attention (NHA), which unifies the long-term memory slots of linear RNNs with the short-term precise tokens of sliding windows through a single softmax attention operation. This achieves native unification of intra-layer and inter-layer mixing—dynamically allocating attention weights between long and short terms without extra fusion parameters—outperforming Transformer and other hybrid baselines on recall-intensive and common-sense reasoning tasks.

Lizard: An Efficient Linearization Framework for Large Language Models

Lizard replaces the softmax attention of pretrained Transformers with a hybrid subquadratic attention module (Gated Linear Attention for global compression + Anchor Window Attention for local precision + learnable gates replacing RoPE). Using only 0.04B tokens for distillation, it outperforms existing linearization methods by 9.4–24.5 points on 5-shot MMLU and achieves a 32% throughput increase via a tensor-core-friendly training algorithm.

CoMeT: Collaborative Memory Transformer for Efficient Long Context Modeling

CoMeT introduces a "global memory + FIFO temporary memory" dual-memory plug-in for existing LLMs. By processing inputs in chunks, it achieves constant memory and linear time complexity. Fine-tuned only on 32k context, it enables precise retrieval at any position within 1M tokens and proposes hierarchical pipeline parallelism to allow fine-tuning 128k context on 16×80GB GPUs.

Task-Aware LLM Routing with Multi-Level Task-Profile-Guided Data Synthesis for Cold-Start Scenarios

A multi-level task-profile-guided data synthesis framework is proposed to address the cold-start problem in LLM routing. TRouter, a routing method using task types as latent variables, is designed to model the query-cost-performance relationship via variational inference, achieving effective routing in both cold-start and in-domain settings.

The Illusion of Specialization: Revealing the "Standing Committee" in Mixture-of-Experts Models

By introducing the CommitteeAudit framework, the authors discover a "Standing Committee" in MoE models—a compact, persistent set of experts consistently activated and dominating routing weights across different domains. This contrasts with the widely assumed domain-specific specialization, revealing an inherent centralized structure in sparse computation.

Alloc-MoE: Budget-Aware Expert Activation Allocation for Efficient Mixture-of-Experts Inference

The "number of activated experts" in MoE inference is abstracted as a global budget \(B\). Optimal Top-K allocation is performed across layers via dynamic programming (Alloc-L), followed by token-level redistribution using global Top-\((K \cdot T)\) selection (Alloc-T). This approach halves the activation budget of DeepSeek-V2-Lite while maintaining accuracy, achieving a 1.15× speedup in prefill and a 1.34× speedup in decode.

Beyond Accuracy: Unveiling Inefficiency Patterns in Tool-Integrated Reasoning

This paper proposes PTE (Prefill Token Equivalents), a hardware-aware efficiency metric for tool-integrated reasoning (TIR) that unifies the costs of internal reasoning and external tool usage. Through large-scale experiments, it reveals four inefficiency patterns in TIR: confirmatory tool use, tool mixing, lack of tool priors, and tool format collapse.

Threshold Differential Attention: Sink-free, Ultra-sparse, and Non-dispersive Long-context Attention

TDA achieves sink-free, 99% precise sparsity, and competitive performance in long-context Transformer attention by combining length-adaptive thresholds with differential inhibitory views.

Browse all 23 LLM Efficiency papers →

---

📚 Pretraining (12)

FOREVER: Forgetting Curve-Inspired Memory Replay for Language Model Continual Learning

The authors realign the "spaced repetition" concept of the Ebbinghaus forgetting curve from "training steps" to "model time" (accumulated parameter update norm \(\Delta_t = \|\Theta_t - \Theta_{t-1}\|_2\)). Specifically, cumulative model time \(\tau_t\) determines when to replay, while the instability ratio \(r_t\) (current update intensity \(\mu_t\) vs. baseline \(\mu_0\)) adaptively controls how to replay (regularization strength). The method consistently outperforms SOTA across 3 CL benchmarks and 4 backbones (0.6B–13B), achieving OP +1.2% and BWT +0.9% over the strongest baseline VBM.

Data Mixing Agent: Learning to Re-weight Domains for Continual Pre-training

This paper proposes Data Mixing Agent, the first model-based end-to-end domain re-weighting framework. By training a small agent using CQL reinforcement learning on extensive data mixing trajectories, it learns generalizable data mixing heuristics. It balances performance between source and target domains in mathematical reasoning continual pre-training and generalizes to unseen source domains, target models, and domain spaces.

Toward Consistent World Models with Multi-Token Prediction and Latent Semantic Enhancement

This paper theoretically analyzes how Multi-Token Prediction (MTP) induces representation contractivity through a gradient coupling mechanism, facilitating the emergence of belief states. However, it also reveals the "structural hallucination" problem of MTP (illegal shortcuts in latent space). The proposed LSE-MTP framework anchors predictions to true latent state trajectories through latent consistency and semantic anchoring losses, significantly improving path legality and robustness in synthetic graphs and real-world Manhattan taxi navigation.

On the Proper Treatment of Units in Surprisal Theory

This paper points out that the choice of the "next unit" in surprisal theory has historically been implicitly determined by pre-trained language model tokenizers. It proposes a finite-state transduction framework that explicitly decouples model tokens, linguistic units, and experimental Regions of Interest (ROI), demonstrating on MECO eye-tracking data that different unit inventories fundamentally alter how surprisal predicts reading time.

Compact Example-Based Explanations for Language Models

This paper proposes Selection Relevance Score, a re-training-free metric to evaluate the quality of training sample subsets as example-based explanations. It demonstrates that the common "select highest influence" strategy is often inferior to random selection and further introduces a new strategy that balances influence and representativeness.

Fine-tuning vs. In-context Learning in Large Language Models: A Formal Language Learning Perspective

The authors utilize Probabilistic Hierarchical Context-Free Grammar (HPCFG) to construct a set of "contamination-free, bounded, and precisely samplable" formal languages as controlled testbeds. They propose the "Discriminative AUC Test" as a unified metric to systematically compare FT and ICL across 18 LLMs from 6 families on 6 languages. The study finds that FT consistently outperforms ICL in-distribution, but both perform equally on out-of-distribution data; ICL shares a similar inductive bias with FT but exhibits significantly higher sensitivity to specific tokens.

Is a Document Educational or Just Wikipedia-Style? -- Pitfalls of Classifier-Based Quality Filtering

This paper discovers that Classifier-based Quality Filtering (CQF) mistakenly equates "Wikipedia-style writing" with "higher educational value." Simple rewriting allows low-quality web pages to bypass pre-training data filtering thresholds; approximately 7% of samples in FineWeb-Edu flip their filtering decisions as a result.

KoCo: Conditioning Language Model Pre-training on Knowledge Coordinates

Ours proposes Knowledge Coordinate (KoCo) conditioning for pre-training, which maps each document to a three-dimensional semantic coordinate (Source, Content, Stability). These coordinates are injected into pre-training as text prefixes, providing the model with explicit context-awareness. This approach improves performance across 10 downstream tasks, accelerates convergence by approximately 30%, and effectively mitigates hallucinations.

SAGE: Sign-Adaptive Gradient for Memory-Efficient LLM Optimization

Ours proposes the SAGE optimizer, which addresses the "embedding layer dilemma" where lightweight optimizers fail on embedding layers. By combining a Lion-style sign update direction with an \(O(d)\) memory overhead adaptive damping scaling factor, SAGE achieves new SOTA perplexity on Llama models (up to 1.3B) with significantly lower optimizer memory.

Demystifying Data Organization for Enhanced LLM Training

This paper systematically investigates the impact of "sample appearance order" in LLM training. By reusing existing sample-level quality/difficulty scores, it proposes four data organization principles: boundary reinforcement, cyclic review, continuous curriculum, and local diversity. The proposed STR and SAW strategies consistently enhance performance in both pre-training and SFT.

Browse all 12 Pretraining papers →

---

✏️ Knowledge Editing (10)

EvoEdit: Evolving Null-space Alignment for Robust and Efficient Knowledge Editing

Ours proposes EvoEdit, which achieves large-scale sequential knowledge editing by dynamically evolving a null-space projector. It efficiently injects new knowledge while maintaining existing knowledge, preserving SOTA performance at the 10K editing scale and running 3.5x faster than AlphaEdit.

One Mask to Rule Them All: On Hidden Facts after Editing and How to Find Them

This paper discovers that ROME / MEMIT does not truly overwrite old knowledge but suppresses it through a shared overattention mechanism; a sparse binary mask can reverse most edits and reduce the success rate of new edits from 98% to 38%.

Spectral Characterization and Mitigation of Sequential Knowledge Editing Collapse

The paper explains why sequential knowledge editing causes LLM general ability collapse from the perspective of SVD spectral structure and proposes REVIVE. By filtering update components that interfere with the dominant singular subspace within the singular vector basis of the original weights, REVIVE enables editors like MEMIT, RECT, and AlphaEdit to maintain both editing success rates and general capabilities under 10,000 to 20,000 continuous edits.

FABLE: Fine-grained Fact Anchoring for Unstructured Model Editing

This paper identifies that existing unstructured model editing methods, while capable of holistic recall of edited text, fail to provide access to fine-grained facts. It proposes the FABLE framework, which uses a two-stage hierarchical strategy to anchor fine-grained facts in shallow layers and integrate holistic narratives in deep layers, and constructs the UnFine diagnostic benchmark for systematic evaluation.

Aligning Language Models with Real-time Knowledge Editing

Introduces CRAFT (a continuously updated Chinese financial knowledge editing dataset) and KEDAS (a knowledge editing alignment paradigm based on diverse edit augmentation and adaptive inference) to resolve the difficulty of balancing success rate, locality, and portability in real-time knowledge editing scenarios.

Can Factual Opinions Be Edited (Manipulated) in Large Language Models?

This paper points out that existing knowledge editing techniques can be used to manipulate the "documented stances of public figures" (factual opinions). To address this, the authors construct the FOE benchmark with evidence and find that current methods result in "surface-level opinion changes with contradictory evidence." They propose a two-stage Self-Generated Evidence-Aligned method, enabling edited models to provide self-consistent evidence for manipulated opinions without relying on explicit instructions.

CLaRE-ty Amid Chaos: Quantifying Representational Entanglement to Predict Ripple Effects in LLM Editing

CLARE proposes a lightweight representational method that quantifies the degree of entanglement between facts through forward activations of a single intermediate layer. It is used to predict ripple effects in model editing, achieving an average improvement of 62.2% in Spearman correlation compared to gradient-based methods, while being 2.74x faster with 2.85x less memory consumption.

HiEdit: Lifelong Model Editing with Hierarchical Reinforcement Learning

HiEdit utilizes hierarchical reinforcement learning to decompose "lifelong model editing" into two subtasks: high-level layer selection and low-level gradient update calculation. This allows the hypernetwork to adaptively modify only half of the layers based on specific knowledge, improving the strong baseline RLEdit by an average of 8.48%.

Representation Interventions Enable Lifelong Knowledge Memory Control in LLMs

This paper proposes RILKE, which transforms lifelong knowledge editing from "modifying model weights" to "applying low-rank interventions in the hidden representation space." Through robust training, query-adaptive routing, and shared subspace modules, RILKE maintains near-perfect editing success rates and strong generalization after 1,000 unstructured knowledge edits while significantly reducing storage overhead.

The Model Agreed, But Didn't Learn: Diagnosing Surface Compliance in Large Language Models

The SA-MCQ diagnostic framework is proposed to reveal the "surface compliance" phenomenon in knowledge editing—where editors achieve high scores on standard benchmarks but fail to truly overwrite internal beliefs. Models revert to original parametric memory in discriminative self-assessment, and sequential editing accumulates representational residue, leading to cognitive instability.

---

💬 LLM (Other) (62)

A Study of LLMs' Preferences for Libraries and Programming Languages

This study presents the first systematic investigation into the preferences of 8 LLMs regarding libraries and programming languages during code generation. It reveals that LLMs exhibit a severe bias toward popular libraries like NumPy (45% unnecessary usage) and the Python language (chosen in 58% of high-performance tasks), and that natural language recommendations often diverge from actual code selection behavior.

Confidence Estimation for LLMs in Multi-turn Interactions

This paper presents the first systematic study of LLM confidence estimation in multi-turn dialogue scenarios. It proposes two core desiderata (per-turn calibration and monotonicity with increasing information), the corresponding InfoECE metric and Kendall’s \(\tau\) evaluation, and the Hinter-Guesser dataset construction paradigm. A novel P(SUFFICIENT) logit probe is introduced. Findings indicate that existing methods (verbalized / SC / P(TRUE)) exhibit poor calibration and monotonicity in multi-turn settings. In contrast, P(SUFFICIENT) reduces InfoECE to 5.27 on the GUESS task (vs. 79.97 for P(TRUE)) and achieves a \(\tau\) of 81.51, although the task remains far from solved.

When TableQA Meets Noise: A Dual Denoising Framework for Complex Questions and Large Tables

By decomposing semantic units in questions and constructing evidence trees for transparent table pruning, the EnoTab framework achieves significant performance gains when processing complex questions and ultra-large tables, effectively mitigating the negative impact of noisy data on reasoning through a dual denoising mechanism.

One Persona, Many Cues, Different Results: How Sociodemographic Cues Impact LLM Personalization

This paper systematically compares the effects of 6 common persona prompting methods (two variants each for name, explicit mention, and dialogue history) across 7 LLMs and 4 tasks. The study find that while average responses are highly correlated across prompting methods, the differences between personas generated by different methods vary significantly. Overly explicit prompts lead to stronger personalization bias, suggesting that bias conclusions should not be drawn based on a single prompting method.

Generative Interfaces for Language Models

This paper proposes Generative Interfaces (GenUI), which enables LLMs to move beyond single-box chat responses by generating interactive Web interfaces tailored to specific queries. Using a structured intermediate representation of "interaction flow graphs + finite state machines" and "adaptive reward-driven iterative refinement," GenUI achieves an 84% overall preference win rate against Claude 3.7's chat UI across 100 UIX prompts.

MulDimIF: A Multi-Dimensional Constraint Framework for Evaluating and Improving Instruction Following in Large Language Models

The authors propose MulDimIF, a multi-dimensional constraint framework that systematically evaluates the instruction-following capabilities of LLMs across three dimensions: constraint patterns (3 types), constraint categories (4 categories, 13 subcategories), and constraint difficulty (4 levels). Model performance is significantly improved via GRPO training, with findings indicating that improvements primarily stem from parameter updates in the attention modules.

Can AI Be a Good Peer Reviewer? A Survey of Peer Review Process, Evaluation, and the Future

The authors provide a systematic survey of AI-assisted peer review methods in the LLM era. They categorize "review generation" into four paradigms: fine-tuning / agent / RL / generation enhancement, classify "after-review" into rebuttal / meta-review / paper revision, and present a four-quadrant evaluation taxonomy (human / reference-based / LLM-based / aspect-oriented). Finally, they discuss the future across six directions: novelty, automatic evaluation, cross-domain, multimodality, and ethics.

Automatic Combination of Sample Selection Strategies for Few-Shot Learning

This paper proposes the ACSESS method, which automatically identifies and combines complementary sample selection strategies through three mechanisms: forward selection, backward selection, and Datamodels. Validated across 23 strategies, 5 ICL models, 3 gradient-based few-shot learning methods, and 14 datasets (6 text, 8 image), the combined strategy consistently outperforms single strategies and ICL-specific baselines.

Min-k Sampling: Decoupling Truncation from Temperature Scaling via Relative Logit Dynamics

Min-k Sampling detects the "semantic cliff" (the boundary between high-confidence candidates and low-quality tail noise) by analyzing the local structure of sorted logit distributions. It achieves strict temperature-invariant truncation, maintaining robust reasoning and creative writing quality even at extreme temperatures.

Understanding Structured Financial Data with LLMs: A Case Study on Fraud Detection

This paper proposes FinFRE-RAG, a two-stage framework that serializes high-dimensional tabular transaction data into natural language via importance-guided feature dimensionality reduction. By combining label-aware retrieval-augmented in-context learning, it significantly improves the F1/MCC of open-source LLMs in financial fraud detection, narrowing the performance gap with specialized tabular classifiers.

Browse all 62 LLM (Other) papers →

---

📖 NLP Understanding (34)

It's High Time: A Survey of Temporal Question Answering

This paper provides a comprehensive survey of Temporal Question Answering (TQA), proposing a unified analytical framework based on three dimensions: corpus temporality, question temporality, and model temporal capability. It systematically reviews the evolution of TQA methods from rule-based pipelines to the Transformer/LLM era, organizes benchmark datasets and evaluation strategies, and identifies future challenges.

A Computational Method for Measuring "Open Codes" in Qualitative Analysis

This paper proposes a theory-based computational method to systematically evaluate human and AI performance in inductive qualitative coding through an LLM-enhanced code merging algorithm and four ground-truth-free metrics (Coverage, Overlap, Novelty, and Divergence).

Agree, Disagree, Explain: Decomposing Human Label Variation in NLI through the Lens of Explanations

The LiTEx reasoning taxonomy is extended from "explanation variation under label agreement" to "label disagreement" scenarios. It is found that annotators may have different labels but similar reasoning, and the consistency of reasoning categories reflects the semantic similarity of explanations better than label consistency.

Beyond Chunking: Discourse-Aware Hierarchical Retrieval for Long Document Question Answering

This paper leverages Rhetorical Structure Theory (RST) to parse the discourse organization of long documents, constructing a sentence-level hierarchical tree with intermediate nodes enhanced by LLM summarization. By performing structure-aware multi-granularity retrieval on this tree, the proposed method consistently outperforms fixed-size chunking and RAPTOR-style semantic clustering across four benchmarks: QASPER, QuALITY, NarrativeQA, and MultiFieldQA-zh.

MTSQL-R1: Towards Long-Horizon Multi-Turn Text-to-SQL via Agentic Training

MTSQL-R1 transforms multi-turn Text-to-SQL from "one-shot translation" into a long-horizon agent training problem that interacts with databases and dialogue memory. Through self-teaching warm-start SFT and multi-level GRPO rewards, small-scale Qwen3 models outperform strong closed-source prompting baselines and short-horizon SFT/RL baselines on CoSQL and SParC.

Table Question Answering in the Era of Large Language Models: A Comprehensive Survey

This paper provides a comprehensive survey of Table Question Answering (TQA) research in the LLM era. It systematically categorizes task settings across five dimensions (table format, question complexity, answer format, modality, and domain) and organizes modeling approaches based on core challenges (table understanding, complex queries, large inputs, data heterogeneity, and knowledge integration). Covering 277 papers, it also provides forward-looking discussions on emerging directions such as reinforcement learning and interpretability.

Test-Time Reasoners Are Strategic Multiple-Choice Test-Takers

This paper systematically compares 12 reasoning LLMs on full multiple-choice questions (MCQs) versus choices-only MCQs. It finds that test-time reasoning (TTR) indeed allows models to perform above chance in choices-only scenarios. However, reasoning traces reveal that this is not entirely shallow cheating but includes "strategic test-taking" behaviors such as inferring missing questions, eliminating incorrect options, and invoking factual knowledge.

ASTRA: Adaptive Semantic Tree Reasoning Architecture for Complex Table Question Answering

ASTRA adaptively reconstructs complex tables into semantic trees and employs a dual-mode reasoning approach consisting of text tree navigation and symbolic code execution. It achieves accuracies of 91.6%, 81.9%, and 90.1% on AIT-QA, SSTQA, and HiTab, respectively, outperforming strong LLMs and existing table structuralization methods.

Can LLMs Estimate Cognitive Complexity of Reading Comprehension Items?

This paper constructs the ReCo reading comprehension cognitive complexity dataset and systematically evaluates whether 8 LLMs can automatically determine the required evidence scope and transformation levels for items. Results indicate that strong models approach but remain significantly lower than experts, particularly in identifying complete evidence sets and fine-grained word-order transformations.

Lost in the Prompt Order: Revealing the Limitations of Causal Attention in Language Models

This paper investigates the sensitivity of Large Language Models (LLMs) to the order of prompt components in multiple-choice questions (MCQA). Through systematic experiments, it excludes training bias and memory decay hypotheses, revealing that the causal attention mask is the fundamental mechanism leading to significant performance degradation in the QOC (Question-Options-Context) order.

Browse all 34 NLP Understanding papers →

---

✍️ Text Generation (17)

Frankentext: Stitching Random Text Fragments into Long-Form Narratives

This paper proposes the Frankentext paradigm, which enables LLMs to stitch random human text fragments into coherent long-form narratives under extreme constraints (90% of text copied verbatim from human writing). This reveals the severe failure of current AI text detectors in mixed-authorship scenarios (72% of Frankentext is misclassified as human writing).

ConlangCrafter: Constructing Languages with a Multi-Hop LLM Pipeline

This paper introduces ConlangCrafter, an LLM-based multi-hop pipeline that decomposes constructed language (conlang) design into modular stages of phonology, grammar, and lexicon. It ensures typological diversity through randomness injection and internal consistency via self-refinement loops, while proposing an automated evaluation framework encompassing typological diversity analysis and translation consistency.

XtraGPT: Context-Aware and Controllable Academic Paper Revision via Human-AI Collaboration

This paper presents XtraGPT—the first open-source LLM suite (1.5B–14B) specifically for academic paper revision. By fine-tuning on 7,000 top-tier conference papers and 140,000 standard-guided instruction-revision pairs, it achieves context-aware paragraph-level controllable revisions. The 7B version matches GPT-4o-mini, while the 14B version outperforms GPT-4o-mini. Human evaluations show an average increase of 0.65 points in predicted paper scores after revisions.

In-depth Research Impact Summarization through Fine-Grained Temporal Citation Analysis

This paper proposes the "Scientific Impact Summarization" task: first identifying fine-grained intents that truly reveal impact from the citation contexts of a paper, and then generating an impact narrative that evolves over time. This approach better illustrates how a paper is adopted, criticized, and transformed by subsequent work compared to simple citation counts.

Adaptive Planning for Multi-Attribute Controllable Summarization with Monte Carlo Tree Search

This paper proposes PACO, which reformulates "multi-attribute controllable summarization" as a planning problem to find an "attribute control sequence." Using a customized Monte Carlo Tree Search (where nodes are full summaries and actions are single-attribute adjustments), it identifies the optimal adjustment path during the prompting stage without any attribute-specific training. With Llama-3.2-1B, it achieves controllability comparable to the Llama-3.3-70B baseline, while Llama-3.3-70B + PACO surpasses all existing methods.

Can You Make It Sound Like You? Post-Editing LLM-Generated Text for Personal Style

The authors conducted a pre-registered online study with 81 participants who used GPT-o4-mini to draft and then manually post-edit style-sensitive texts such as wedding vows and apology letters. The findings reveal that while post-editing significantly moves the text toward the user's personal style and away from the LLM's style, the edited texts still systematically retain more "AI-like" traces than independent writing—a residue that participants themselves fail to perceive.

Children's English Reading Story Generation via Supervised Fine-Tuning of Compact LLMs with Controllable Difficulty and Safety

The authors utilized 2,580 stories generated by GPT-4o / Llama-3.3-70B corresponding to the UFLI K–2 English reading curriculum to perform four SFT designs (baseline, Good Stories, Rewarded SFT, and simulated children's pronunciation errors) on three 8B models (Llama 3 / Granite 3.3 / Apertus). The results demonstrate that compact models + appropriate SFT strategies can outperform zero-shot GPT-4o and Llama-3.3-70B on key K-2 metrics such as Spache readability, syntactic complexity, and toxicity. Among these, Rewarded SFT proved most stable and nearly hallucination-free.

EDUMATH: Generating Standards-aligned Educational Math Word Problems

The authors systematize the task of "generating math word problems (MWP) aligned with K-12 math curriculum standards," collecting 11,000+ STEM MWP training data points annotated by real US teachers. Through an SFT + KTO + ModernBERT filtering pipeline, they trained two open-source SOTA generators, EDUMATH-12B/30B. They conducted the first RCT on actual 3rd-5th grade students, finding that while student accuracy was comparable between LLM-generated and human-written problems, students showed an almost unanimous preference for customized LLM problems.

FACTS: Table Summarization via Offline Template Generation with Agentic Workflows

Ours proposes FACTS (Fast, Accurate, and Privacy-Compliant Table Summarization), which automatically generates reusable offline templates (SQL queries + Jinja2 templates) through a three-stage Agentic workflow. It achieves rapid, accurate, and privacy-compliant query-focused table summarization, outperforming baselines across FeTaQA, QTSumm, and QFMTS benchmarks.

Investigating the Representation of Backchannels and Fillers in Fine-tuned Language Models

This paper trains BERT, GPT-2, TurnGPT, LLaMA-3 8B, and Qwen-3 8B on English and Japanese spoken dialogue corpora using three fine-tuning tasks: MASK, NTP, and TTP. It utilizes t-SNE visualization and silhouette clustering to quantify the representation quality of "backchannels" (e.g., uh-huh) and "fillers" (e.g., um). The study finds that fine-tuning significantly distinguishes these "semantically bleached" functional words within the embedding space and enables models to naturally generate diverse backchannels/fillers during NLG, marking a quantifiable step toward "human-like conversational LMs."

Browse all 17 Text Generation papers →

---

🗣️ Dialogue Systems (26)

Simulated Students in Tutoring Dialogues: Substance or Illusion?

This paper proposes a evaluation framework for simulated students in mathematics tutoring dialogues. It finds that simple prompting often produces "students who seem to know how to answer," whereas SFT and DPO align more closely with real student behavior, though error replication and modeling of individual differences remain largely unresolved.

APEX-MEM: Agentic Semi-Structured Memory with Temporal Reasoning for Long-Term Conversational AI

The proposed system constructs long-term conversational memory using a trio of "domain-agnostic ontology-supported property graphs + append-only event storage + ReAct multi-tool retrieval agents." By never overwriting during construction and resolving temporal conflicts only at retrieval, it achieves 88.88% on LOCOMO (3.5% higher than MIRIX) and 86.2% on LongMemEval (13.7% higher than the strongest RAG baseline).

Preference Learning Unlocks LLMs' Psycho-Counseling Skills

This paper constructs the PsyCoPref preference dataset for psycho-counseling response quality and employs reward models, DPO, and iterative preference learning to train LLMs. The resulting 8B model achieves an 87.0% win rate against GPT-4o in psycho-counseling responses.

SPASM: Stable Persona-driven Agent Simulation for Multi-turn Dialogue Generation

This paper proposes SPASM, a stability-centric persona-driven multi-turn dialogue simulation framework. Through modular persona generation, Egocentric Context Projection (ECP), and termination detection, it significantly reduces character drift and "echo" effects in LLM-LLM dialogues, constructing a high-quality dataset of 45,000 multi-turn dialogues.

Context-Agent: Dynamic Discourse Trees for Non-Linear Dialogue

The authors propose Context-Agent, which models multi-turn dialogue history as a "forest of discourse trees" (where each tree represents an independent topic and each branch represent an instruction refinement/fork). Nodes are organized by navigational intent rather than semantic similarity. Accompanying the model is the NTM benchmark for evaluating non-linear long-range dialogues, demonstrating improved task completion rates and reduced token consumption across various LLMs.

Author-in-the-Loop Response Generation and Evaluation: Integrating Author Expertise and Intent in Responses to Peer Review

This work redefines academic author response (rebuttal) generation as an "Author-in-the-Loop" task, introducing the Re3Align dataset (3.4K papers, 440K sentence-level edit annotations, 15K review-response-revision triplets), the REspGen controllable generation framework, and the REspEval evaluation suite with 20+ metrics. The approach systematically validates the effects of author input, controllability, and evaluation-guided refinement across 5 state-of-the-art LLMs.

Disambiguation-Centric Finetuning Makes Enterprise Tool-Calling LLMs More Realistic and Less Risky

The DiaFORGE framework is proposed, featuring a disambiguation-centric synthetic data generation pipeline, reasoning-chain finetuning, and a dynamic evaluation system. This allows open-source LLMs to achieve a tool-calling success rate 27 percentage points higher than GPT-4o and 49 percentage points higher than Claude-3.5-Sonnet when facing near-duplicate enterprise APIs.

Metro: Towards Strategy Induction from Expert Dialogue Transcripts for Non-collaborative Dialogues

Metro automatically induces expert dialogue transcripts into a "Strategy Forest"—a collection of trees rooted in K-Means clustered dialogue states. Each node represents an LLM-expanded micro-principle action, and branches represent complete action trajectories pruned by Wilson confidence lower bounds and MCTS-style value backpropagation. During inference, it retrieves a specific tree to extract short-term (breadth) and long-term (depth) recommendations in parallel. Without any training, it outperforms baselines such as PRINCIPLES, PPDPP, and GDP-Zero by approximately 10% on P4G and CB non-collaborative dialogue tasks.

Reasoning Gets Harder for LLMs Inside A Dialogue

This paper introduces the Boulder dynamic benchmark, demonstrating that while LLMs perform well on isolated reasoning problems, their performance significantly degrades when the same problems are embedded in task-oriented dialogues. This is primarily attributed to multi-turn context, dialogue role constraints, and the burden of tool calling.

STRIDE-ED: A Strategy-Grounded Stepwise Reasoning Framework for Empathetic Dialogue Systems

This paper proposes the STRIDE-ED framework, which constructs a comprehensive empathetic strategy system covering positive/neutral/negative emotions. By designing task-aligned multi-stage cognitive CoT reasoning combined with strategy-aware data refinement and a two-stage SFT+PPO training paradigm, it achieves SOTA performance in empathetic dialogue across multiple open-source LLMs, reaching an emotion accuracy of 57.25% and a BLEU-4 of 4.67.

Browse all 26 Dialogue Systems papers →

---

🌐 Multilingual & Translation (64)

Evaluating Robustness of Large Language Models Against Multilingual Typographical Errors

This paper proposes MulTypo—a multilingual typo generation algorithm based on language-specific keyboard layouts and 10-finger typing habits. It systematically evaluates the robustness of 18 open-source LLMs across 12 languages and 5 downstream tasks, demonstrating that typos significantly impact generation and reasoning tasks, instruction-tuned models are more fragile, and typo effects exhibit cross-lingual and directional asymmetry.

Beyond Literal Mapping: Benchmarking and Improving Non-Literal Evaluation Evaluation

The authors construct MENT, a meta-evaluation dataset for non-literal translation (7,530 human annotations), revealing the unreliability of traditional metrics and LLM-as-Judge in non-literal scenarios. They propose the RATE agentic evaluation framework, which improves correlation with human judgment by over 3.2 points through a reflective core agent that dynamically invokes functional sub-agents.

LaoBench: A Large-Scale Multidimensional Lao Benchmark for Large Language Models

LaoBench is the first large-scale, multidimensional Lao evaluation benchmark for LLMs, containing 17,000+ expert-curated samples. It covers three dimensions: Culture-Knowledge Application, Lao K12 Curriculum, and Lao-Chinese-English trilingual translation. It features a unique three-part design—Open-source 7k + Black-box 10k + Open-ended 500. The 10k black-box set prevents contamination via a controlled scoring service. Mainstream closed-source models (GPT-5-High, Gemini-2.5-Pro, etc.) still lag behind human experts by ~10-20 percentage points, indicating that Lao cultural reasoning and translation fidelity remain significant unsolved challenges.

Evaluating the Impact of Verbal Multiword Expressions on Machine Translation

This paper presents the first systematic evaluation of the impact of Verbal Multiword Expressions (VMWEs: Verbal Idioms (VID), Verb-Particle Constructions (VPC), and Light Verb Constructions (LVC)) on machine translation quality. Analyzing 8 MT systems across 7 language pairs using two QE models and human DA scores, the study proves that VMWEs consistently lead to performance degradation. This degradation is strictly positively correlated with "non-compositionality" (VID > VPC > LVC), and even GPT-4.1/GPT-5.1 cannot eliminate this regression.

Is Human-Like Text Liked by Humans? Multilingual Human Detection and Preference Against AI

The authors organized 19 native experts to conduct 8.8k human-machine text discrimination trials across 16 datasets involving 9 languages, 9 domains, and 11 SOTA LLMs. They found that the average expert accuracy reached 87.6% (significantly higher than the "near random" conclusions of early studies) and further revealed that while machine text rewritten with prompts explicitly addressing differences can lower detection accuracy to 72.5%, humans tend to prefer machine text when they cannot distinguish its source, challenging the implicit assumption that "human-like equals liked-by-human."

TransLaw: A Large-Scale Dataset and Multi-Agent Benchmark Simulating Professional Translation of Hong Kong Case Law

This paper constructs the first sentence-level parallel dataset, HKCFA Judgement 97-22, specifically for English-Chinese translation of Hong Kong Court of Final Appeal judgements. It proposes the TransLaw multi-agent system, which simulates professional legal translation workflows. TransLaw significantly outperforms single-agent benchmarks in automatic metrics, professional legal translator evaluations, and cost-efficiency.

Digitizing Nepal's Written Heritage: A Comprehensive HTR Pipeline for Old Nepali Manuscripts

This is the first end-to-end Handwritten Text Recognition (HTR) pipeline for Old Nepali. By employing a "Synthetic Devanagari → Printed Nagari → Old Nepali Manuscripts" three-stage transfer learning curriculum, \(8\times\) data augmentation with 20 techniques, byte-level BPE, and a script-aware decoder, the CER is reduced from a fine-tuned TrOCR baseline of \(9.6\%\) to \(4.9\%\). The code, models, and a Streamlit web application are open-sourced.

Location Not Found: Exposing Implicit Local and Global Biases in Multilingual LLMs

This paper introduces the LocQA benchmark (12 languages, 49 regions, 2,156 region-relevant Q&As) to reveal implicit biases in LLMs through geographically ambiguous questions (e.g., "What is the emergency phone number?"). It uncovers persistent cross-lingual US-centric defaults (50% of model responses contain US answers vs. 26% in the data) and a "population probability engine" effect driven by population size within languages. Furthermore, instruction tuning is found to exacerbate global bias.

LQM: Linguistically Motivated Multidimensional Quality Metrics for Machine Translation

Ours proposes LQM (Linguistically Motivated Multidimensional Quality Metrics), a six-level linguistically motivated MT error typology (sociolinguistics → pragmatics → semantics → morphosyntax → orthography → graphetics), and constructs a bidirectional parallel corpus of 3,850 sentences across 7 Arabic dialects. Through expert annotation of 6,113 error spans, the study reveals systematic deficiencies in existing MT systems regarding dialectal and culture-aware translation.

Mitigating Catastrophic Forgetting in Target Language Adaptation of LLMs via Source-Shielded Updates

Proposes Source-Shielded Updates (SSU), a column-wise freezing strategy driven by source data importance scores. In continual pre-training (CPT) using only unlabeled target language data, it reduces source language performance degradation from 20.3% (Full Fine-Tuning) to 3.4% while maintaining comparable or superior target language performance.

Browse all 64 Multilingual & Translation papers →

---

🔍 Information Retrieval & RAG (73)

PL-MTEB: Polish Massive Text Embedding Benchmark

PL-MTEB constructs a 30-task evaluation set for Polish text embeddings covering classification, clustering, pair classification, retrieval, and semantic similarity. It systematically evaluates 30 Polish and multilingual embedding models, showing that while large models generally lead, factors such as task type, training data leakage, and model scale significantly impact the conclusions.

ReasonEmbed: Enhanced Text Embeddings for Reasoning-Intensive Document Retrieval

ReasonEmbed introduces three technical innovations—the ReMixer non-trivial synthetic data method (82K high-quality samples), Redapter adaptive reasoning-intensity weighted training, and multi-backbone implementations—achieving an nDCG@10 of 38.1 on the BRIGHT benchmark, significantly outperforming all existing text embedding models by approximately 10 points.

HyperMem: Hypergraph Memory for Long-Term Conversations

HyperMem replaces pairwise edges in traditional RAG with "hyperedges" (edges connecting \(\ge 3\) nodes), organizing long-term conversation memory into a "Topic → Episode → Fact" structure. By combining coarse-to-fine retrieval with hypergraph embedding propagation, it solves retrieval fragmentation caused by multi-episode cross-temporal dependencies, achieving a 92.73% LLM-as-judge accuracy on the LoCoMo benchmark (compared to the Prev. SOTA of 86.49%).

CiteGuard: Faithful Citation Attribution for LLMs via Retrieval-Augmented Validation

CiteGuard proposes a retrieval-augmented agent framework that provides a more faithful foundation for scientific citation attribution via expanded retrieval actions (including full-text search and contextual retrieval), achieving 68.1% accuracy on the CiteME benchmark—a 10 percentage point improvement over baselines and close to human performance (69.2%).

End-to-End Optimization of LLM-Driven Multi-Agent Search Systems via Heterogeneous-Group-Based Reinforcement Learning

This paper proposes MHGPO (Multi-Agent Heterogeneous Group Policy Optimization), a critic-free multi-agent RL method. By employing heterogeneous group relative advantage estimation and backward reward propagation, it achieves end-to-end optimization in a three-agent search system (Rewriter→Reranker→Answerer). It captures implicit cross-agent dependencies and cross-trajectory correlations, significantly outperforming MAPPO and GRPO baselines on multi-hop QA benchmarks such as HotpotQA.

VideoStir: Understanding Long Videos via Spatio-Temporally Structured and Intent-Aware RAG

VideoStir proposes a structured and intent-aware long video RAG framework. It models videos as spatio-temporal graphs for multi-hop clip retrieval and trains an intent relevance scorer for frame-level filtering. It achieves performance comparable to SOTA long video RAG methods without relying on auxiliary text tools.

Beyond Chunks and Graphs: Retrieval-Augmented Generation through Triplet-Driven Thinking

T2RAG replaces the minimum retrieval unit of RAG from "text chunks/KG nodes" with atomic triplets. Off-line, the corpus is extracted into a collection of triplet propositions for indexing. On-line, the LLM decomposes the question into searchable triplets with ? placeholders, iteratively retrieving evidence from the triplet library to fill in the blanks until all placeholders are resolved to generate the final answer. This achieves an average improvement of up to 11% across six datasets while reducing retrieval costs by up to 45%.

RiTeK: A Dataset for Large Language Models Complex Reasoning over Textual Knowledge Graphs in Medicine

RiTeK constructs two large-scale medical Textual Knowledge Graphs (TKG) and corresponding complex reasoning QA datasets, covering 6 topological structures and rich textual descriptions. It evaluates 11 retrieval methods and reveals the severe inadequacies of existing LLM-driven retrieval systems in medical TKG reasoning.

ChatR1: Reinforcement Learning for Conversational Reasoning and Retrieval Augmented Question Answering

The authors extend "Search + Reasoning" RL frameworks (e.g., Search-R1 / R1-Searcher) from single-turn QA to multi-turn conversational QA. They propose ChatR1: a framework that jointly optimizes reasoning, searching, and answering end-to-end via PPO. It introduces an "intent-aware reward" using token-F1 between model-generated search queries and human-authored rewrites as a turn-level dense reward. ChatR1 outperforms ChatGPT/Claude using a 3B backbone across five CQA datasets and demonstrates significantly improved out-of-domain transfer capabilities.

Quantifying and Improving the Robustness of Retrieval-Augmented Language Models Against Spurious Features in Grounding Data

This paper proposes the SURE framework to systematically evaluate the sensitivity of RAG generation to semantically irrelevant spurious features (style, source, logic, format, metadata) in retrieved documents and significantly improves RALM robustness using synthetic data generated by SURE through SFT/DPO.

Browse all 73 Information Retrieval & RAG papers →

---

💻 Code Intelligence (49)

SWE-QA: Can Language Models Answer Repository-level Code Questions?

SWE-QA constructs a repository-level code question-answering benchmark covering 15 real-world Python repositories and 720 high-quality QA pairs. It induces question types from GitHub issues and validates answers through human experts. Experiments show that vanilla LLMs direct prompting is weak, and only RAG or tool-integrated agents like OpenHands/SWE-agent can approach the demands of real-world development QA.

CodeRL+: Improving Code Generation via Reinforcement with Execution Semantics Alignment

This paper proposes CodeRL+, which integrates execution semantics alignment into the RLVR training pipeline. By enabling models to infer variable-level execution trajectories, it bridges the gap between code textual representation and execution semantics. CodeRL+ achieves an average 4.6% improvement in pass@1 for code generation and improvements of 15.5% and 4.4% on code reasoning and test output generation benchmarks, respectively.

ReCode: Reinforcing Code Generation with Reasoning-Process Rewards

ReCode trains a reward model capable of evaluating the quality of code reasoning processes via CRPL and utilizes CG-GRPO to activate process rewards only when code execution is correct, thereby improving the Pass@1 of code generation models while avoiding reward hacking.

RExBench: Can coding agents autonomously implement AI research extensions?

RExBench places coding agents into real AI paper repositories to implement expert-designed research extensions. Performance is scored via controlled execution results, revealing that even the strongest current agents achieve only about a one-third success rate, indicating a significant gap in autonomous research capabilities.

EET: Experience-Driven Early Termination for Cost-Efficient Software Engineering Agents

Ours proposes EET—an experience-driven early termination method that identifies invalid iterations and terminates them early during the patch generation and selection stages. It reduces the total cost of SE Agents by 19%-55% (average 32%) while incurring almost no loss in task performance (maximum 0.2%).

SciCoQA: Quality Assurance for Scientific Paper–Code Alignment

Ours introduces SciCoQA, the first benchmark dataset for detecting discrepancies between scientific papers and their code implementations. It contains 635 discrepancy instances (92 real + 543 synthetic). Evaluation of 22 LLMs reveals that the strongest model only detects 46.7% of real discrepancies, highlighting a critical capability gap in automated scientific quality assurance.

SecureVibeBench: Evaluating Secure Coding Capabilities of Code Agents with Realistic Vulnerability Scenarios

This paper proposes SecureVibeBench, the first repository-level multi-file editing secure coding benchmark. It constructs 105 C/C++ secure coding tasks from 41 OSS-Fuzz projects. By accurately restoring the scenarios where vulnerabilities were first introduced through cascaded static and dynamic analysis, the evaluation reveals that only 23.8% of the code produced by the best agent (SWE-agent + Claude Sonnet 4.5) satisfies both functional correctness and security.

CodeWiki: Evaluating AI's Ability to Generate Holistic Documentation for Large-Scale Codebases

Ours proposes CodeWiki, an open-source framework based on hierarchical decomposition and recursive multi-agent processing for automatic repository-level code documentation generation. It also constructs the CodeWikiBench benchmark, where it surpasses the closed-source system DeepWiki (64.06%) with a quality score of 68.79% across seven programming languages.

Taming System Complexity: Demystifying Software Engineering Agents in Diagnosing Linux Kernel Faults

By establishing LinuxFLBench, a large-scale Linux kernel fault localization benchmark, this study reveals the limitations of existing LLM Agents in complex systems and proposes the LinuxFL+ framework. Through two-dimensional expansion (directory-awareness and potential causes), LinuxFL+ significantly improves fault localization accuracy at a low cost.

AutoMonitor-Bench: Evaluating the Reliability of LLM-Based Misbehavior Monitor

This paper constructs AutoMonitor-Bench, the first systematic benchmark for evaluating whether LLM-based monitors can reliably identify model misbehavior (3,010 paired samples covering safety violations, sycophancy/bias, and specification gaming). Evaluation across 22 open-source and closed-source monitoring models reveals a systematic trade-off between Miss Rate (MR) and False Alarm Rate (FAR). Furthermore, SFT experiments on 153k samples demonstrate that fine-tuning on easily constructed misbehavior fails to generalize to implicit specification gaming.

Browse all 49 Code Intelligence papers →

---

🎨 Image Generation (5)

Multimodal Large Language Models for Multi-Subject In-Context Image Generation

This paper proposes MUSIC, which introduces the visual reasoning capabilities of Multimodal Large Language Models (MLLMs) into multi-subject in-context image generation. Through automated training data synthesis, visual CoT, and semantic-driven spatial layout planning, it significantly mitigates issues of subject omission, identity confusion, and semantic drift when generating multiple reference subjects simultaneously.

ANCHOR: LLM-driven Subject Conditioning for Text-to-Image Synthesis

This paper proposes the ANCHOR dataset, featuring 70K+ abstract captions from 5 news outlets to expose T2I model failures in multi-subject, contextual reasoning, and fine-grained grounding. It introduces SAFE, which utilizes LLMs to extract key subjects and reinforces subject representations at the embedding layer to enhance image-text consistency.

From AR to Diffusion: Efficiently Adapting Large Language Models with Strictly Causal and Elastic Horizons

This paper proposes FLUID, which efficiently adapts pre-trained autoregressive (AR) LLMs into diffusion-based parallel generation models using strictly causal attention and entropy-aware Elastic Horizons. With only 2.7B adaptation tokens, it achieves reasoning and code generation performance close to strong AR models and superior to existing diffusion baselines.

MENTOR: Efficient Autoregressive Image Generation with Balanced Multimodal Control

MENTOR utilizes a unified autoregressive decoder and two-stage multimodal training to align reference images and text instructions into the same generation prefix. With only 3M training data and a budget of approximately 1.5 days on 8 A100 GPUs, it achieves a superior balance between concept preservation and prompt following.

Think Bright, Diffuse Nice: Enhancing T2I-ICL via Inductive-Bias Hint Instruction and Query Contrastive Decoding

This paper proposes TBDN, a training-free framework that utilizes Hint Instruction to focus LVLMs on the final query and Query Contrastive Decoding to suppress prior-dominated hallucinations. By delivering more accurate textual descriptions to diffusion models, it significantly improves text-to-image in-context learning performance on CoBSAT and T2I Fast Mini-ImageNet.

---

🎬 Video Generation (4)

Self-Correcting Text-to-Video Generation with Misalignment Detection and Localized Refinement

VideoRepair is introduced as the first training-free, model-agnostic self-correction framework for text-to-video generation. It utilizes MLLMs to detect fine-grained text-video misalignments, preserving correct regions while selectively refining problematic ones. It consistently improves alignment quality across four different T2V backbone models on EvalCrafter and T2V-CompBench.

OSCBench: Benchmarking Object State Change in Text-to-Video Generation

The authors propose OSCBench—the first benchmark specifically designed to evaluate Object State Change (OSC) capabilities in text-to-video (T2V) models. Built on cooking scenarios with 1,120 prompts covering Regular, Novel, and Compositional scenarios, the benchmark reveals that even the strongest T2V models achieve an OSC accuracy of only 0.786.

Accelerating Training of Autoregressive Video Generation Models via Local Optimization with Representation Continuity

The authors propose the Local Optimization + Representation Continuity (ReCo) training strategy. By optimizing within local windows and constraining smooth transitions of hidden states, they achieve a 2x acceleration in training autoregressive video generation models without sacrificing generation quality.

TeachMaster: Generative Teaching via Code

TeachMaster proposes the Generative Teaching paradigm, using code as an interpretable intermediate representation for educational videos. It employs collaborating agents for planning, code generation, narration, debugging, synchronization, and layout to produce full-course videos, achieving near-human quality while reducing the production cost of a 45-hour course to approximately 0.3% of traditional methods.

---

🧩 Multimodal VLM (82)

PRISM: Self-Pruning Intrinsic Selection Method for Training-Free Multimodal Data Selection

PRISM discovers that the non-zero mean of MLLM visual features causes Global Semantic Drift, which contaminates similarity-based data selection. By using training-free mean re-centering and low-correlation sample selection, it achieves 101.7% relative performance while retaining only approximately 30% of visual samples, reducing end-to-end GPU time by about 70%.

AdaTooler-V: Adaptive Tool-Use for Images and Videos

This paper identifies a widespread blind tool-use problem in existing "thinking with images" MLLMs—models tend to force zoom-in or frame extraction for all visual questions, resulting in overthinking that degrades accuracy and increases inference costs. To address this, the authors propose AdaTooler-V, which introduces the AT-GRPO reinforcement learning algorithm. By using a sample-level Tool Benefit Score to dynamically adjust reward scales (encouraging tool use when effective and penalizing it when unnecessary), a 7B model achieves 89.8% on the V* high-resolution benchmark, surpassing GPT-4o and Gemini 1.5 Pro.

UniversalRAG: Retrieval-Augmented Generation for Multimodal Corpora

UniversalRAG proposes a general any-to-any RAG framework that utilizes modality-aware routing and granularity-aware retrieval to dynamically select the most appropriate knowledge sources from heterogeneous multimodal corpora (text, image, video at varying granularities). This approach avoids the modality gap problem inherent in unified embedding spaces and significantly outperforms single-modality and unified methods across 10 benchmarks.

A Survey on MLLM-based Visually Rich Document Understanding: Methods, Challenges, and Emerging Trends

This paper systematically reviews Visually Rich Document Understanding (VRDU) based on Multimodal Large Language Models (MLLMs), categorizing OCR-based and OCR-free methods from two dimensions: feature representation/fusion and training paradigms, while discussing emerging directions such as data scarcity, multi-page documents, multilingual support, RAG, and agents.

From Verbatim to Gist: Distilling Pyramidal Multimodal Memory via Semantic Information Bottleneck

This paper proposes MM-Mem, a pyramidal multimodal memory architecture inspired by Fuzzy Trace Theory. It organizes memory into three levels: a Sensory Buffer (visual-dominant), an Episodic Stream (event-level summaries), and a Symbolic Schema (Knowledge Graph). Redundancy is compressed bottom-up via SIB-GRPO (Semantic Information Bottleneck + Reinforcement Learning), while retrieval is conducted top-down driven by entropy. The method achieves SOTA performance on four long-video benchmarks.

TEMA: Anchor the Image, Follow the Text for Multi-Modification Composed Image Retrieval

This paper proposes TEMA (Text-oriented Entity Mapping Architecture), the first framework for Composed Image Retrieval (CIR) oriented toward multi-modification texts. It enhances modified entity coverage through an MMT Parser Assistant (PA) and addresses the clause-entity alignment problem with an Entity Mapping (EM) module. Furthermore, it constructs two multi-modification benchmarks, M-FashionIQ and M-CIRR, achieving state-of-the-art performance in both original and multi-modification scenarios.

Efficient Inference for Large Vision-Language Models: Bottlenecks, Techniques, and Prospects

This paper proposes a systematic taxonomy for LVLM inference efficiency, analyzing bottlenecks along the encoding-prefilling-decoding pipeline. It reveals the systemic efficiency barrier caused by "vision token dominance" and summarizes a comprehensive technical map ranging from information density shaping and long-context attention management to memory bandwidth breakthroughs.

Prune-then-Merge: Towards Efficient Multi-Vector Visual Document Retrieval

This paper proposes Prune-then-Merge, a two-stage training-free multi-vector document compression framework. It first removes low-information patches via adaptive attention pruning, then merges the remaining high-signal patches through hierarchical agglomerative clustering. It extends the near-lossless compression range from 50-60% to 60-70% across 29 VDR datasets and significantly outperforms single-stage methods at high compression rates of 80%+.

MathFlow: Enhancing the Perceptual Flow of MLLMs for Visual Mathematical Problems

This work introduces the FlowVerse benchmark (decomposing mathematical problem information into four components: DI/EI/RP/OQ to construct six variants) and the MathFlow modular pipeline (decoupling perception and reasoning into independent stages). By training a specialized perception model, MathFlow-P-7B, to extract key information from mathematical diagrams, the approach significantly enhances the visual mathematical problem-solving capabilities of various reasoning models.

GuideDog: A Real-World Egocentric Multimodal Dataset for Blind and Low-Vision Accessibility-Aware Guidance

GuideDog utilizes an "expert-norm-driven silver-label generation + manual verification for gold labels" pipeline to construct 22K egocentric pedestrian scene image-text pairs (including an 818-question QA benchmark) from 269 global walking videos. This provides the first scaled, geographically diverse, and standardized training and evaluation data for MLLMs in BLV (Blind and Low-Vision) navigation tasks.

Browse all 82 Multimodal VLM papers →

---

🧠 VLM Reasoning (32)

Position: Multimodal Large Language Models Can Significantly Advance Scientific Reasoning

This is a position paper advocating that Multimodal Large Language Models (MLLMs) can significantly advance interdisciplinary scientific reasoning. It proposes a four-stage research roadmap (Broad Knowledge Recognition → Analogical Generalization → Insightful Reasoning → Creative Hypothesis Generation) and provides a systematic review of MLLM applications across mathematics, physics, chemistry, and biology, identifying five major challenges and eight future directions.

ErrorRadar: Benchmarking Complex Mathematical Reasoning of Multimodal Large Language Models Via Error Detection

This paper formally defines the multimodal error detection task and constructs the ErrorRadar benchmark—comprising 2,500 K-12 multimodal math problems derived from real student responses. It evaluates MLLM capabilities in two subtasks: error step localization (STEP) and error type classification (CATE), finding that the strongest model, GPT-4o, still trails human evaluation by approximately 10-15%.

Forest Before Trees: Latent Superposition for Efficient Visual Reasoning

This paper proposes Laser, which performs visual reasoning in latent space via Dynamic Window Alignment Learning (DWAL). By maintaining a "probabilistic superposition" of future semantics rather than precise token-by-token prediction, the model achieves a "global-to-local" cognitive hierarchy. Laser reaches SOTA among latent reasoning methods across six benchmarks using only 6 reasoning tokens (a 97%+ reduction), outperforming Monet by an average of 5.03%.

Addressing Overthinking in Large Vision-Language Models via Gated Perception-Reasoning Optimization

The GPRO framework is proposed to address overthinking in LVLMs by dynamically routing computation to three paths (Fast/Perception Re-check/Reasoning Reflection) at each token generation step through a meta-reasoning controller, simultaneously improving both accuracy and efficiency.

What's Missing in Screen-to-Action? Towards a UI-in-the-Loop Paradigm for Multimodal GUI Reasoning

This paper proposes the UILoop (UI-in-the-Loop) paradigm, reframing GUI reasoning from the traditional "Screen → Action" into a "Screen → UI Element → Action" cyclic process. Through UI element-driven reinforcement fine-tuning, the model is taught to explicitly locate, understand, and utilize key UI elements, achieving SOTA performance on GUI reasoning tasks.

MMErroR: A Benchmark for Erroneous Reasoning in Vision-Language Models

This paper proposes MMErroR, a multi-modal erroneous reasoning benchmark containing 1,997 samples. Each sample embeds a single reasoning error across 6 domains and 4 error types. It requires VLMs to not only detect the presence of errors in a reasoning chain but also classify the error type (Vision Perception, Knowledge Application, Question Understanding, or Reasoning Error). Evaluation of 12 representative VLMs reveals that the strongest model, Gemini-3-Pro-Preview, achieves only 66.65% accuracy.

TemporalVLM: Video LLMs for Temporal Reasoning in Long Videos

This paper proposes TemporalVLM, which extracts local fine-grained temporal features through a time-aware segment encoder (overlapping sliding Video Q-Former + fusion module) and aggregates global long-range dependencies using a BiLSTM. This marks the first introduction of LSTM into Video LLMs, outperforming previous methods across four tasks: dense video captioning, temporal localization, highlight detection, and action segmentation.

PROGRESSLM: Towards Progress Reasoning in Vision-Language Models

This paper defines the ability to "judge task completion stages from a single-frame observation" as the VLM's progress reasoning capability. It constructs Progress-Bench and ProgressLM-45K, demonstrating that explicit learning of "episodic retrieval + mental simulation" is more stable than simple prompt-based reasoning.

VL-Calibration: Decoupled Confidence Calibration for Large Vision-Language Models Reasoning

VL-Calibration decouples the verbalized confidence of LVLMs into visual confidence and reasoning confidence. By utilizing image-perturbation KL divergence, token entropy, and token-level advantage reweighting for training, the model simultaneously reduces ECE and improves accuracy across 13 visual reasoning benchmarks.

GeoRC: A Benchmark for Geolocation Reasoning Chains

Proposes GeoRC, the first geolocation reasoning chain benchmark written by GeoGuessr champion-level experts (800 reasoning chains, 500 scenes). It evaluates the ability of VLMs to generate auditable reasoning chains, finding that while closed-source VLMs can match human localization accuracy, the quality of their reasoning chains lags significantly, while open-source VLMs are almost equivalent to a pure hallucination baseline.

Browse all 32 VLM Reasoning papers →

---

⚡ VLM Efficiency (6)

HERMES: KV Cache as Hierarchical Memory for Efficient Streaming Video Understanding

This paper proposes HERMES, which conceptualizes KV cache as a hierarchical memory framework (shallow = sensory memory, middle = working memory, deep = long-term memory) based on a mechanistic analysis of MLLM decoder hierarchical attention preferences. It achieves training-free efficient streaming video understanding, maintaining or improving accuracy while reducing video tokens by 68%. The TTFT latency is <30ms, 10x faster than the previous SOTA.

HiPrune: Hierarchical Attention for Efficient Token Pruning in Vision-Language Models

This paper identifies a hierarchical attention pattern in vision encoders—middle layers focus on primary objects while deep layers capture global information. Based on this, it proposes HiPrune, a training-free and model-agnostic vision token pruning method. By selecting three types of tokens (Anchor/Buffer/Register) to preserve multi-level visual information, it maintains 99.3% performance using only 1/3 of the tokens, reducing FLOPs by 58.7%.

MACS: Modality-Aware Capacity Scaling for Efficient Multimodal MoE Inference

To address the "straggler" problem where Multimodal MoE models are bottlenecked by the "slowest expert" during Expert Parallelism (EP) inference, MACS re-estimates expert load using the Shannon entropy of visual tokens as semantic importance weights. It dynamically scales expert capacity based on the real-time modality composition of the batch. MACS is a training-free inference framework that maintains nearly identical performance (averaging 99.7% of vanilla MoE) across 12 multimodal benchmarks, significantly outperforming token-counting methods like CAI-MoE.

From Inheritance to Saturation: Disentangling the Evolution of Visual Redundancy for Architecture-Aware MLLM Inference Acceleration

This work reveals two sources of visual redundancy in MLLM inference: Inherited Visual Redundancy (IVR) caused by dense ViT tokenization and Secondary Saturation Redundancy (SSR) caused by deep semantic saturation, which manifests differently across backbone architectures. The proposed HalfV framework handles these two types of redundancy separately, achieving a 4.1x FLOPs acceleration on Qwen2.5-VL while preserving 96.8% of the performance.

APB-V: Accelerating Long-Video Understanding via Sequence-Parallelism-aware Approximate Attention

APB-V accelerates long-video LMM inference using sequence-parallelism-aware approximate attention and system-level load balancing. While preserving full visual embeddings, it achieves speedups of 12.72×, 1.70×, and 1.18× compared to FlashAttn, ZigZagRing, and APB, respectively, under a 64-frame 1440p setting without significant performance loss.

ReGATE: Learning Faster and Better with Fewer Tokens in MLLMs

ReGATE utilizes a frozen text-only teacher to estimate which output tokens require visual information, combined with the student's historical learning difficulty to dynamically select training tokens. This allows MLLMs to train faster with fewer tokens without changing architecture or adding parameters, achieving or exceeding standard fine-tuning performance on multiple image and video benchmarks.

---

🎵 Audio & Speech (71)

Full-Duplex-Bench-v2: A Multi-Turn Evaluation Framework for Duplex Dialogue Systems with an Automated Examiner

The authors propose Full-Duplex-Bench-v2, where a GPT-Realtime-powered Examiner interacts with full-duplex models in real-time via WebRTC across four task categories (Daily/Correction/Entity/Safety) and two pacing modes (Fast/Slow). Evaluation scores cover turn-taking, instruction-following, and task-specific dimensions. Findings reveal that performance for GPT-Realtime, Moshi, and Freeze-Omni degrades as dialogues progress, with open-source models performing particularly poorly on correction and entity tracking.

SpeechLLM-as-Judges: Towards General and Interpretable Speech Quality Evaluation

This paper extends speech quality assessment from "assigning a score" to "interpretable speech judging" by constructing the SpeechEval dataset, which contains 32,207 multi-lingual audios and 128,754 annotations. By utilizing CoT instruction tuning and GRPO training, SQ-LLM was developed, outperforming existing speech LLMs and expert models across four task categories: quality scoring, pair-wise comparison, improvement suggestions, and deepfake detection.

MTR-DuplexBench: Towards a Comprehensive Evaluation of Multi-Round Conversations for Full-Duplex Speech Language Models

This paper proposes MTR-DuplexBench, a comprehensive benchmark for evaluating Full-Duplex Speech Language Models (FD-SLMs) in multi-round scenarios. By introducing an innovative turn segmentation method to address blurred turn boundaries and context inconsistency, the framework evaluates four dimensions: conversational features, conversation quality, instruction following, and safety. Experiments reveal that existing FD-SLMs suffer from continuous performance degradation during multi-round interactions.

S2S-Arena: Evaluating Paralinguistic Instruction Following in Speech-to-Speech Models

S2S-Arena proposes a benchmark to evaluate S2S models directly in the speech modality. Using a four-level paralinguistic interaction protocol, 1,243 speech samples, and 1,001 pairwise comparisons, it reveals significant performance gaps in current systems regarding complex tone, emotion, speaking style, and expressive control.

ZipVoice-Dialog: Non-Autoregressive Spoken Dialogue Generation with Flow Matching

This paper proposes ZipVoice-Dialog, the first flow-matching-based non-autoregressive (NAR) zero-shot dialogue speech generation model. Through two simple designs—a curriculum learning strategy and speaker turn embeddings—the model resolves issues of speech unintelligibility and turn confusion when flow matching is directly applied to dialogue scenarios. Additionally, the first large-scale open-source dialogue speech dataset, OpenDialog (6.8k hours), is released.

ControlAudio: Tackling Text-Guided, Timing-Indicated and Intelligible Audio Generation via Progressive Diffusion Modeling

This paper proposes ControlAudio, a unified progressive diffusion modeling framework. Through a three-stage progressive training strategy (TTA pre-training → timing control fine-tuning → joint timing and intelligible speech training) and progressive guided sampling, it achieves text-guided, timing-precise, and intelligible speech generation within a single diffusion model. It significantly outperforms existing methods in timing accuracy and speech clarity.

Closing the Modality Reasoning Gap for Speech Large Language Models

This paper introduces TARS (Trajectory Alignment for Reasoning in Speech), a reinforcement learning-based framework that aligns speech-conditioned reasoning trajectories with text-conditioned trajectories through two dense signals: representation alignment and behavior alignment. It achieves SOTA performance in 7B-scale models, with the Modality Recovery Rate (MRR) approaching or even exceeding 100%.

StressTest: Can YOUR Speech LM Handle the Stress?

The authors propose the StressTest benchmark to evaluate the ability of Speech Language Models (SLMs) to understand the meaning of sentence stress. Findings indicate that existing models struggle to reason about speaker intent based on stress patterns. StresSLM, trained via the Stress-17k synthetic data pipeline, significantly outperforms frontier models on stress detection and reasoning tasks.

[b] = [d] − [t] + [p]: Self-supervised Speech Models Discover Phonological Vector Arithmetic

This work systematically demonstrates the existence of linear phonological feature vectors within the representation spaces of self-supervised speech models (S3M). These vectors satisfy word2vec-style vector arithmetic relationships, and their scaling correlates continuously with acoustic measurements.

MSU-Bench: Musical Score Understanding Benchmark

MSU-Bench is the first human-annotated benchmark for full musical score understanding, comprising 1,800 generative QA pairs from 150 works across four difficulty levels. Evaluations reveal significant deficiencies in LLMs/VLMs regarding score localization and hallucinations, while text input via ABC notation significantly mitigates these issues.

Browse all 71 Audio & Speech papers →

---

🔎 AIGC Detection (17)

Authorship Attribution in Multilingual Machine-Generated Texts

Existing research on machine-generated text authorship attribution (identifying which specific LLM or human produced a text) is almost entirely monolingual (primarily English). This paper is the first to formally define Multilingual Authorship Attribution (ML-MGT) and Cross-Lingual Authorship Attribution (CL-MGT). Through a systematic evaluation of 18 languages \(\times\) 8 generators (7 LLMs + human) using statistical methods, fine-tuned encoders, contrastive learning, and fine-tuned decoders, it finds that while fine-tuned/contrastive methods adapt well to multiple languages (best macro-F1 > 0.9), they degrade severely when transferring across different language families or writing systems, revealing the challenges of real-world multilingual scenarios.

ExaGPT: Example-Based Machine-Generated Text Detection for Human Interpretability

ExaGPT reframes the task of "determining whether a text is human-written or LLM-generated" as "identifying which side has more similar spans in a data store." By utilizing BERT embeddings, k-NN retrieval, and dynamic programming for optimal span segmentation, it provides interpretable evidence (most similar retrieved span examples) while improving accuracy by up to \(+37.0\) points over previous explainable detectors at 1% FPR.

Frame In, Frame Out: Measuring Framing Bias in LLM-Generated News Summaries

This paper proposes FIFO, a method that uses an LLM jury with expert calibration to measure whether LLM news summaries introduce framing bias on XSum at scale. It finds that several high-capacity models exhibit higher proportions of framed expressions compared to human summary baselines.

MASH: Evading Black-Box AI-Generated Text Detectors via Style Humanization

This paper proposes MASH (Multi-Stage Style Humanization Alignment), which utilizes a three-stage pipeline consisting of style-injection SFT → DPO alignment → inference-time refinement. By training a rewriter with only 0.1B parameters, it evades AI text detectors with an average attack success rate of 92% in black-box settings while maintaining excellent linguistic quality.

When Personalization Tricks Detectors: The Feature-Inversion Trap in Machine-Generated Text Detection

This work reveals the "Feature-Inversion Trap" of MGT detectors in personalized scenarios—where features distinguishing human-written text (HWT) and machine-generated text (MGT) in general domains invert in personalized domains, causing detector performance to collapse or even flip. The authors propose the StyloCheck framework to predict cross-domain performance changes by quantifying the detector's reliance on inverted features, achieving a prediction correlation of over 0.85.

AEGIS: A Holistic Benchmark for Evaluating Forensic Analysis of AI-Generated Academic Images

AEGIS is the first comprehensive benchmark for academic image forgery forensics, covering 7 major academic image categories with 39 subcategories, 4 forgery strategies (entirely fabricated, reference-based rewriting, local inpainting, and local editing), and 25 generative models. It proposes four tasks: forgery scope discrimination, text artifact recognition, manipulation type classification, and tampered pixel localization. Evaluating 25 MLLMs and 9 expert models reveals a structural complementarity: even GPT-5.1 achieves an overall score of only 48.80%, and expert models reach a pixel IoU of only 30.09%, highlighting that "generation evolves faster than forensics" and the trade-off between "MLLM reasoning vs. expert model sensitivity."

Beyond the Final Actor: Modeling the Dual Roles of Creator and Editor for Fine-Grained LLM-Generated Text Detection

Ours proposes RACE (Rhetorical Analysis for Creator-Editor Modeling), which utilizes Rhetorical Structure Theory (RST) to construct logic graphs for modeling the thought architecture of the "Creator," while extracting discourse unit-level features to capture the linguistic style of the "Editor." This enables four-way fine-grained LLM-generated text detection (Human-written / LLM-generated / LLM-polished Human / Human-rewritten LLM).

C-ReD: A Comprehensive Chinese Benchmark for AI-Generated Text Detection Derived from Real-World Prompts

C-ReD constructs a Chinese AI-generated text detection benchmark covering five writing scenarios, nine LLM generators, and real-world prompts. It demonstrates that detection difficulty depends heavily on the domain, generator, and prompt, while fine-tuning on C-ReD significantly enhances generalization to unseen models and external Chinese data.

Can AI-Generated Persuasion Be Detected? Persuaficial Benchmark and AI vs. Human Linguistic Differences

This paper introduces Persuaficial—a high-quality multilingual benchmark for AI-generated persuasive text covering six languages. It systematically evaluates the differences in automatic detection difficulty between LLM-generated and human-written persuasive texts, finding that subtle AI persuasion is significantly harder to detect than human persuasion (\(F_1\) drops by approximately 20%), whereas overly intensified persuasion is actually easier to identify.

DetectRL-X: Towards Reliable Multilingual and Real-World LLM-Generated Text Detection

DetectRL-X constructs a benchmark containing 3.456 million samples across multiple languages, domains, attacks, and lengths with parallel binary/ternary classification, proving that existing detectors still have significant robustness gaps in real-world multilingual and human-AI collaborative writing scenarios.

Browse all 17 AIGC Detection papers →

---

🤖 Robotics & Embodied AI (11)

GoViG: Goal-Conditioned Visual Navigation Instruction Generation via Multimodal Reasoning

GoViG proposes a new task of generating navigation instructions based only on initial and goal egocentric observations. It decomposes the task into two steps: "imagining intermediate frames then writing instructions." By jointly training Anole-7B with a dual objective of token-level MSE and label-smoothing CE, and employing one-pass or interleaved multimodal reasoning strategies, the method improves the BLEU-4 score from a baseline of 0.08 to 0.32, maintaining 0.27 on cross-domain real-world videos.

Breaking Down and Building Up: Mixture of Skill-Based Vision-and-Language Navigation Agents

SkillNav decomposes the vision-language navigation task into 5 atomic skills (Direction Adjustment, Vertical Movement, Stop, Landmark Identification, Area Identification) + 1 Temporal Order Planning skill. Each skill fine-tunes a DUET sub-agent using synthetic data, while a training-free VLM router performs temporal reordering + sub-goal localization + skill selection. It achieves SOTA generalization capabilities on GSA-R2R (Test-N-Scene SPL 48% vs. the previous highest of 43%).

Libra-VLA: Achieving Learning Equilibrium via Asynchronous Coarse-to-Fine Dual-System

Libra-VLA decomposes robot actions into a hybrid action space of "discrete macro-intent + continuous micro-pose." It utilizes System 2 (VLM + parallel coarse-action head) for low-frequency planning and System 1 (diffusion transformer + independent SigLIP encoder) for high-frequency refinement. Achieving true asynchronous execution via an intent buffer, it reaches a SoTA of 97.2% on LIBERO and 79.5% zero-shot on LIBERO-Plus (10% higher than the previous OpenVLA-OFT+).

Limited Linguistic Diversity in Embodied AI Datasets

This paper performs a systematic "linguistic diversity audit" on mainstream VLA training corpora (RT-1, BRIDGE, TacoPlay, Language Table, LIBERO). By quantifying lexical, semantic, and syntactic dimensions, it reveals that VLA data contains < 2% unique instructions, RT-1 has only 49 unique words in the entire corpus, and negation/conditional sentences account for < 1%. This "template-based poverty" compared to instruction-tuning corpora (OASST2 93%, Alpaca 99.8% unique) may be the root cause of VLA models' vulnerability to paraphrasing and generalization failures.

Cultivating Forensic Reasoning for Generalizable Multimodal Manipulation Detection

This paper proposes REFORM, shifting multimodal forgery detection from "direct label fitting" to "learning a verifiable forensic reasoning process." Through the ROM reasoning-annotated dataset, dual decoders, and GRPO training, REFORM achieves superior cross-domain generalization and interpretable detection results on ROM, DGM4, and MMFakeBench.

VLN-NF: Feasibility-Aware Vision-and-Language Navigation with False-Premise Instructions

This paper proposes the VLN-NF benchmark—the first task requiring VLN agents to identify false-premise instructions and output NOT-FOUND in 3D partially observable environments. It further introduces the REV-SPL evaluation metric and the ROAM two-stage hybrid framework, where ROAM achieves 6.1 REV-SPL, representing a 45% improvement over supervised baselines.

When Does Language Matter? Multilingual Instructions Reveal Step-wise Language Sensitivity in Vision-Language-Action Models

By translating the LIBERO robotic manipulation benchmark into ten languages, this paper systematically reveals for the first time that VLA models suffer a 30–50% drop in success rates under non-English instructions. It identifies that "linguistic influence is highly non-uniform across execution steps"—where only a few critical steps are sensitive to language but dominate failure cases. Based on this, a method for inference-time representation alignment specifically on these steps is proposed, significantly recovering multilingual performance.

GROKE: Vision-Free Navigation Instruction Evaluation via Graph Reasoning on OpenStreetMap

GROKE proposes evaluating navigation instructions without any vision by serializing OpenStreetMap (OSM) data into JSON and utilizing Gemini-3 Pro as a follower agent to execute instructions on the graph. Navigation metrics (Navigation Error / SR / SDTW) serve as proxies for instruction quality. Compared to heuristic baselines on Map2Seq, it reduces Navigation Error (NE) by 68.5%, and results show that NE is significantly correlated with human judgment of "instruction clarity" (\(r = -0.31, p < 0.01\)).

Ability-Oriented Failure Attribution for Vision-Language Navigation Agents

This paper addresses multi-level ability failures in embodied agents (specifically Vision-Language Navigation VLN agents) by proposing the CanTest framework. Through ability-oriented test oracles and failure attribution mechanisms, it precisely localizes specific ability defects (Perception/Memory/Planning/Decision-making) leading to task failure, discovering 23–34% more failure cases than existing methods.

ElasticFlow: One-Step Physics-Consistent Policy with Elastic Time Horizons for Language-Guided Manipulation

The paper proposes ElasticFlow, which replaces instantaneous velocity fields with MeanFlow (mean velocity fields) for learning language-conditioned robotic actions. By explicitly encoding control granularity using an "Elastic Time Horizon \(\Delta t=t-r\)", it achieves 1-NFE single-step inference (~71Hz) and outperforms OpenVLA and \(\pi_0\) on long-horizon tasks such as LIBERO-Long and CALVIN ABC-D.

Browse all 11 Robotics & Embodied AI papers →

---

🎮 Reinforcement Learning (46)

RL-PLUS: Countering Capability Boundary Collapse of LLMs in Reinforcement Learning with Hybrid-policy Optimization

RL-PLUS proposes a hybrid-policy optimization method that addresses external data distribution mismatch through Multiple Importance Sampling (MIS) and guides the model to learn low-probability but correct reasoning paths via the Exploration-based Advantage Function (EAF). It successfully breaks the capability boundary collapse caused by RLVR, achieving SOTA (average 53.4) across six mathematical reasoning benchmarks and consistent improvements across models by up to 69.2%.

AttnPO: Attention-Guided Process Supervision for Efficient Reasoning

Ours proposes AttnPO, a low-overhead process-supervised RL framework that leverages the model's intrinsic attention signals for step-level credit assignment. By identifying Key-Focus Heads (KFH) to distinguish between redundant and critical reasoning steps, AttnPO significantly reduces reasoning length while substantially improving accuracy.

CE-GPPO: Coordinating Entropy via Gradient-Preserving Clipping Policy Optimization in Reinforcement Learning

The CE-GPPO algorithm is proposed. By reintroducing gradient signals for low-probability tokens outside the PPO clipping interval through stop-gradient operations, it achieves fine-grained coordinated control of policy entropy and attains a better balance between exploration and exploitation.

A Goal Without a Plan Is Just a Wish: Efficient and Effective Global Planner Training for Long-Horizon Agent Tasks (EAGLET)

EAGLET decouples long-horizon agent tasks into "global planner + local executor" modules. It trains a plug-and-play planner through a two-step pipeline: "cold-start SFT with homologous consensus filtering" followed by "GRPO fine-tuning using executor capability gain as reward." It achieves new SOTA on three long-horizon benchmarks while reducing training costs to 1/8 of RL baselines.

Semantic-Space Exploration and Exploitation in RLVR for LLM Reasoning

This paper argues that the traditional token-level exploration-exploitation trade-off in RLVR is an artifact of measurement. It proposes decoupling exploration and exploitation in the latent semantic space using Effective Rank (ER) and its temporal derivatives (ERV/ERA). Based on this, the VERL method is designed to achieve simultaneous improvement in both, resulting in gains of up to 21.4% on benchmarks such as Gaokao Math.

Scaling Behaviors of LLM Reinforcement Learning Post-Training: An Empirical Study

This paper presents the first systematic study of scaling behaviors in LLM reinforcement learning (RL) post-training. Conducted on the Qwen2.5 series (0.5B-72B), the study reveals that performance follows a power-law relationship with training resources, and learning efficiency tends toward saturation as model scale increases.

LoVeC: Reinforcement Learning for Better Verbalized Confidence in Long-Form Generations

LoVeC trains LLMs to append a numerical <confidence> tag (0–10) after each sentence during long-form generation. Using GRPO (online, requiring an oracle fact-checker) or DPO (offline preference pairs), the model aligns these tags with factuality determined by GPT-4o. This enables single-pass decoding to output calibratable, machine-parseable confidence scores, outperforming the Prev. SOTA LUQ across Brier/ECE/Spearman metrics and achieving a 20x inference speedup.

d-TreeRPO: Towards More Reliable Policy Optimization for Diffusion Language Models

To address two major reliability bottlenecks in RL for Diffusion Language Models (dLLM)—sparse rewards and probability estimation bias—the authors propose d-TreeRPO. It organizes rollouts into a tree structure, calculating step-wise advantages bottom-up using verifiable rewards from leaf nodes. Simultaneously, it provides a theoretical proof that "higher model confidence leads to more accurate single-step forward probability estimation," and designs a time-scheduled self-distillation loss to sharpen the policy in later training stages. Tested on LLaDA-8B-Instruct, it achieves gains of +86.2% on Sudoku, +51.6% on Countdown, +4.5% on GSM8K, and +5.3% on Math500.

KnowRL: Exploring Knowledgeable Reinforcement Learning for Factuality

KnowRL integrates "atomic fact verification" as a process-level reward directly into the GRPO training loop, performing factual assessment on each step of the slow-thinking model's Chain-of-Thought (CoT). Simultaneously, it employs a "positive reward for refusal" strategy to teach the model to identify its own knowledge boundaries. This approach reduces the SimpleQA Incorrect Rate by 20.3% without compromising (and even slightly improving) reasoning capabilities like GPQA/AIME, while demonstrating cross-lingual transfer from English knowledge to Chinese QA.

NaviMaster: Learning a Unified Policy for GUI and Embodied Navigation Tasks

NaviMaster reformulates both GUI operations and embodied navigation into a unified MDP of "visual target localization + action execution." It trains a Qwen2.5-VL-7B policy using GRPO on mixed trajectories with distance-aware dense rewards, outperforming single-domain training and mainstream baselines in OOD GUI tasks, spatial affordance prediction, and ObjectNav.

Browse all 46 Reinforcement Learning papers →

---

🎁 Recommender Systems (22)

Bridging Language and Items for Retrieval and Recommendation: Benchmarking LLMs as Semantic Encoders

This paper introduces the Amazon Reviews 2023 large-scale dataset (570M reviews / 48M items) and constructs the BLaIR benchmark. Covering Sequential Recommendation, Collaborative Filtering, and Item Search (short and complex queries), the study benchmarks 11 top-tier LLMs as semantic encoders. It reveals that model rankings on BLaIR are almost uncorrelated with MTEB (Spearman -0.476), highlighting the unique requirements of recommendation scenarios for semantic encoders.

From Recall to Forgetting: Benchmarking Long-Term Memory for Personalized Agents

This paper proposes the Memora benchmark and the FAMA metric, extending long-term memory evaluation from shallow factual retrieval to memory consolidation and mutation handling across weeks to months, revealing systemic failures of existing LLMs and memory agents in handling frequent knowledge updates.

MemRec: Collaborative Memory-Augmented Agentic Recommender System

MemRec employs a lightweight LLM to specifically manage a dynamic "Collaborative Memory Graph" (connecting semantic memories of multiple users and items via interaction edges), and feeds distilled "collaborative facets" to a heavy-duty reasoning LLM for final recommendation. By utilizing a "Curate-then-Synthesize" denoising strategy and asynchronous \(O(1)\) label propagation updates, it achieves a relative H@1 improvement of +15% to +29% over the SOTA i2Agent across four benchmarks, with a significant +91.4% gain over Vanilla LLMs for sparse users.

Mirroring Users: Towards Building Preference-aligned User Simulator with User Feedback in Recommendation

The authors rewrite "user feedback logs" in recommendation systems into a unified simulation scenario of "User Memory + Exposure List" understandable by LLMs. They then generate explicit chain-of-thought decision processes as "clarifications" using the EKB consumer decision model. Through uncertainty decomposition and rejection sampling, 10K high-quality SFT/DPO data points are distilled, allowing a 3B Llama user simulator to outperform GPT-5 and Gemini-2.5-Flash in predicting real user behavior across 8 domains.

Culinary Crossroads: A RAG Framework for Enhancing Diversity in Cross-Cultural Recipe Adaptation

Authors observe that standard RAG "produces non-diverse outputs even when given diverse contexts" in creative tasks. They design CARRIAGE, a plug-and-play framework featuring query rewriting, diversity-aware MMR re-ranking, sliding-window dynamic context, and contrastive context injection. This framework effectively transfers "contextual diversity" to "output diversity," improving lexical/semantic/ingredient diversity and CultureScore in Spanish cross-national recipe adaptation, achieving Pareto efficiency compared to closed-book LLMs.

IceBreaker for Conversational Agents: Breaking the First-Message Barrier with Personalized Starters

This paper proposes IceBreaker, which addresses the "first-message barrier" for conversational agents through a two-step "handshake"—Resonance-aware Interest Distillation to capture trigger interests and Interaction-oriented Starter Generation coupled with Personalized Preference Alignment. In A/B testing on one of the world's largest conversational products, it increased active user days by +1.84‰ and click-through rate (CTR) by +94.25‰.

ReRec: Reasoning-Augmented LLM-based Recommendation Assistant via Reinforcement Fine-tuning

This paper proposes ReRec, a Reinforcement Fine-tuning (RFT) framework for recommendation assistants. It provides fine-grained reward signals through dual-graph augmented reward shaping, differentiated supervision of reasoning steps via Reasoning-Aware Advantage Estimation (RAAE), and dynamic adjustment of training difficulty via an online curriculum scheduler. ReRec enables LLMs to handle complex multi-step reasoning recommendation queries, significantly outperforming existing methods on the RecBench+ benchmark.

From Past To Path: Masked History Learning for Next-Item Prediction in Generative Recommendation

Proposes the Masked History Learning (MHL) training framework, which incorporates a masked history reconstruction auxiliary task into the autoregressive training of generative recommendation. Combined with an entropy-guided adaptive masking strategy and a curriculum learning scheduler, it shifts the model from merely predicting "what is next" to understanding "why this path was formed," significantly outperforming SOTA on three datasets.

HARPO: Hierarchical Agentic Reasoning for User-Aligned Conversational Recommendation

Proposes the HARPO framework, which redefines conversational recommendation as a structured decision-making problem optimized for recommendation quality. Through four components—hierarchical preference learning, value-network-guided tree search reasoning, virtual tool operations, and multi-agent refinement—it significantly outperforms existing methods on the ReDial, INSPIRED, and MUSE benchmarks.

What Makes LLMs Effective Sequential Recommenders? A Study on Preference Intensity and Temporal Context

This paper reveals that existing LLM-based recommendation systems lose critical information—preference intensity and temporal context—due to binary preference modeling. It proposes the RecPO framework, which incorporates these two factors into preference optimization through an adaptive reward margin, significantly outperforming baselines like S-DPO across five datasets.

Browse all 22 Recommender Systems papers →

---

🔄 Self-Supervised Learning (1)

LLMSurgeon: Diagnosing Data Mixture of Large Language Models

LLMSurgeon formalizes the question "what data was this LLM trained on" as Data Mixture Surgery. By using the soft confusion matrix of a proxy classifier to invert the domain distribution within generated text, it estimates pre-training data mixture proportions while only requiring access to model outputs.

---

🔗 Causal Inference (7)

Parallel Universes, Parallel Languages: A Comprehensive Study on LLM-based Multilingual Counterfactual Example Generation

This paper systematically investigates the multilingual counterfactual generation capabilities of LLMs across six languages. By comparing direct generation and translation-based paths, it finds that the translation path yields higher label flip rates but requires more edits. It identifies four common error patterns and validates that multilingual counterfactual data augmentation outperforms cross-lingual augmentation, particularly for low-resource languages.

Better and Worse with Scale: How Contextual Entrainment Diverges with Model Size

This paper establishes the first scaling laws for the "contextual entrainment effect," discovering that larger models are more resistant to false information in semantic contexts (negative exponent) but more prone to copying irrelevant tokens in non-semantic contexts (positive exponent), revealing opposing scaling behaviors between semantic filtering and mechanical copying functions.

ClimateCause: Complex and Implicit Causal Structures in Climate Reports

ClimateCause constructs the first expert-annotated dataset for complex and implicit causal structures in climate reports (874 causal relations), supporting nested causality, multi-event decomposition, correlation direction, and spatio-temporal context labeling. It proposes a readability metric based on causal graph semantic complexity, with LLM benchmarking revealing that causal chain reasoning remains a significant challenge.

Function Words as Statistical Cues for Language Learning

The authors use Universal Dependencies corpora across 186 languages to demonstrate that three distributional properties—"high frequency + syntactic predictability + phrase boundary alignment"—are cross-linguistically universal. Simultaneously, they construct seven counterfactual variants of English to train GPT-2 small, proving that transformer learners perform best only when all three properties are satisfied. They identify a Goldilocks effect: function words must be both sufficiently frequent and sufficiently diverse to be both reliable and discriminative.

Evaluating Counterfactual Strategic Reasoning in Large Language Models

This paper evaluates the strategic adaptation capabilities of LLMs using label perturbations, payoff perturbations, and joint counterfactual versions of the Repeated Prisoner's Dilemma and Rock-Paper-Scissors. It finds that while many models appear proficient in familiar games, they continue to apply templated strategies even after payoff structures are altered.

iTAG: Inverse Design for Natural Text Generation with Accurate Causal Graph Annotations

The iTAG framework is proposed, which utilizes a three-stage inverse design pipeline (parameterized causal graph construction → CoT-based concept assignment → structure-preserving text generation) to generate data with both extremely high causal graph annotation accuracy and text naturalness. This serves as a practical substitute for real annotated data in benchmarking text causal discovery algorithms.

Learning Invariant Modality Representation for Robust Multimodal Learning from a Causal Inference Perspective

This paper proposes CmIR (Causal Modality Invariant Representation learning), which explicitly disentangles each modality into causal invariant representations and environment-specific spurious representations based on causal inference theory. Through an elegant objective function combining invariance constraints, mutual information constraints, and reconstruction constraints, it ensures that invariant representations maintain stable predictive relationships across environments. It achieves SOTA performance in multimodal sentiment, humor, and sarcasm detection, particularly excelling in OOD and noisy scenarios.

---

🔬 Interpretability (63)

Flattery in Motion: Benchmarking and Analyzing Sycophancy in Video-LLMs

The authors construct the first Video-LLM sycophancy benchmark, ViSE (367 videos / 6,367 multiple-choice questions / 7 categories of sycophantic scenarios). They systematically reveal the universal phenomenon across 9 SOTA Video-LLMs where "models abandon visual evidence to cater to users" and propose two training-free mitigation methods: (i) key-frame selection reduces sycophancy by up to 22.01% (and is proven via attention analysis to eliminate "first-frame bias" and "middle-layer instability"); (ii) representation steering reduces MSS by an average of 35.69% in the most difficult scenarios, bringing MSS close to 0 across 5 categories on LLaVA-OneVision.

Interpretable Traces, Unexpected Outcomes: Investigating the Disconnect in Trace-Based Knowledge Distillation

By constructing a verifiable intermediate reasoning chain dataset using a rule-based problem decomposition method, this work reveals that the semantic correctness of CoT reasoning chains is unreliably correlated with final answer accuracy (correct chains lead to correct answers only 28% of the time). Furthermore, the most interpretable reasoning chains are not the most performance-enhancing—lengthy R1 chains perform best but are rated as the least interpretable by users.

Curing "Miracle Steps" in LLM Mathematical Reasoning with Rubric Rewards

This paper identifies the widespread presence of "Miracle Steps"—phenomena where reasoning chains leap to the correct answer without derivation—in current LLM mathematical reasoning. It proposes the Rubric Reward Model (RRM), a process-based reward function using problem-specific scoring rubrics. During RL training, RRM significantly reduces Miracle Steps by 71% and improves the Verified Pass@1024 on AIME2024 from 26.7% to 62.6%.

The Impact of Off-Policy Training Data on Probe Generalisation

This paper systematically compares the impact of four types of training data—on-policy natural, on-policy incentivised, on-policy prompted, and off-policy—on the generalization of LLM activation probes. It finds that probes for behaviors visible on the text surface are robust, while "intentional" behaviors like deception, sycophancy, and sandbagging are highly susceptible to domain shifts. The authors propose using an on-policy incentivised test set to predict generalization failures in real-world monitoring.

Linear Probes Detect Task Format, Not Reasoning Mode in Language Model Hidden States

This paper uses probes, residual de-confounding, trace-anchor, and causal steering experiments on Qwen3-14B to demonstrate that while linear probes appear to distinguish deductive, inductive, and abductive reasoning with 100% accuracy, they actually detect data source and task format rather than reasoning modes within hidden states.

Sparse Feature Coactivation Reveals Causal Semantic Modules in Large Language Models

This paper automatically discovers semantic modules representing concepts and relations in LLMs via cross-layer co-activation graphs of SAE features from few prompts. It demonstrates that ablating or amplifying these modules allows for predictable manipulation of relational reasoning in Gemma 2 2B, achieving success rates up to 98% in single concept/relation scenarios and 90% in compositional scenarios.

Compositional Steering of Large Language Models with Steering Tokens

This paper proposes compositional steering tokens, which compress behavior instructions into embedding vectors in the input space via self-distillation. By training a dedicated compositional token <and> to capture the universal concept of "composition," the method demonstrates strong generalization capabilities across unseen behavior combinations, unseen behaviors, and an unseen number of combined behaviors.

Crosscoding Through Time: Tracking Emergence & Consolidation Of Linguistic Representations Throughout LLM Pretraining

By training a shared feature dictionary across multiple pretraining checkpoints of the same LLM using a sparse crosscoder, this work proposes the Relative Indirect Effect (RelIE) to measure how the causal importance of individual features "emerges, persists, or vanishes" over token counts. This study provides the first observation of the concept-level evolutionary trajectory in Pythia, OLMo, and BLOOM—from "specific subword detectors" to "internalized abstract syntactic/cross-lingual detectors."

MINED: Probing and Updating with Multimodal Time-Sensitive Knowledge for Large Multimodal Models

The authors propose MINED—the first evaluation benchmark for multimodal time-sensitive knowledge, consisting of 2,104 \((subject, hypernym, property, attribute-list)\) quadruplets across 11 sub-tasks in 6 dimensions (Cognition / Awareness / Trustworthiness / Understanding / Reasoning / Robustness), totaling 4,208 questions. Evaluation of 15 LME's shows Gemini-2.5-Pro achieving the highest average \(\text{CEM}=63.07\) but still lacking ~15% of the knowledge; further tests using knowledge editing methods like FT-LLM / IKE effectively update outdated knowledge in LLaVA-v1.5 and Qwen-VL under single editing, but performance significantly degrades under lifelong editing (FT-LLM drops by 43.2% on average).

Probing Semantic Alignment, Lexical Invariance, and Syntactic Influence in LLM Metaphor Processing

This is a diagnostic analysis paper: instead of competing for performance, the authors probe LLM metaphor processing from three complementary dimensions—semantic property alignment, lexical invariance, and syntactic influence. They find that "high scores on metaphor benchmarks" may stem from heterogeneous shallow signals (semantic drift + stable lexical anchors + heuristic sensitivity to syntactic irregularities) rather than robust integrated semantic understanding.

Browse all 63 Interpretability papers →

---

📦 Model Compression (59)

Stable On-Policy Distillation through Adaptive Target Reformulation

This paper proposes Veto, a target-level reformulation method that stabilizes on-policy knowledge distillation by constructing a teacher-student geometric bridging distribution in logit space. A single parameter \(\beta\) simultaneously acts as an adaptive gradient vetoer in forward KL (suppressing harmful gradients from low-confidence tokens) and a decisiveness knob in reverse KL (balancing reward-driven behavior and output diversity). It achieves a 9.2% improvement over SFT on GSM8K.

Enabling Agents to Communicate Entirely in Latent Space

This paper proposes Interlat, a framework that enables LLM agents to communicate entirely in latent space. The sender transmits the final layer's hidden states as a continuous representation of "thought." The receiver interprets these latent messages via a communication adapter and further compresses them to just 8 tokens through latent space reasoning while maintaining competitive performance, achieving a communication speedup of up to 24×.

Find Your Optimal Teacher: Personalized Data Synthesis via Router-Guided Multi-Teacher Distillation

Ours propose PerSyn (Personalized data Synthesis), which utilizes a "Route-then-Generate" paradigm where a router assigns the optimal teacher model for each prompt. By considering both student learnability and teacher response quality, this approach is more efficient and effective than the traditional "Generate-then-Select" paradigm, consistently surpassing all baselines in both instruction tuning and mathematical reasoning scenarios.

FastKV: Decoupling of Context Reduction and KV Cache Compression for Prefill-Decoding Acceleration

This paper proposes FastKV, which decouples context reduction (Token-Selective Propagation during prefill) from KV cache compression (layer-wise KV retention during decoding). It achieves 1.82× prefill and 2.87× decoding speedup on LLaMA-3.1-8B-Instruct, while maintaining accuracy within a 1% drop on LongBench.

Not All Directions Matter: Towards Structured and Task-Aware Low-Rank Model Adaptation

Ours proposes StructLoRA: it utilizes an Information Bottleneck (IB) to filter out task-irrelevant directions in low-rank updates and employs a Graph Neural Network (GNN) during training to coordinate LoRA updates across different layers. It consistently outperforms LoRA, AdaLoRA, DoRA, and Sensitivity-LoRA across language, vision, and multimodal tasks while maintaining zero additional inference overhead.

Reason Only When Needed: Efficient Generative Reward Modeling via Model-Internal Uncertainty

The E-GRM framework is proposed to estimate uncertainty using the convergence behavior of model-internal parallel decoding. CoT reasoning is triggered only when necessary, and a discriminative scorer trained with hybrid loss evaluates reasoning path quality. This achieves SOTA performance on multiple reward model benchmarks while reducing inference latency by 62%.

GlimpRouter: Efficient Collaborative Inference by Glimpsing One Token of Thoughts

This paper proposes GlimpRouter: in step-level LRM collaborative inference, the small model first decodes only the "first token" of each reasoning step. Its entropy \(\mathbf{H}_{\text{init}}\) is used to estimate step difficulty; if low, the small model continues; if high, it switches to the large model. It is training-free, requires no large model verifier, achieves +10.7% accuracy with −25.9% latency improvement on AIME25 compared to a standalone large model, and is orthogonally compatible with token-level Speculative Decoding.

DeepPrune: Parallel Scaling without Inter-Trace Redundancy

This paper proposes DeepPrune, which trains a specialized judge model to predict answer equivalence from partial reasoning traces. By combining this with an online greedy clustering algorithm to dynamically prune redundant parallel CoT paths, it reduces token consumption by 65.73%-88.50% while maintaining competitive accuracy (within 3 percentage points).

The Pitfalls of KV Cache Compression

This paper identifies that KV cache compression leads to selective forgetting and system prompt leakage in multi-instruction prompts. The issue stems from uneven eviction across different instructions and the erroneous deletion of critical tokens. The authors propose two simple modifications—whitelist retention and fair eviction—to significantly reduce leakage and stabilize instruction following.

LLM Prompt Duel Optimizer: Efficient Label-Free Prompt Optimization

This paper formalizes label-free prompt optimization as a dueling bandit problem and proposes the Prompt Duel Optimizer (PDO). By utilizing Double Thompson Sampling to efficiently select the most informative prompt pairs for comparison and combining it with a top-performer mutation strategy to expand the search space, PDO identifies stronger prompts with fewer judge calls on BBH and MS MARCO.

Browse all 59 Model Compression papers →

---

🕸️ Graph Learning (24)

From Nodes to Narratives: Explaining Graph Neural Networks with LLMs and Graph Context

Ours proposes Gspell, a lightweight post-hoc explanation framework that projects GNN node embeddings into the LLM embedding space and constructs hybrid prompts (soft prompts + text). This enables LLMs to directly reason over GNN internal representations to generate natural language explanations and explanatory subgraphs, achieving a favorable balance between faithfulness and interpretability on Text-Attributed Graphs (TAGs).

MegaRAG: Multimodal Knowledge Graph-Based Retrieval Augmented Generation

MegaRAG utilizes MLLMs to perform parallel entity-relation extraction from each page of long documents, merging them into a Multimodal Knowledge Graph (MMKG). It uses a "subgraph-guided" refinement round to complete cross-modal and cross-page relations. Combined with dual-path retrieval and two-stage answer generation, it significantly outperforms GraphRAG/LightRAG/VisRAG, achieving 64.85% accuracy on SlideVQA(2k) (compared to the best baseline of 27.66%).

ARK: Answer-Centric Retriever Tuning via KG-augmented Curriculum Learning

The ARK framework is proposed, which filters positive samples through a three-dimensional answer sufficiency score (Forward + Backward + Retriever alignment) and utilizes LLM-constructed Knowledge Graphs (KG) to generate hard negative samples of progressive difficulty for curriculum contrastive learning. It achieves an average F1 improvement of 14.5% across 10 datasets.

AgentGL: Towards Agentic Graph Learning with LLMs via Reinforcement Learning

AgentGL is proposed as the first reinforcement learning-based Agentic Graph Learning (AGL) framework. It enables LLM agents to autonomously navigate Text-Attributed Graphs (TAGs) using graph-native search tools, achieving absolute accuracy improvements of up to 17.5% in node classification and 28.4% in link prediction.

LegalGraphRAG: Multi-Agent Graph Retrieval-Augmented Generation for Reliable Legal Reasoning

LegalGraphRAG constructs a hierarchical legal graph using fact, ontology, and rule graphs, and utilizes a Researcher-Auditor-Adjudicator multi-agent workflow for retrieval, verification, and adjudication, improving accuracy and evidence traceability in legal judgment generation.

Autonomous Knowledge Graph Exploration with Adaptive Breadth-Depth Retrieval

This paper proposes ARK: a training-free Knowledge Graph (KG) retrieval agent that exposes only two minimal tools—"global lexical search" and "single-hop neighbor expansion"—allowing the LLM to autonomously switch between breadth and depth without seed nodes or fixed hop counts. It pushes the average Hit@1 on three STaRK graphs to 59.1%, achieving up to a 31.4% improvement over training-free baselines, and enables label-free strategy distillation into Qwen3-8B.

Collaboration of Fusion and Independence: Hypercomplex-driven Robust Multi-Modal Knowledge Graph Completion

M-Hyper encodes multi-modal knowledge graph entities into four orthogonal bases of a biquaternion, carrying three independent modalities (Structure/Visual/Textual) and one fused modality respectively. Through the Hamilton product, it simultaneously achieves "modal independence preservation" and "pairwise sufficient interaction," outperforming 18 baselines on DB15K, MKG-W, and MKG-Y datasets with minimal memory usage and training time.

ComplianceNLP: Knowledge-Graph-Augmented RAG for Multi-Framework Regulatory Gap Detection

ComplianceNLP is an end-to-end financial regulatory compliance system that constructs a knowledge graph from 12,847 SEC / MiFID II / Basel III regulations to enhance RAG retrieval. Combined with LEGAL-BERT-based multi-task obligation extraction and threshold-scored gap analysis, it outperforms GPT-4o+RAG by 3.5 points on RegObligation / GapBench with an 87.7 F1. It achieves \(2.8\times\) inference acceleration via domain-specific knowledge distillation + Medusa speculative decoding. Over four months of parallel operation, it processed 9,847 updates, reaching a 96.0% recall rate and a 3.1× increase in analyst efficiency.

TagRAG: Tag-guided Hierarchical Knowledge Graph Retrieval-Augmented Generation

TagRAG replaces expensive entity community partitioning and global graph summarization in GraphRAG with "object tags + domain tag chains." While significantly reducing construction and retrieval costs, it maintains global knowledge integration capabilities and achieves higher win rates than NaiveRAG, GraphRAG, LightRAG, and MiniRAG on four UltraDomain datasets using the small model Qwen3-4B.

AutoPKG: An Automated Framework for Dynamic E-commerce Product-Attribute Knowledge Graph Construction

AutoPKG is proposed as a multi-agent LLM framework for automatically constructing a Product-Attribute Knowledge Graph (PKG) from multimodal e-commerce content. Using a Type Induction Agent, Attribute Key Discovery Agent, Attribute Value Extraction Agent, and a centralized KGD decision agent, it enables continuous evolution and normalization of a dynamic ontology. It achieves 0.953 WKE (Type) and 0.724 WKE (Key) on the Lazada dataset, with a 7.89% recommendation GMV gain in online A/B testing.

Browse all 24 Graph Learning papers →

---

📈 Time Series (8)

STReasoner: Empowering LLMs for Spatio-Temporal Reasoning in Time Series via Spatial-Aware Reinforcement Learning

STReasoner utilizes Network SDEs to synthesize spatio-temporal time series data with graph structures and textual semantics. By integrating a time-series encoder, a three-stage training pipeline, and a spatial-aware S-GRPO, the model learns to perform explicit reasoning based on temporal dynamics and spatial dependencies.

Time-RA: Towards Time Series Reasoning for Anomaly Diagnosis with LLM Feedback

Defining the new Time-RA task, this work upgrades time series anomaly detection from binary classification to generative reasoning diagnosis (detection + classification + root cause explanation). It constructs RATs40K, the first multimodal benchmark comprising ~40,000 samples across 10 domains and 20 anomaly types, validating the feasibility of this paradigm through an AI feedback labeling pipeline and LLM fine-tuning.

TSAQA: Time Series Analysis Question And Answering Benchmark

TSAQA is a unified time series question answering benchmark: it casts 6 types of temporal analysis tasks (anomaly detection, classification, representation, comparison, data transformation, and temporal relations) into 3 closed-form question types (true/false TF, multiple-choice MC, and the newly proposed puzzling PZ). Across 13 domains with 210k samples, LLMs and time series foundation models are evaluated under a unified zero-shot protocol—results indicate that even the strongest commercial model, Gemini-2.5-Flash, achieves an average accuracy of only 65.08%, leaving significant room for improvement.

A Unified Framework for Modeling Heterogeneous Financial Data via Dual-Granularity Prompting

The FinLangNet framework is proposed, utilizing a dual-module architecture (DeepFM for static features and a Transformer with a dual-granularity prompting mechanism for temporal behavior) to achieve multi-scale credit risk prediction. Its deployment on the Didi Finance platform resulted in a 6.3pp increase in KS and a 9.9% reduction in the bad debt rate.

Test of Time: Rethinking Temporal Signal of Benchmark Contamination

This paper demonstrates that "performance decay after cutoff" is not robust evidence of benchmark contamination: as long as the same set of source documents is converted from original fill-in-the-blank questions to LLM-rephrased questions, the temporal decay signal changes significantly or even disappears.

STK-Adapter: Incorporating Evolving Graph and Event Chain for Temporal Knowledge Graph Extrapolation

This paper proposes STK-Adapter, which embeds three MoE modules in each layer of a Large Language Model (LLM)—ST-MoE for capturing spatio-temporal structures, EA-MoE for modeling event chain semantics, and CMA-MoE for deep cross-modal alignment. It addresses the issues of spatio-temporal information loss and layer-wise dilution caused by shallow alignment between TKG embeddings and LLMs, significantly outperforming SOTA on four benchmark datasets.

ODTQA-FoRe: An Open-Domain Tabular Question Answering Dataset for Future Data Forecasting and Reasoning

ODTQA-FoRe introduces an open-domain tabular question answering task focused on future numerical forecasting and post-forecast reasoning. It provides the TimeFore three-agent framework, which chains table retrieval, SQL data acquisition, specialized time-series forecasting, and answer normalization into an evaluable baseline.

Temporal Leakage in Search-Engine Date-Filtered Web Retrieval: A Retrospective Forecasting Case Study

This paper systematically audits the date filters of Google and DuckDuckGo, finding that search engine date filtering fails significantly in retrospective forecasting (RF) evaluations—\(71\%\) (Google) and \(81\%\) (DuckDuckGo) of questions contain at least one page with major post-cutoff information leakage, causing prediction Brier scores to artificially drop from \(0.24\) to \(0.10\).

---

🩺 Medical LLM (47)

CURE-Med: Curriculum-Informed Reinforcement Learning for Multilingual Medical Reasoning

The authors construct CureMed-Bench, a medical reasoning dataset covering 13 languages (including low-resource languages like Amharic, Yoruba, and Swahili) with 15,774 open-ended questions. They propose Cure-Med: a two-stage "code-switching aware SFT + curriculum GRPO" framework that jointly optimizes reasoning correctness and language consistency. At 7B, it achieves a language consistency/logical accuracy of 85.21% / 54.35%, and at 32B, it reaches 94.96% / 70.04%.

HeteroRAG: A Heterogeneous Retrieval-Augmented Generation Framework for Medical Vision Language Tasks

HeteroRAG constructs the MedAtlas knowledge base with 2.7 million image-text pairs and five types of corpora. It decomposes medical multimodal RAG into three components—ModCLIPs trained by modality to retrieve reports, MQG generating customized queries per corpus to retrieve documents, and HKPT preference fine-tuning to align cross-modality and multi-source knowledge—enabling a 7B model to consistently outperform open-source Med-LVLMs with 4-5× its parameters across 11 datasets.

Language Reconstruction with Brain Predictive Coding from fMRI Data

Ours proposes PredFT, an end-to-end fMRI-to-Text decoding model that integrates a main network (language decoding) and a side network (brain predictive coding representation). By extracting forward-looking semantic representations from predictive brain regions (PTO areas) and fusing them into the decoding process, PredFT achieves a BLEU-1 of 34.95% (Sub-1) on the LeBel dataset, a Gain of 7.84 percentage points compared to the strongest baseline MapGuide.

Inflated Excellence or True Performance? Rethinking Medical Diagnostic Benchmarks with Dynamic Evaluation

This paper proposes DyReMe, a dynamic medical diagnostic evaluation framework. It utilizes the DyGen module to generate brand-new diagnostic cases incorporating clinical distractors such as differential diagnoses and misdiagnosis factors. Through the EvalMed module, LLMs are evaluated across four dimensions—Accuracy, Veracity, Helpfulness, and Consistency—revealing that existing static benchmarks overestimate the diagnostic capabilities of LLMs. For instance, GPT-5's accuracy dropped by 8.25% on DyReMe, and 12 LLMs all exhibited significant deficiencies in trustworthiness.

Beyond the Individual: Virtualizing Multi-Disciplinary Reasoning for Clinical Intake via Collaborative Agents

The proposed Aegle framework virtualizes Multi-Disciplinary Teams (MDT) through a graph-structured multi-agent architecture. By introducing decoupled parallel reasoning and dynamic topology into the clinical intake process, it outperforms SOTA models on 53 metrics across 24 clinical departments.

Calibrated? Not for Everyone: How Sexual Orientation and Religious Markers Distort LLM Accuracy and Confidence in Medical QA

Ours investigates how social identity markers (sexual orientation and religious beliefs) distort the accuracy and confidence calibration of LLMs in medical QA. It is found that "homosexual" markers consistently lead to performance degradation and calibration crises across 9 LLMs, and intersectional identities produce non-additive, specific harm.

"Excuse Me, May I Say Something…" CoLabScience: A Proactive AI Assistant for Biomedical Discovery

CoLabScience utilizes the PULI (Positive-Unlabeled Learning for Intervention) framework to train an LLM assistant capable of proactively deciding when and how to intervene in biomedical team discussions. It leverages GRPO and an RL coordinator to automatically identify optimal intervention timings and generate scientific suggestions from streaming dialogues.

Faithfulness vs. Safety: Evaluating LLM Behavior Under Counterfactual Medical Evidence

This paper constructs the MedCounterFact dataset—systematically replacing interventions in clinical trials with nonsense words, medical terms, non-medical objects, and toxic substances. It finds that leading LLMs exhibit nearly unconditional compliance with the context in the face of counterfactual medical evidence, confidently providing answers even when "evidence" suggests heroin or mustard gas is effective, revealing a severe lack of defined boundaries between faithfulness and safety.

HypEHR: Hyperbolic Modeling of Electronic Health Records for Efficient Question Answering

This paper proposes HypEHR, a Lorentz hyperbolic model with only 22M parameters. It embeds medical codes, visit records, and questions into hyperbolic space and aligns them with the ICD ontology structure via hierarchy-aware regularization, achieving performance close to LLM-based methods on the MIMIC-IV EHR-QA task.

MARCH: Multi-Agent Radiology Clinical Hierarchy for CT Report Generation

This paper proposes MARCH, a multi-agent framework that simulates the hierarchical collaboration of radiology Residents, Fellows, and Attending physicians. Through a three-stage process (initial drafting, retrieval-augmented revision, and consensus-driven finalization), it generates CT reports. On the RadGenome-ChestCT dataset, it achieves a CE-F1 of 0.399, representing a 57.7% improvement over the best baseline, Reg2RG (0.253).

Browse all 47 Medical LLM papers →

---

🧬 Computational Biology (5)

ToxReason: A Benchmark for Mechanistic Chemical Toxicity Reasoning via Adverse Outcome Pathway

This paper introduces ToxReason, a benchmark for mechanistic chemical toxicity reasoning based on the Adverse Outcome Pathway (AOP) framework. It integrates drug-target experimental data with toxicity labels, requiring models to reason from Molecular Initiating Events (MIE) to organ-level Adverse Outcomes (AO). A 4B model trained with GRPO reinforcement learning outperforms large models like GPT-5 in both toxicity prediction (F1 71.4%) and reasoning quality.

AROMA: Augmented Reasoning Over a Multimodal Architecture for Virtual Cell Genetic Perturbation Modeling

The AROMA framework is proposed, which integrates text evidence, knowledge graph topology, and protein sequence features in a multimodal architecture. Combined with a two-stage training strategy (SFT + GRPO), it achieves interpretable and precise genetic perturbation effect prediction.

ProtoCycle: Reflective Tool-Augmented Planning for Text-Guided Protein Design

ProtoCycle proposes a reflective agent framework that uses an LLM as a planner combined with a lightweight tool environment for text-guided protein sequence design. It replaces one-shot text-to-sequence generation with a multi-round "plan-tool-evaluate-reflect" cycle. On Mol-Instructions, it improves ProTrek to 14.681 and Retrieval to 0.936, achieving language alignment that nears or exceeds specialized protein design models using only ~2,000 SFT trajectories and online RL.

BioTool: A Comprehensive Tool-Calling Dataset for Enhancing Biomedical Capabilities of Large Language Models

BioTool constructs an instruction fine-tuning dataset consisting of 7,040 human-verified "query–API call" pairs covering 34 commonly used tools from three major biomedical databases: NCBI, Ensembl, and UniProt. After fine-tuning 4B-scale open-source LLMs with this data, the tool-calling quality exceeds commercial models such as GPT-5.1, Gemini-3 Pro, and Claude-4.5-Sonnet by over 15%.

ChemAmp: Amplified Chemistry Tools via Composable Agents

This paper proposes the "Tool Amplification" paradigm (distinct from traditional tool orchestration). Through the ChemAmp framework, chemistry-specific tools (UniMol2, Chemformer, etc.) are treated as composable building blocks to dynamically construct task-specific super-agents. It outperforms specialized models and general LLMs on four core chemistry tasks, including molecular design and reaction prediction, while reducing inference token costs by 94%.

---

👥 Social Computing (45)

Beyond the Crowd: LLM-Augmented Community Notes for Governing Health Misinformation

The authors perform an empirical analysis of 30.8K health-related Community Notes from X, revealing systematic slow-response issues: a median delay of 17.6 hours for the first helpful verdict and 87.9% of notes remaining unrated. They propose the CrowdNotes+ framework, utilizing (1) Evidence Augmentation and (2) Utility-Guided Automation modes for LLM-generated notes, paired with a "Relevance → Correctness → Helpfulness" three-stage evaluation. 15 LLMs on the new HealthNotes benchmark significantly outperform the 73.19% helpfulness of human notes (with the o3 model reaching 81.15%).

Justice in Judgment: Unveiling (Hidden) Bias in LLM-assisted Peer Reviews

The authors systematically audit LLM peer review bias across 9 LLMs using a counterfactual evaluation that "modifies author metadata without changing paper content." They find all models exhibit significant favoritism toward Ranked-Stronger (RS) institutions and higher tolerance for senior PIs and prolific authors. Crucially, even when models appear neutral in hard ratings, soft ratings (expected scores based on token probabilities) reveal much stronger hidden bias, highlighting an alignment failure where "alignment masks rather than eliminates preferences."

Bayesian Social Deduction with Graph-Informed Language Models

This paper proposes GRAIL (Graph Reasoning Agent Informed through Language), a hybrid reasoning framework that externalizes probabilistic reasoning to a factor graph model while utilizing LLMs for language understanding and interaction. GRAIL defeated human players for the first time in the social deduction game Avalon (67% win rate), with resource consumption significantly lower than large-scale reasoning models.

VeriTaS: The First Dynamic Benchmark for Multimodal Automated Fact-Checking

VeriTaS utilizes a quarterly updated seven-stage automated pipeline to transform real-world multilingual image-text-video claims from professional fact-checking organizations into a standardized, interpretable, and evaluable multimodal fact-checking benchmark. It demonstrates that the strongest current multimodal models still fall significantly short of reliable AFC.

Inertia in Moral and Value Judgments of Large Language Models

This paper systematically measures "Value Inertia" across 7 mainstream LLMs using a "Large-scale random persona × Moral/Value questionnaire" paradigm. It finds highly stable inertia in the Harm/Fairness dimensions—where personas struggle to shift the model's response direction—and introduces two quantifiable metrics, Inertia Index and Steerability, to reveal that these preferences are unevenly distributed and aligned with safety training objectives.

Is this chart lying to me? Automating the detection of misleading visualizations

Proposes the Misviz (2,604 real-world misleading visualizations) and Misviz-synth (57,665 synthetic visualizations) benchmarks covering 12 misleading types. Systematically evaluates the performance of MLLMs, rule checkers, and image classifiers in detecting misleading charts, revealing that this task remains highly challenging.

Who Gets Which Message? Auditing Demographic Bias in LLM-Generated Targeted Text

This paper provides the first systematic analysis of bias in LLMs when generating targeted messages under demographic conditions. Introducing the Persuasion Bias Index (PBI), the study finds that GPT-4o, Llama, and Mistral employ more aggressive persuasion strategies for men and younger audiences in climate communication, with contextual prompts systematically amplifying these disparities.

Among Us: Language of Conspiracy Theorists on Mainstream Reddit

Analyzing 10 years of longitudinal data from 510 million Reddit comments, the study finds that users active in conspiracy communities exhibit detectable unique linguistic patterns even in mainstream communities (average 87% classification accuracy). However, these patterns are highly dependent on community context, with community-specific models outperforming global models by up to 17 percentage points.

MM-StanceDet: Retrieval-Augmented Multi-modal Multi-agent Stance Detection

The authors reconstruct multimodal stance detection into a 4-stage multi-agent pipeline: CLIP retrieval of similar samples providing few-shot CoT, three expert agents (text/image/cross-modal conflict) for analysis, three debater agents (pro/con/neutral) for debating, and a final adjudicator agent for self-reflection and labeling. Across five datasets, it outperforms strong baselines including GPT-4V, TMPT, and MV-Debate in both in-target and zero-shot settings.

Prompt-Level Distillation: A Non-Parametric Alternative to Model Fine-Tuning for Efficient Reasoning

This paper proposes Prompt-Level Distillation (PLD), which extracts, clusters, and de-conflicts reasoning patterns from a teacher model on training samples to construct a system prompt for the student model. This significantly enhances the reasoning and classification capabilities of small models without updating parameters.

Browse all 45 Social Computing papers →

---

🛡️ AI Safety (5)

OmniCompliance-100K: A Multi-Domain Rule-Grounded Real-World Safety Compliance Dataset

This paper constructs OmniCompliance-100K, the first large-scale, multi-domain safety compliance dataset grounded in real-world cases. It contains 12,985 human-curated regulatory/policy rules and 106,009 real-world compliance cases collected via web search agents, covering nine domains such as AI safety, data privacy, finance, and healthcare. Extensive benchmarking reveals systemic shortcomings in the safety compliance capabilities of current LLMs.

On the (In-)Security of the Shuffling Defense in the Transformer Secure Inference

This paper demonstrates that the commonly used "expose intermediate activations after shuffling" defense in Transformer secure inference is insecure. It proposes an attack that first aligns activations under different random permutations and then solves linear equations to extract weights. The attack recovers approximately usable model weights for Pythia-70m and GPT-2 with a query cost of approximately $1.

Reverse Constitutional AI: A Framework for Controllable Toxic Data Generation via Probability-Clamped RLAIF

This paper proposes Reverse Constitutional AI (R-CAI), which synthesizes automated, controllable, and multi-dimensional adversarial toxic data by inverting the principles of Constitutional AI into a "Toxic Constitution." Combined with a critique-revision loop and a probability-clamped RLAIF mechanism, R-CAI effectively mitigates semantic degradation caused by reward hacking, achieving a 15% improvement in semantic coherence.

UniVid: A Unified Vision-Language Model for Video Moderation

UniVid evolves video moderation systems from unmaintainable "fragmented" architectures to interpretable, reusable "end-to-end" systems by replacing 1000+ black-box classifiers with a unified policy-aware captioning VLM, achieving a 42.7% reduction in violation leakage during production deployment on the ByteDance platform.

Signals Are Not States: Neuro-Symbolic Safeguards for Culturally Aware Classroom AI

The paper argues that classroom AI should not directly interpret culturally contextualized signals such as "silence, averted gaze, or code-switching" as educational judgments like "low engagement, inattention, or low ability." It proposes the NSCR neuro-symbolic framework: mapping multimodal signals into typed facts with uncertainty, provenance, and cultural scope, followed by executable reasoning and governance policies to generate evidence-based claims, while actively deferring (DEFER) when evidence is insufficient or stereotype risks are high.

---

📂 Others (4)

Automated Knowledge Component Generation and Interpretable Knowledge Tracing in Coding Problems

This paper utilizes LLMs to automatically generate and cluster Knowledge Components (KCs) for open-ended programming problems. It proposes KCGen-KT, which converts student mastery of each KC into soft tokens as input for Llama 3, improving both correctness prediction and student code generation performance on CodeWorkout and FalconCode.

NSF-SciFy: Mining the NSF Awards Database for Scientific Claims

NSF-SciFy extracts 2.8M scientific claims and investigation proposals from NSF award abstracts, building a resource orders of magnitude larger than existing scientific claim datasets and demonstrating significant performance gains for claim and proposal extraction models.

Qayyem: A Real-time Platform for Scoring Proficiency of Arabic Essays

Qayyem is the first web platform supporting cross-prompt multi-trait automated essay scoring for Arabic. It integrates various scoring schemes ranging from feature engineering to SOTA neural models, supporting end-to-end academic writing assessment workflows.

Neural Induction of Finite-State Transducers

To be added after reading the paper in depth.