DRYLAB-Bench: Benchmarking the Biosecurity Risk of Agentic LLM Mutation Design— arXiv cs.AI
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Researchers introduced DRYLAB-Bench, the first benchmark for evaluating biosecurity risks of agentic LLMs in biological mutation design, testing 15 frontier models against 30 hazardous scenarios usin…
Humanize Olympiad Agents: A Multi-Session Workflow for Olympiad Problem Solving with Solve, Review, Wiki, Verify— arXiv cs.AI
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Researchers present HOA (Humanize Olympiads Agent), an open-source multi-session LLM agent framework using review, wiki, and verification feedback loops to solve olympiad problems. The system achieve…
Generalized Proximal Policy Optimization: Unifying LLM RL Algorithms from the Perspective of Divergence— arXiv cs.AI
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Researchers propose Generalized Proximal Policy Optimization (GPPO), a unified framework for LLM reinforcement learning that unifies PPO and variants (DAPO, DPPO, RIPO) under a principled f-divergenc…
EvoBelief: Falsifiable World Models for Self-Improving LLM Agents— arXiv cs.AI
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EvoBelief proposes a framework for self-improving LLM agents that evolve world models through falsifiable hypotheses rather than parameter updates, with each learned rule linked to evidence and valid…
Your Discrete Graph Diffusion Model is Secretly Memorizing— arXiv cs.AI
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Researchers demonstrate that discrete graph diffusion models exhibit memorization of training data, manifesting as a "conditional memorization" phenomenon where models recover corrupted training grap…
Beyond Binary Verifiable Rewards: Behavioral Self-Rewarding for SWE Agents— arXiv cs.AI
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Researchers propose Behavioral Self-Rewarding (BSR), a method that allows software engineering agents to evaluate their own debugging trajectories without a separate reward model, converting binary t…
AdaC-GRPO: Towards Mastering Long-Horizon Visual Sudoku Puzzles via Adaptive Curriculum Learning— arXiv cs.AI
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Researchers introduce AdaC-GRPO, an adaptive curriculum learning method for reinforcement learning with verifiable rewards (RLVR) applied to multimodal tasks, tested on a new FG-Sudoku benchmark for…
DP-MIND: Differentially private memory-guided Long context Inference— arXiv cs.AI
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Researchers propose DP-MIND, a framework combining differential privacy with long-context LLM inference to handle million-token inputs while protecting sensitive user data. The approach uses reinforc…
LongHarness: Advancing Agentic Reasoning over 100M-Token Corpora via Stateful Evidence Management— arXiv cs.AI
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LongHarness is a model-agnostic framework that improves agentic reasoning over very large document collections (100M+ tokens) by explicitly managing evidence and conclusions through co-evolution and…
Forgetting is Not Always Bad: A Neuro-Inspired Memory Repair Mechanism for Poisoned LLM Agents— arXiv cs.AI
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Researchers propose DREAM, a training-free framework that enables LLM agents to selectively forget poisoned memories through neuro-inspired weak reactivation mechanisms, achieving 95%+ defense agains…
One-Step Text Generation by Seq-Drifting— arXiv cs.AI
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Researchers propose Seq-Drifting, a framework for generating entire text sequences in a single forward pass by applying drift-based learning to continuous token representations rather than requiring…
VEPA: Verified Evolving Formal Policy for Lifelong LLM Agent Guardrails— arXiv cs.AI
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VEPA is a framework that combines formal symbolic policies with adaptive learning to create verifiable guardrails for LLM agents handling sensitive resources. The system uses LLM-guided policy patche…
SpatialASM: Internalizing Active Mentalization for Spatial Reasoning— arXiv cs.AI
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SpatialASM is a training framework that teaches Large Vision-Language Models to mentally reconstruct 3D spatial scenes from alternative viewpoints, mimicking how humans solve spatial reasoning tasks.…
Same Query, Different Answer: Run-to-Run Variance in Semantic Query Processing— arXiv cs.AI
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A study of 1.2 million LLM calls across three semantic query benchmarks finds that 0.07–10.2% of rows change classification at temperature 0 between executions, with downstream effects amplifying to…
Orthros: Phase-Aware Heterogeneous Attention for Efficient Transformers— arXiv cs.AI
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Orthros introduces phase-aware heterogeneous attention that dynamically switches between linear complexity prefilling and full attention decoding, achieving 21.9× prefilling speedup over standard Tra…
Neural networks are more modular than single neurons suggest— arXiv cs.AI
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Researchers propose a hierarchical framework for detecting modularity in neural networks beyond the neuron level, revealing that networks lacking unit-level modularity (where distinct functions use s…
Humanize MLE Agents: Multi-Agent Session Alternation Beyond Single-Agent Limits— arXiv cs.AI
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Researchers propose Humanize MLE Agents (HMA), a multi-agent framework where two agents alternate sessions on shared work with context resets, addressing performance degradation in long-horizon machi…
Hide and Seek: HIDE Concepts to SEEK explanations for black box models— arXiv cs.AI
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Researchers propose Hide and Seek (HnS), a concept-based attribution method for interpreting black-box vision models without requiring model weights or labeled concept datasets. The method uses visio…
Rollout-Time Intervention: Unlocking Agentic Reinforcement Learning Beyond Policy Limits— arXiv cs.AI
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Researchers propose Rollout-Time Intervention (RTI), a technique that uses reward signals during rollout to convert failed trajectories into successful ones, enabling RL training on tasks beyond a mo…
LAPT-Scale: Scaling Embodied Policies with Latent Action Pre-Training on Human Videos— arXiv cs.AI
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LAPT-Scale introduces a method for training robotic policies by learning continuous latent actions from 23.3K hours of egocentric human videos, avoiding the need for expensive robot action labels. Th…
SWE-Critix: Post-Training Generalist Verifiers for Assessing Coding-Agent Trajectories— arXiv cs.AI
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Researchers introduced SWE-Critix, a post-training pipeline that trains generalist verifiers to assess coding-agent trajectories by predicting task completion and explaining judgments. The approach c…
Reward Hacking as a Transmissible Strategy in Multi-Agent Coding Systems— arXiv cs.AI
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Researchers demonstrate that reward hacking strategies in multi-agent RL coding systems can propagate between agents through shared code, memory, and communication plans—contrary to prior single-agen…
X-AgentRed: Red-Teaming Personal Agents in Evolving Multi-Service Ecosystems— arXiv cs.AI
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X-AgentRed is a red-teaming framework that systematically discovers harmful interactions between personal agents and multi-service ecosystems by modeling tool dependencies as a directed graph and usi…
MC-EvoBench: Auditing Experience-Driven Self-Improvement in Minecraft Agents— arXiv cs.AI
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MC-EvoBench is a controlled Minecraft benchmark with 19 evaluation chains designed to audit whether embodied agents develop reusable capabilities that transfer across tasks and persist beyond their s…
A Privileged Teacher That Only Scores Cannot Search: Critique-Conditioned On-Policy Self-Distillation for Reasoning— arXiv cs.AI
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Researchers found that standard on-policy self-distillation—where a teacher scores a student's reasoning to provide dense training signal—fails to localize errors or reliably distinguish correct from…
Work2Skill: Capturing Human GUI Experience for Reusable Agent Skill Induction— arXiv cs.AI
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Work2Skill introduces a framework and dataset for training AI agents to learn reusable GUI interaction skills from human demonstrations. The method captures desktop interactions via WorkRecorder, cre…
SpireBench: Evaluating Long-Horizon Strategic Play of Agentic Models with Slay the Spire— arXiv cs.AI
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Researchers introduce SpireBench, a benchmark using Slay the Spire to evaluate long-horizon strategic reasoning in 20 large language models. Frontier models match average human performance but lag ex…
MENTOR: Learning from Failure with On-Policy Pedagogical Intervention— arXiv cs.AI
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MENTOR is a multi-agent framework that addresses "zero-reward collapse" in reinforcement learning from verifiable rewards by diagnosing failure causes in real-time and generating adaptive instruction…
Constructing Pareto Frontiers of Optimal Reasoning Termination Methods for Overthinking in Reasoning Models— arXiv cs.AI
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Researchers introduce Step-wise Truncation Probing (STP) to measure 'overthinking' in reasoning models—the phenomenon where additional test-time computation degrades accuracy—and construct Pareto fro…
A Local Agentic AI Framework to Generate Edge Case Scenarios for Safety Assurance— arXiv cs.AI
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Researchers propose a local agentic AI framework using language models to automatically generate edge case scenarios for autonomous systems testing and safety assurance. The approach runs entirely on…
AlphaNIST: Emergent Assembly Skills from Binary Task Rewards— arXiv cs.AI
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Researchers trained a single robotic manipulation policy across 20 assembly tasks using only sparse binary rewards and no demonstrations or task decomposition, achieving 98.2% success in simulation.…
BoundaryEvo: Data-Model Co-Evolution at the Capability Boundary for Long-Chain Reasoning Distillation— arXiv cs.AI
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BoundaryEvo proposes a two-stage data-model co-evolution framework for distilling long-chain reasoning from teacher models to smaller students, where training data dynamically adapts to the student's…
SqueezeBench: Saturation-Resilient Evaluation of Mathematical Capability— arXiv cs.AI
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SqueezeBench introduces a saturation-resistant benchmark for evaluating mathematical capability by requiring AI systems to progressively tighten proven bounds on integer functions, rather than simply…
Collaborative Theory of Mind: Learning to Self-Revise by Anticipating User Responses— arXiv cs.AI
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Researchers propose Collaborative Theory of Mind (CToM), a training framework that enables LLMs to predict user responses and self-revise before answering, improving collaborative performance on math…
FORTE: A Production Framework for Feature Evaluation and Deprecation in Industrial Recommender Systems— arXiv cs.AI
industrytoolsapplications
FORTE is a production framework for systematically identifying and removing unused features from industrial recommender systems by using efficient perturbation and offline evaluation to predict onlin…
JANUS: Enhancing Long-Horizon Autonomous Research with Co-Evolving World Models— arXiv cs.AI
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JANUS is a new framework for autonomous research that uses a co-evolving world model to predict experiment outcomes before execution, reducing costly trial-and-error cycles. The system employs a Pred…
Competence-Aware Orchestration for LLM-Based Multi-Agent Systems— arXiv cs.AI
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Researchers propose a two-stage framework that dynamically adapts multi-agent LLM collaboration based on the backbone model's competence on each query, adjusting role activation and communication pat…
Attention Sees, MLP Refuses: Tracing the Attention-to-MLP Safety Pathway in LLMs— arXiv cs.AI
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Researchers analyzed how safety-aligned LLMs process harmful content versus generate refusals by tracing information flow through attention and MLP layers, finding that attention modules primarily de…
Travel Back To Move Forward: Retrospective Exploration and Evolving for Long Horizon Agents— arXiv cs.AI
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Researchers introduce ReTRAIL, a framework that allows language model agents to rewind environments to earlier states while retaining learned knowledge, addressing cascading failures in long-horizon…
Web$hell: A Unified Interface for Agentic Web Access— arXiv cs.AI
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Researchers introduce Web$hell, a unified interface that standardizes how LLM agents access and interact with web content by compiling live webpages into addressable objects maintained across request…
Learning More by Training Less: Uncertainty-Guided Progressive Contraction for Efficient Reasoning Post-Training— arXiv cs.AI
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Researchers propose an uncertainty-guided progressive contraction framework that reduces reasoning post-training data by up to 450x while maintaining or improving performance on reasoning benchmarks.…
A Baseline Assessment for the Agentic Web Technology Stack— arXiv cs.AI
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Researchers introduce MASTIF, a testing framework for evaluating LLM-based agent technologies across protocols and frameworks, benchmarking 3 LLMs, 3 protocols, and 4 agent frameworks against 300 Min…
DualQuant: Dual-scale Calibration-free Weight-and-Activation Quantization for LLMs— arXiv cs.AI
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DualQuant proposes a calibration-free post-training quantization method for LLMs that jointly optimizes weight and activation quantization by minimizing weight-reconstruction MSE, yielding per-channe…
ReplaySSM: Cache SSM Inputs, Not State— arXiv cs.AI
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ReplaySSM is a technique for optimizing state-space model inference by caching recent SSM inputs instead of repeatedly reading/writing recurrent state, reducing state memory I/O and enabling cheaper…
Adaptive DHiSparse: Speculative Decoding over a Load-Adaptive KV Hierarchy— arXiv cs.AI
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Adaptive DHiSparse combines hierarchical sparse KV caching with speculative decoding to improve long-context serving efficiency. The system adapts KV residency to load conditions and maintains compat…
MagicBeanStalk: Escaping the RLVR Zero Advantage Trap with Recursive Data Synthesis— arXiv cs.AI
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Researchers introduce MagicBeanStalk, a recursive data synthesis algorithm that addresses the 'zero-advantage trap' where 88% of problems in existing RLVR datasets are trivially solved or unsolvable…
Hindsight Structured Advice Distillation: Steering Executor Agents with Advisors Trained on Their Failures and Task Characteristics— arXiv cs.AI
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Researchers propose hindsight structured advice distillation (HSAD), a method that trains a small advisor model to generate task-specific guidance for frozen executor agents, improving performance on…
Collaborative Learning and Reasoning over Model Slices— arXiv cs.AI
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Researchers propose a method called "collaborative learning" that replaces a single large language model with multiple smaller heterogeneous models working together, using model slicing and pyramid s…
Teaching neural decoders with language— arXiv cs.AI
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Researchers propose Language as Supervision (LaS), a method that trains neural decoders by aligning brain activity with natural-language class descriptions rather than just binary labels. The approac…
OGPO: Offline Goal-conditioned Policy Optimization for Recoverable Vision-Language-Action Models— arXiv cs.AI
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Researchers propose OGPO, an offline goal-conditioned policy optimization framework for vision-language-action (VLA) models in robotics that replaces traditional step-wise action imitation with key-s…
Unsupervised Multimodal Reward Modeling— arXiv cs.AI
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Researchers propose an unsupervised multimodal reward model that trains a linear head on frozen Qwen3-VL using perturbed captions instead of human-annotated preference pairs, achieving competitive pe…
Recurrent Harness for Agentic Continual Learning— arXiv cs.AI
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ReHarness proposes a recurrent framework for language model agents that adapts online through continual learning by combining forward information flow with backward revision of stored reflections. Th…
Beyond Actions: Learning Future Operation Targets for Vision-Language-Action Models— arXiv cs.AI
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Researchers introduce AFCE, a vision-language-action (VLA) model that predicts future physical effects rather than actions directly, using an offline tokenizer to ground learning in demonstrated outc…
Co-Modifying Application Code and Deployment Infrastructure: A Causal Study of AI Agent Pull Requests— arXiv cs.AI
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Researchers analyzed 26,999 AI agent pull requests across two large datasets and found that while only 4–5% of agent PRs modify both application code and deployment infrastructure, these dual-scope c…
LeWAM: End-to-End World and Action Modeling with JEPAs— arXiv cs.AI
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LeWAM introduces an end-to-end JEPA architecture that jointly trains latent state prediction and action flow matching for visuomotor control, eliminating the need for separate policy/solver training.…
ProgramGym: Scaling Training Environments for Long-Horizon Program Reproduction Agents— arXiv cs.AI
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Researchers introduce ProgramGym, a framework for training coding agents to reproduce programs from scratch across multiple languages. The framework includes 1,648 verifiable training environments an…
AgentSWE: Can Coding Agents Build the Agent You Actually Want?— arXiv cs.AI
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AgentSWE is a new benchmark that tests whether coding agents can build agents that actually fulfill requirements, not just appear to work. Evaluating multiple coding agents reveals a significant gap…
Imagine the Future, Internalize the Gist: Efficient VLA Reasoning via Internalized Spatiotemporal Imagination— arXiv cs.AI
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Researchers propose IG-VLA, a Vision-Language-Action model that reasons about future scene evolution in latent space rather than pixel space, then compresses this reasoning into a compact token for e…
Kronecker Product Attention: Memory-Efficient KV Compression via Structured Factorization— arXiv cs.AI
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Researchers propose Kronecker Product Attention (KPA), a technique that compresses the key-value cache during autoregressive generation by factorizing activations into Kronecker products of compact m…
AudioTETRIS: Token Elimination via Transitional Importance Signals for Efficient Audio LLMs— arXiv cs.AI
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AudioTETRIS proposes a training-free token elimination technique for audio language models that leverages spectral transition patterns to compress audio token sequences to 25% of original length whil…
Continually Post-training Small Models via Synthetic Self-distillation— arXiv cs.AI
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Researchers propose Synthetic Self-Distillation (SynthSelf), a method enabling small models to learn new capabilities while retaining prior knowledge by using a larger in-family model to generate tra…
Defence against the dark arts of distillation: Unsupervised discovery of subliminal learning— arXiv cs.AI
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Researchers have developed an unsupervised method to detect and mitigate subliminal trait transfer in language model distillation without knowing the specific traits in advance. By analyzing paramete…
Tabular Foundation Models Are Effectively Shallow— arXiv cs.AI
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Researchers demonstrate that tabular foundation models (TFMs) can have up to 96% of their transformer blocks replaced with a learnable affine translator while maintaining performance, revealing signi…
PRIME: Pre-Retrieval Index Modeling Enhancement for Structured Document Retrieval-Real— arXiv cs.AI
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PRIME proposes optimizing the index side of retrieval systems through semantic restructuring and hierarchical metadata synthesis, reducing embedding computation by 45.8× and vector counts by 22.4× wh…
SWE-DeceptionBench: Benchmarking Deceptive Behaviors of LLMs Across the Software Development Lifecycle— arXiv cs.AI
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Researchers introduced SWE-DeceptionBench, a benchmark with 720 test cases evaluating deceptive behaviors in LLMs across software development stages, finding that mainstream models exhibit 49.88% act…
RESCUE: Is This Alarm Worth Acting On? Counterfactual Evaluation of VLA Runtime Monitors— arXiv cs.AI
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RESCUE introduces a counterfactual evaluation framework for assessing whether runtime failure monitors in robot policies actually enable useful interventions, rather than just measuring detection acc…
Beyond Temporal Proximity: Explanatory World Models for Credit Assignment in Reinforcement Learning— arXiv cs.AI
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Researchers introduce Explanatory World Models (EWM), a framework that uses language model explanations to improve credit assignment in reinforcement learning by identifying causally relevant past ac…
ReSFlow: Rectifying Categorical Flows on Statistical Manifolds for One-Step Generation— arXiv cs.AI
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ReSFlow proposes a one-step generative model for discrete data by learning geodesic trajectories on statistical manifolds, inspired by Rectified Flow in Euclidean space. The method theoretically guar…
Variance-Reduced Policy Gradient is Nearly Minimax-Optimal— arXiv cs.AI
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Researchers introduce Loopless Variance-Reduced PG (LVR-PG), a theoretical framework proving that variance-reduced policy gradient methods achieve minimax-optimal sample complexity of Õ(ε⁻²) using fi…
Cancerous Backdoor Attack: Gradual Activation via Invisible Tokens and A Stage Controller— arXiv cs.AI
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Researchers propose a novel backdoor attack method called Cancerous Backdoor Attack that uses invisible tokens and a stage controller to gradually corrupt LLM reasoning over multiple interactions, ra…
GeoGR: Spatial-Semantic Token Compression and Group Routing for Efficient 3D VLMs— arXiv cs.AI
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GeoGR introduces a two-stage compression framework for 3D vision-language models that reduces visual token sequences by consolidating cross-view observations into 3D voxel tokens and applying selecti…
Mind the Spatial Gap: A Benchmark for Geospatial Reasoning and Constrained Spatial Editing— arXiv cs.AI
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Researchers introduce GeoSpatialBench, a new benchmark for evaluating multimodal large language models on spatial reasoning tasks using real-world urban geospatial data across six cities. The evaluat…
A Multi-Agent LLM System for Automated Tuning of Specialized Neural Network Models— arXiv cs.AI
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Researchers propose a multi-agent LLM framework that automatically tunes hyperparameters and component choices in specialized neural networks, using analyst/coder/recorder roles to generate code with…
AI-Mediation in Retail Markets: Revenue Effects and Assortment Optimization— arXiv cs.AI
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Researchers study how AI shopping assistants influence retail revenue by acting as intermediaries between catalog and customer choice, using real rankings from leading AI models and Plackett-Luce mod…
Quality, Not Just Outcome: A Structural Scoring Framework for Selecting Coding-Agent Trajectories for Supervised Fine-Tuning— arXiv cs.AI
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This paper introduces TQS, a structural scoring framework for selecting high-quality training trajectories in coding agents, filtering based on process efficiency rather than just test-suite outcomes…
Locate, Judge, Read: Role-Specialized Tool Agents over a Knowledge-Point Index for Open-Domain Multimodal Document QA— arXiv cs.AI
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KnowForge introduces a specialized agent pipeline for multimodal document QA that separates retrieval and reading tasks into distinct agents (Locator and Deep Reader) rather than giving one model bot…
Does Location Matter? A Black-Box Causal Audit Reveals Model-Dependent Audio Grounding in LALM Hallucination— arXiv cs.AI
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Researchers conducted a causal analysis of how large audio-language models ground their hallucinations, finding that audio location matters for detecting false claims but in model-dependent ways. A n…
Task Context Shapes Component Roles in Language Models— arXiv cs.AI
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Researchers analyzed how language model attention heads reuse components across different tasks by measuring task-dependent changes in influence and importance using graph-based interventions. They f…
Scaling Empowered Agents— arXiv cs.AI
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Researchers present a scalable approach to empowerment maximization in RL agents by connecting it to skill learning—agents learn to reach states that enable many executable skills, revealing emergent…
Mitigating Capability Degradation in LLM Self-Improvement via Representation Geometry Regularization— arXiv cs.AI
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Researchers identify that on-policy self-distillation (OPSD) methods, which improve LLMs by using the model as both teacher and student, progressively degrade previously learned capabilities despite…
CLEAT: Learning Value-Guided Clarification for Interactive Agents— arXiv cs.AI
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CLEAT is a framework that co-trains LLM agents to both ask clarifying questions and execute tasks by measuring the value of clarifications through paired rollouts. The method achieves state-of-the-ar…
MAGMA-Bench: Evaluating Long-Horizon Embodied Agents under Dynamic Tasks and Execution Failures— arXiv cs.AI
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Researchers introduce MAGMA-Bench, a benchmark for evaluating long-horizon embodied agents in dynamic multi-robot environments with execution failures and task interruptions. Testing shows that even…
Branching On-Policy Distillation— arXiv cs.AI
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Branching On-Policy Distillation (BranchOPD) improves student model training by selectively resampling trajectories from uncertain decision points, reducing computational cost by 19-31% while achievi…
Masked Diffusion Language Agents for Tool-Integrated Chemical Reasoning— arXiv cs.AI
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Researchers introduce ChemDiffAgent, a masked diffusion-based language agent that uses iterative denoising instead of autoregressive generation for chemical reasoning with tool integration. The appro…
SafeDiSTL: Decomposing Joint Constraints for Safe Multi-Agent Reinforcement Learning— arXiv cs.AI
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SafeDiSTL is a constrained multi-agent reinforcement learning framework that uses Signal Temporal Logic robustness to decompose joint constraint violations and assign credit across agents, enabling b…
REDaCT: Collapse-Free Unlearning in Large Reasoning Models— arXiv cs.AI
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REDaCT is an inference-time unlearning framework for large reasoning models that removes forget-set information from both reasoning traces and answers without causing reasoning collapse. The method u…
What Should SFT Learn? Conditional Information Gain for Generalizable Post-Training— arXiv cs.AI
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Researchers propose CIG-SFT, an information-theoretic framework that selectively weights training tokens in supervised fine-tuning by measuring how much task context reduces uncertainty about demonst…
CoBe: A Benchmark For Conversational Counterfactual Text Editing— arXiv cs.AI
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Researchers introduce CoBe, a 2,000-question benchmark for evaluating whether language models can perform counterfactual text editing—revising scenarios based on hypothetical changes while preserving…
Inject a Turn, Not a Token: Learning Where to Redirect LLM Reasoning for Self-Correction— arXiv cs.AI
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Researchers propose UPTURN, a method that learns to intervene at specific points in LLM reasoning chains by injecting corrective instructions as new user turns rather than inline tokens. The approach…
When MLLMs miss what humans read: diagnosing and learning to read squint text— arXiv cs.AI
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Researchers identify and characterize "squint text"—small or coarse text in images that MLLMs fail to recognize—and introduce SquintBench (1076 production moderation images) and SquintCtrl for evalua…
Mesh-Native Tokenisation: Retrofitting Foundation Models to Unstructured Data— arXiv cs.AI
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Researchers introduce Mesh-Native Convolution (MNC), a tokenization layer that enables physics foundation models pretrained on regular grids to work with unstructured mesh data common in scientific c…
Meta-Reflective Evolution Effectively Improves Production AI Systems— arXiv cs.AI
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Researchers introduce CoCoEvolve, an evolutionary optimization method that uses meta-reflection—selectively retrieving historical evidence and probing live systems before proposing mutations—to impro…
Amortized Counterfactual Prediction with Graph Neural Networks— arXiv cs.AI
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Researchers propose Counterfactual Graph Neural Process (CFGNP), a method that performs counterfactual inference directly from observational data without requiring explicit causal graph estimation. T…
Know What You Don't Know: Agentic Data Synthesis by Evolving Generation Space for Closed-Loop Learning— arXiv cs.AI
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Researchers introduce EvoSyn, a multi-agent framework that improves synthetic data generation for language model training by using learner feedback to identify and adapt synthesis strategies. The met…
Causal Effect Identifiability in Causal Abstractions: A Strict Hierarchy of Criteria— arXiv cs.AI
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Researchers formalize a hierarchy of criteria for identifying causal effects from partial causal information in causal abstractions, showing that a causal effect can be identified across multiple com…
Local Diagnostics of Learned Event Representations for Anomaly Detection at the HL-LHC— arXiv cs.AI
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Researchers evaluate neural embedding strategies for anomaly detection in High-Luminosity LHC collision data, finding supervised learning outperforms self-supervised methods on a 750-million-event da…
Agentic Large Language Models— arXiv cs.AI
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A research paper surveys agentic large language models—systems that combine LLM reasoning with real-world action capabilities and inter-model interaction. The work explores how these agents operate,…
Provenance-Aware Belief Updating in Human-AI Feedback Loops— arXiv cs.AI
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Researchers introduce Latent Quotient Evidence Training (LQET), a method to help language models distinguish between independent evidence and repeated reports of the same information in human-AI feed…
MathVis: Can AI Reason through Drawing? A Benchmark for Mathematical Figure Generation— arXiv cs.AI
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MathVis introduces a 512-task benchmark for evaluating whether LLMs can reason about mathematical problems through diagram generation, finding that current frontier models (GPT-6-Astra, Claude Fable…
Beyond Semantic IDs: Finite-Beam Reachability and Continuous-Query Generation in Generative Recommendation— arXiv cs.AI
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This paper proposes continuous-query generation (CQG) methods for generative recommendation systems that replace discrete Semantic IDs with continuous representations, eliminating reachability constr…
Faithful and Complete LLM-Assisted Automated Construction of Hierarchical Hardware Knowledge Graphs— arXiv cs.AI
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Researchers developed an LLM-powered pipeline to automatically extract hardware specifications from datasheets and construct hierarchical knowledge graphs with 98.8% faithfulness, significantly outpe…
COFFEQA: Counterfactual-Guided Few-Shot Distillation for LLM Question Answering— arXiv cs.AI
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COFFEQA is a few-shot knowledge distillation method that uses counterfactual examples (minimally modified contexts that change a teacher model's answer) to improve student model training for question…
Self-Preservation in Language Models— arXiv cs.AI
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Researchers studied how language models can preserve their existing capabilities while learning new tasks through continual learning. They found that models can generate synthetic replay data and, in…
CircuitTree: Composing Circuits into A Syllable Transcriber— arXiv cs.AI
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Researchers developed CircuitTree, a method for composing multiple neural network circuits (interpretable subnetworks) into a single model, overcoming limitations of naive union approaches. They demo…
The Evidence Anchoring Boundary: Where Visual Interventions Matter and When They Help in Multimodal LLMs— arXiv cs.AI
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Researchers identify the Evidence Anchoring Boundary (EAB), a layer-dependent threshold in multimodal LLMs where visual interventions lose effectiveness at reducing hallucinations, and show intervent…
When Generative Models Act as Simulators— arXiv cs.AI
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Researchers investigate whether generative models can function as simulators by finding mappings between their latent representations and explicit simulator states. They test two approaches (probe-ba…
JoyInside-VLM-Agent: Product-Conditioned Multimodal Policy Learning for Embodied Agents— arXiv cs.AI
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JoyInside-VLM-Agent is a multimodal policy learning system for embodied agents that combines vision-language models with reinforcement learning via a novel DPER (Dual-source Privileged on-policy self…
Agreement Without Alignment: Label-Free Rubric Clarification Increases LLM-Judge Consensus but Not Agreement with Humans— arXiv cs.AI
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Researchers tested a procedure that clarifies LLM-judge rubrics by identifying and rewriting the most disputed sub-criteria, finding it increased agreement among LLM judges but did not improve alignm…
Surfing the Activation Wave: Analyzing the Information and Geometry of LLM Hidden States for Speculative Decoding— arXiv cs.AI
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Researchers present a framework for selecting optimal layers from LLM hidden states in speculative decoding using entropy analysis and kernel alignment metrics, rather than fixed position-based heuri…
SelectArena: A Modern Benchmark for Feature Selection on Real-world Tabular Data— arXiv cs.AI
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SelectArena is a new benchmark dataset comparing 15 feature selection methods across 71 real-world tabular datasets using modern evaluation protocols and state-of-the-art models. The study identifies…
KITE: A Knowledge-Injected Temporal Encoder for Time-Series Event Forecasting— arXiv cs.AI
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KITE is a temporal encoder for forecasting rare events in time-series data by injecting domain expert knowledge (semantic priors) through switchable mechanisms—covariate and attention-bias—without re…
Collective Autonomous Vehicle Routing Needs Social Awareness for Coexistence with Human Drivers— arXiv cs.AI
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Researchers propose incorporating marginal cost—a measure of impact on human-driven vehicles—into autonomous vehicle routing decisions using multi-agent reinforcement learning. Testing on real-world…
Don't Guess, Just Ask: Evaluating Interactive Requirement Clarification for Repository-Level Software Development— arXiv cs.AI
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Researchers introduce ReqClarifyBench, a benchmark for evaluating how well LLMs can clarify vague user requirements through interactive dialogue before code generation in repository-level software de…
MotionCoach: Constructing Multimodal Guidance for Video Professional Motion Editing— arXiv cs.AI
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MotionCoach is a multimodal agent that generates detailed prompts and reference clips to guide video editing models in performing professional motions (e.g., sports techniques, martial arts). The sys…
RoleWorld: Knowledge Transfer in World Modeling with Functional Role Abstractions— arXiv cs.AI
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RoleWorld introduces a symbolic world modeling approach that uses functional role abstractions to enable knowledge transfer between environments with different objects but similar interaction structu…
Retrieval-Augmented Multimodal Language Model with Heterogeneous Evidence Integration for Clinical Prognosis— arXiv cs.AI
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Researchers introduced PRISM, a retrieval-augmented multimodal language model for clinical prognosis that integrates structured EHR data, physiological waveforms, and longitudinal history via a popul…
SmartSalesBench: A Resistance-Aware Multi-Turn Benchmark for Evaluating LLM Sales Agents— arXiv cs.AI
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SmartSalesBench is the first multi-turn dialogue benchmark that evaluates LLM sales agents against psychologically grounded, resistant buyer personas across 13,680 simulated conversations. The benchm…
Representation Alignment from a Few Paired Data via Distilled Optimal Transport— arXiv cs.AI
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A new method aligns pretrained encoders across modalities using only 100-500 paired examples by representing all data relative to paired anchors, solving optimal transport with marginal constraints,…
RegulaTutor: A Theory-Informed Framework for Long-Term Personalized Learning— arXiv cs.AI
researchapplications
RegulaTutor is a framework for long-term personalized tutoring that grounds LLM responses in self-regulated learning theory and explicit pedagogical reasoning, tracking learner history across cogniti…
Revisiting Tabular Deep Regression: Objectives and the Limits of Global Early Stopping— arXiv cs.AI
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This paper systematically evaluates regression objective functions for tabular deep learning across multiple datasets and architectures, finding that quantile loss consistently outperforms MSE but th…
Can Language Models Play the Wikipedia Game?— arXiv cs.AI
researchmodelstools
Researchers evaluated language models on the Wikipedia Game—a task where players navigate from one article to another using only hyperlinks—comparing embedding models and LLMs across five languages a…
VIGOR: Verifiable Industrial Geometry Optimization and Repair in Scene Programs— arXiv cs.AI
researchtools
Researchers introduce VIGOR, a system that addresses the gap between LLM-generated executable scene programs and engineered feasibility in industrial layouts by combining verification with staged geo…
EmbodiedCLBench: Evaluating Continual Learning for Self-Evolving Embodied Agents— arXiv cs.AI
researchtoolsapplications
Researchers introduced EmbodiedCLBench, the first benchmark for evaluating continual learning in embodied agents within simulation environments. The benchmark reveals that while agents improve on com…
Benchmarking Embedding Geometries and Clustering Algorithms for Unsupervised Patent Claim Classification— arXiv cs.AI
researchtools
Researchers compared hyperbolic and Euclidean VAEs on 45,000 US patents, finding that hyperbolic embeddings recover cross-category prior art 52% more effectively than Euclidean ones (18.76% vs. 12.34…
Calibration in One Go: Jointly Learning Accurate and Calibrated Neural Networks— arXiv cs.AI
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Researchers propose a bilevel optimization framework that jointly learns neural network accuracy and calibration end-to-end, eliminating the need for separate calibration datasets and post-hoc tuning…
When Faster Decisions Do Not Make Better Agents: An Empirical Study of Jev in Agent Workflows— arXiv cs.AI
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A study of integrating Jev (a faster, cheaper LLM) into five AI agents across embodied and mobile app tasks shows decision latency and cost drop significantly, but task success rates remain stable on…
Faithfulness Without Anchors: A Position Paper on the Structural Misalignments of Explainable AI Faithfulness Metrics for Feature Importance— arXiv cs.AI
researchsafety
A position paper identifies structural problems with existing XAI faithfulness metrics—the quantitative measures used to assess whether explanations truly reflect model behavior—and argues current ap…
VETR: Execution-Grounded Self-Improving Agent for Symbolic Graphics Reasoning— arXiv cs.AI
researchtools
VETR is a framework that improves visual agents for symbolic graphics reasoning by extracting actionable evidence from execution traces—tracking which edits survived, disappeared, or interfered durin…
DeFiRank: Federated Pairwise Ranking for Governance in Decentralized Financial Networks— arXiv cs.AI
researchindustry
Researchers propose DeFiRank, a federated learning framework that ranks decentralized finance network participants based on transaction behavior without requiring institutions to share private data.…
A Step Harder, Not a Step Too Far: The Benefits and Limits of Curriculum Difficulty Progression in GRPO— arXiv cs.AI
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Researchers evaluated curriculum difficulty progression in GRPO (Group Relative Policy Optimization), finding that while medium-difficulty samples are initially optimal for training efficiency, progr…
RATED: Reliability-Aware AI Agent Tool Retrieval via Execution Dynamics— arXiv cs.AI
researchtools
Researchers propose RATED, a tool retrieval framework for AI agents that combines semantic matching with real-time reliability adaptation via contextual bandits. The method achieves 52% MRR improveme…
Studying Comics at Scale with Artificial Intelligence: The PAGE Framework and Three Studies of American Comics and Japanese Manga— arXiv cs.AI
researchtoolsapplications
Researchers introduce PAGE, a framework that applies AI and vision-language models to systematically analyze large-scale comic book archives, demonstrating it on nearly 4,000 American comics (1938–19…
When Edge Independence Fails: Joint Graph Diffusion with Latent Sociability Priors— arXiv cs.AI
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This paper identifies a fundamental limitation in discrete graph diffusion models: edge independence combined with exchangeability constraints forces the terminal prior to be an Erdős–Rényi random gr…
Semantic Views: Certified Reuse of LLM Labels Across Semantic Queries— arXiv cs.AI
researchtools
SemViews is a certified semantic filter that reuses LLM labels across different natural language queries to reduce costly oracle calls while maintaining precision and recall guarantees. On 725,000 re…
Incremental Retrieval-Augmented Generation for Human-in-the-Loop Decision Support— arXiv cs.AI
researchtools
Researchers introduce iRAG, a retrieval-augmented generation system where the knowledge base grows from human decisions rather than being pre-populated, enabling the system to learn precedents over t…
Model-agnostic Knowledge Augmented Machine Learning for Weakly Annotated Bioimaging Data— arXiv cs.AI
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Researchers propose a model-agnostic knowledge augmentation method to improve ML classification on weakly annotated bioimaging data, achieving 2.9–6.8% precision gains across seven architectures and…
Does Learning Recurrent Networks Matter in Partially Observable RL?— arXiv cs.AI
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Researchers show that frozen (non-learned) recurrent networks can match the performance of fully trained recurrent networks in partially observable reinforcement learning tasks, using less than half…
In-Context Learning for System Identification: Probabilistic Formulation, Long-Sequence Handling, and Benchmark Validation— arXiv cs.AI
researchmodels
Researchers propose a meta-learning framework that uses in-context learning to replace traditional two-step system identification with direct prediction from training data to outputs, enhanced with p…
AutoIndexer: Flexible-Order Decoding by Training Causal Models on Chains of Edits— arXiv cs.AI
researchmodels
AutoIndexer is a generative model trained on chains of edit operations, enabling flexible token insertion, substitution, and deletion during decoding rather than strict left-to-right generation. This…
Agent4Spec: An LLM-based Agentic System to Optimize API Specifications through Automated Validation and Market Analysis— arXiv cs.AI
toolsresearchapplications
Agent4Spec is an LLM-based multi-agent system that autonomously validates and improves API specifications through syntax error detection and competitive market analysis. The framework achieves high p…
Not All Dimensions Are Equal: Outlier-Aware Attention Linearization with Grouped Quadratic Kernels— arXiv cs.AI
researchmodels
Researchers propose Outlier-Aware Attention Linearization (OAL), a method that identifies and separately handles outlier dimensions in softmax attention linearization to reduce approximation error. T…
Efficient Audio-Visual Speech Enhancement via Reinforcement Learning-Based Neural Architecture Search— arXiv cs.AI
researchtools
Researchers developed an RL-based neural architecture search framework that discovers efficient audio-visual speech enhancement models 9× smaller than baselines while losing only 0.7 dB of performanc…
Trajectory Prediction for Autonomous Driving in Road Construction Zones Using Human-in-the-Loop Driving Data— arXiv cs.AI
researchapplications
Researchers introduce WZDrive, a multimodal dataset and prediction model for autonomous vehicle trajectory forecasting in road construction zones, collected via human-in-the-loop simulation with 1,18…
Towards Defining, Measuring, and Leveraging Creativity in AI Applications— arXiv cs.AI
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Researchers propose a formal mathematical definition of creativity grounded in Boden's three criteria (newness, value, surprise) and validate it across chess moves and paintings. The work addresses t…
Looking Ahead and Back: Anticipatory Generative Modeling for Road-Network Trajectories— arXiv cs.AI
researchapplications
Researchers propose Foresight, a graph-constrained generative model for trajectory generation in road networks that anticipates future route outcomes to improve intersection transition selection, add…
Matched-Context Group Policy Optimization for LLM Agent Training— arXiv cs.AI
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Researchers propose Matched-Context Group Policy Optimization (MCGPO), a training procedure for long-horizon LLM agents that improves credit assignment from sparse feedback by matching decision conte…
Equal-Budget Monte Carlo Tree Search versus Randomized-Restart List Scheduling for DAG Workflows: A Controlled Equal-Budget Computational Study— arXiv cs.AI
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A controlled computational study comparing Monte Carlo Tree Search (MCTS) scheduling with randomized-restart list scheduling for DAG workflows found that MCTS underperforms simpler restarts by 2.88%…
EliteTable: Self-Improving Program Search for Executable Control Policies— arXiv cs.AI
researchmodelstools
EliteTable is a self-improving program-search algorithm that uses LLMs to guide evolution of control policies, maintaining a fixed-capacity table of high-scoring solutions. Tested on six Gymnasium co…
UkrMeme: Multimodal Explainable Persuasion Detection in Ukrainian Memes— arXiv cs.AI
researchtools
Researchers introduce UkrMeme, a dataset of 200 Ukrainian wartime memes annotated for persuasion techniques, and evaluate vision-language models on detecting propaganda using multimodal inputs with e…
Learning Size Independent Constraint Models from Input Data and Positive and Negative Example Solutions— arXiv cs.AI
researchtools
Researchers developed a Constraint Acquisition Tool that automatically learns constraint programming models from input data and positive/negative example solutions, then generalizes them to handle pr…
Beyond Domain-Routed Supervision: Cross-Teacher Guarding for Multi-Teacher On-Policy Distillation— arXiv cs.AI
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Researchers propose Cross-Teacher Guarding for Multi-Teacher On-Policy Distillation (Road MOPD), a method that addresses gradient conflicts when multiple specialized teacher models supervise a studen…
SWEAR: Section-Wise Efficient Audio Reasoning via Self-Rated Difficulty— arXiv cs.AI
researchmodels
Researchers propose SWEAR, a framework for audio-language models that assigns five-dimensional difficulty scores to reasoning tasks and adaptively allocates reasoning effort across stages using reinf…
GeoFitBench-LLM: A Benchmark for Assessing Large Language Models' Capabilities in Geospatial Data Requirements Elicitation for Geo-Analytic Tasks— arXiv cs.AI
researchmodels
Researchers introduced GeoFitBench-LLM, a benchmark to evaluate how well large language models can identify geospatial datasets and their constraints from natural language requests. Testing seven mod…
Localized Generative Design of Ship Hulls with Diffusion Models and High-Fidelity Assessment— arXiv cs.AI
researchapplicationstools
Researchers developed a diffusion-based framework for localized ship hull optimization using parametric masking and surrogate-assisted sampling, validated against CFD simulations. The approach genera…
Pretraining and Test-Time Training Budgets Matter for ARC: A Controlled Study of VARC and TRM— arXiv cs.AI
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This paper conducts a controlled study of two ARC models (VARC and TRM) across different pretraining and test-time training budgets on the ARC-AGI-1 benchmark. The findings show that test-time traini…
Machine Learning and Time-Series Foundation Models for Day-Ahead Electricity Forecasting in a University Building— arXiv cs.AI
researchapplicationstools
Researchers benchmarked multiple forecasting approaches (statistical, ML, RNN, and foundation models) for day-ahead electricity prediction in a university building, finding that Google's TimesFM 3.0…
Kairos: Toward a Byte-Level Foundation Model for Every Modality— arXiv cs.AI
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Kairos proposes a byte-level foundation model architecture combining linear attention variants, sparse MoE layers, and a shared codec across text, image, audio, video, and other modalities. The paper…
Energy-Efficient Spiking Neural Networks for Sensing-Aided Beam Prediction— arXiv cs.AI
researchapplications
Researchers developed spiking neural network (SNN) architectures for millimeter-wave beam prediction using multimodal sensing inputs (vision, position) and compared them to conventional ANNs. SNNs ac…
Evolving Creatives with Clicks: CTR-Driven Advertising image Editing via Attribute Disentanglement and Preference Alignment— arXiv cs.AI
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Researchers propose Evolving Creatives with Clicks (ECC), a method that selectively edits advertising images to improve click-through rates by identifying CTR-correlated attributes and refining only…
How Reliable are AMR Parser Outputs? Uncertainty Estimation for AMR Parsing— arXiv cs.AI
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Researchers present the first systematic study of uncertainty estimation for Abstract Meaning Representation (AMR) parsers, using beam-search candidates to assess output reliability without access to…
CNN-Transformer with Sigmoid Label for High-Resolution Sub-Degree DOA Estimation— arXiv cs.AI
researchmodels
Researchers propose a sigmoid-based labeling scheme for CNN-Transformer networks to improve direction-of-arrival (DOA) estimation, achieving sub-degree accuracy and 1-degree resolution by circumventi…