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ReFract: Benchmarking Perspective Awareness in Language Model Agents with Text World Models

arXiv:2610.03356v1 Announce Type: new Abstract: Large Language Model (LLM) agents are increasingly deployed in high-stakes settings such as industrial maintenance and equipment fault troubleshooting,

Content type

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Safety

Register-Routed Delayed Fusion: Rewiring Shortcut-Prone Observation Fusion in Visuomotor Imitation

DGX agent

arXiv:2610.02813v1 Announce Type: new Abstract: Visuomotor imitation policies combine high-dimensional visual observations with compact signals such as proprioception, and their fusion topology determ

safetyarxiv-cs-ro

5 Oct 2026

Agents

Reinforcement Learning Techniques for the Optimization of Target Polarization in Nuclear Physics Scattering Experiments

arXiv:2610.02452v1 Announce Type: new Abstract: The operation of dynamically polarized targets in nuclear physics experiments relies on continuous tuning of the microwave frequency to compensate for r

agentsarxiv-cs-ai

Relevant Evidence Decoding for Audio-Visual Hallucination Mitigation

arXiv:2610.02976v1 Announce Type: new Abstract: Audio-Visual Large Language Models (AV-LLMs) remain prone to cross-modal hallucinations, where one modality incorrectly affects predictions about anothe

Reliability of Probabilistic Emulation of Physical Systems

arXiv:2606.12997v2 Announce Type: replace Abstract: Two dominant approaches have emerged for generating probabilistic forecasts of physical systems: generative models, such as diffusion or flow matchi

researcharxiv-cs-lg

Reliability Stress Tests and Decision-Time Routing for Chest X-ray Vision-Language Models

model-releasesarxiv-cs-cv

arXiv:2610.02270v1 Announce Type: cross Abstract: Medical vision-language model (VLM) evaluation is sensitive to workflow design, prompting strategy, and benchmark construction, yet most studies treat

Tutorials

Reliable Self-Evolution with Imperfect Proxy Rewards

arXiv:2610.02975v1 Announce Type: new Abstract: Large language model (LLM)-based self-evolving search is a promising approach to scientific discovery. However, high-fidelity evaluation of every candid

tutorialsarxiv-cs-ai

Applications

ReSCUE: Re-translation with Sentence Commitment for Unsegmented Long-Form Simultaneous Sign Language Translation

arXiv:2610.03022v1 Announce Type: cross Abstract: Simultaneous Sign Language Translation (SLT) is critical for real-time communication, yet existing methods remain largely confined to sentence-level,

applicationsarxiv-cs-cl

Rethinking Epistemic Uncertainty in Node Classification through Information Growth

arXiv:2610.03418v1 Announce Type: cross Abstract: Epistemic uncertainty should decrease as additional information about the data-generating process (DGP) becomes available to the predictor. Yet, exist

Rethinking Fixed Temporal Grids: Frequency-Disentangled Motion Generation

arXiv:2610.03012v1 Announce Type: new Abstract: Most human motion generation methods encode motion as tokens on a uniform temporal grid, where every token spans the same fixed time window. Human motio

researcharxiv-cs-cv

Rethinking RAG in Long Videos: What to Retrieve and How to Use It?

arXiv:2606.13141v2 Announce Type: replace Abstract: Retrieval-augmented generation is extending beyond text to long videos, where query-relevant chunks can be represented across multiple modalities an

Rethinking What to Cache in Few-Step Diffusion Transformers: Solver-Aware Target Selection

arXiv:2610.03577v1 Announce Type: cross Abstract: Diffusion Transformers (DiTs) can generate high-quality images and videos, but generating each sample requires multiple costly DiT forward passes. Two

Rethinking World-Action Model for Compositional and In-Context Robotic Manipulation

model-releasesarxiv-cs-ro

arXiv:2610.02368v1 Announce Type: new Abstract: Long-horizon compositional manipulation has become increasingly important for real-world robot deployment, where a single task involves multiple coordin

Revealing Epistemic Uncertainty in MLLMs via Causal-Invariant Masking

arXiv:2610.02887v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) suffer from hallucinations, creating a critical need for Uncertainty Quantification (UQ) to ensure reliable d

ReviewBench: An open benchmark for AI code review

model-releasesgithub-ai-blog

We’re launching ReviewBench, a benchmark for code review agents built on representative GitHub pull requests, multi-source ground truth, calibrated evaluation, and production-aligned metrics. The post

Revisiting Input Time-frequency Representations in Multi-pitch Estimation for Vocal Ensembles

arXiv:2610.03656v1 Announce Type: cross Abstract: Multi-pitch estimation in vocal ensembles is challenging because singers occupy overlapping pitch ranges and often sing at closely spaced fundamental

Revisiting Visual Representation Enhancement of VLMs via Kernel Canonical Correlation Analysis

arXiv:2610.02718v1 Announce Type: cross Abstract: Vision-language models such as CLIP exhibit strong semantic generalization, but remain limited in fine-grained visual perception. A recent work named

Reward Inflation: A Healthy Stimulus for Reinforcement Learning

arXiv:2610.02545v1 Announce Type: new Abstract: Reward serves as the primary learning signal in reinforcement learning (RL). However, while reward magnitudes are typically held fixed throughout traini

Applications

RIFAR: Reliability and Forgetting-Aware Replay for Continual Robot Learning

arXiv:2610.03079v1 Announce Type: new Abstract: Genuine embodied agency requires robots to turn continuous real-world experience into lasting, transferable skills. This demands continual learning that

applicationsarxiv-cs-ai

Right Order, Wrong Scale: Auditing LLM Judges for Occupational AI Measurement

arXiv:2610.02492v1 Announce Type: new Abstract: LLM judges are increasingly used to assess whether AI outputs meet workplace requirements, but agreement on response rankings does not establish agreeme

RINS: Residual-Image Neural Subspace Solvers for Large Sparse Linear Systems

model-releasesarxiv-cs-lg

arXiv:2610.02217v1 Announce Type: cross Abstract: Large sparse linear systems from PDE discretizations require correction subspaces whose operator images explain the current residual. We study this re

Local Ai

RIPPLE in Still Water: Zero-Shot Clustering in Federated Learning with Wavelet Scattering Transform

arXiv:2610.03054v1 Announce Type: new Abstract: Clustered Federated Learning (FL) partitions a client population into groups of similar local distributions and trains one specialized model per cluster

local-aiarxiv-cs-lg

Industry

RL environments now have a home on the @huggingface Hub. Every RL framework has its own way to find environments. Custom hubs, runtime regis…

RL environments now have a home on the @huggingface Hub. Every RL framework has its own way to find environments. Custom hubs, runtime registries, GitHub lists with a custom loader. If you publish for

industryclem-delangue--x

RMCW: A Deletion-Robust Watermark Based on Reed--Muller Codes for Language Models

model-releasesarxiv-cs-cl

arXiv:2610.02817v1 Announce Type: cross Abstract: Large Language Model (LLM) watermarking provides a lightweight mechanism for identifying text generated by a specific model, but its robustness remain

RNADyn: A Benchmark for Generating and Understanding RNA Dynamics

model-releasesarxiv-cs-lg

arXiv:2610.03712v1 Announce Type: new Abstract: Ribonucleic acid (RNA) functions through conformational changes that are not fully captured by static structures. However, large-scale standardized RNA

RoboBridge: A Self-Evolving Embodied Agent Framework for Sim-to-Real Transfer

arXiv:2610.02717v1 Announce Type: new Abstract: A key challenge in bringing embodied intelligence into the real world is transferring capabilities from simulation to reality and enabling agents to con

safetyarxiv-cs-ro

RoboChemGym: A Protocol-Driven Generative Simulation Framework for Long-Horizon Chemical Manipulation

model-releasesarxiv-cs-ro

arXiv:2610.02708v1 Announce Type: new Abstract: Wet-lab experimentation serves as the gold standard for hypothesis verification in scientific discovery; yet it is inherently labor-intensive, costly, a

Robust Adversarial Quantification via Conflict-Aware Evidential Deep Learning

arXiv:2506.05937v3 Announce Type: replace-cross Abstract: Reliability of deep learning models is critical for deployment in high-stakes applications, where out-of-distribution or adversarial inputs ma

ROUTEAUDIT: Interaction-Aware Identification for Budgeted Multi-Verifier Routing

arXiv:2610.02808v1 Announce Type: new Abstract: Adaptive multi-verifier systems are commonly compared through endpoint quality-cost gaps, even when the verifier catalog, availability, accounting, info

Applications

ROVE: Unlocking Human Interventions for Humanoid Manipulation via Reinforcement Learning

arXiv:2606.17011v2 Announce Type: replace-cross Abstract: Human interventions provide crucial corrective signals for post-training Vision-Language-Action (VLA) models. However, enabling seamless human

applicationsarxiv-cs-lg

RT-SFT: Text Style Transfer from Non-Parallel Corpora by Roundtrip Translation

arXiv:2602.15013v2 Announce Type: replace Abstract: Text style transfer (TST) is naturally a supervised task - rewrite a sentence in a target style while preserving its meaning - yet the parallel corp

researcharxiv-cs-cl

RUL-Aware RRT*: Degradation-Balanced Motion Planning for Robotic Manipulators

arXiv:2610.02469v1 Announce Type: new Abstract: Robotic manipulators operating over long durations often experience uneven joint degradation, which causes the weakest actuator to fail prematurely, lea

researcharxiv-cs-ro

RxnOptBench: Benchmarking LLMs for Reaction-Condition Optimization in Organic Methodology

arXiv:2610.02242v1 Announce Type: cross Abstract: Chemical reaction-condition optimization -- choosing the catalyst, ligand, solvent, reagent, temperature, time, and atmosphere that jointly maximize y

RYOPO: Bringing End-to-End Category-Level Object Pose Estimation into Real Time

arXiv:2610.03013v1 Announce Type: new Abstract: Category-level object pose estimation predicts the rotation, translation, and metric size of unseen instances within known categories. Many accurate RGB

researcharxiv-cs-cv

S^{2}-PINN: Stochastic Separable Physics-Informed Neural Networks

arXiv:2610.03303v1 Announce Type: new Abstract: Uncertainty quantification (UQ) for random partial differential equations (PDEs) is ubiquitous in computational science and engineering. However, classi

researcharxiv-cs-lg

S2S-JEPA: Predicting the Predictable at Subseasonal-to-Seasonal Timescales

arXiv:2610.03106v1 Announce Type: cross Abstract: The subseasonal-to-seasonal (S2S) timescale, roughly from two weeks to two months ahead, is a critical forecast window for sectors such as agriculture

Safe Streaming Flow Planning by Aligning Sampling Dynamics with Execution Dynamics

arXiv:2610.03132v1 Announce Type: cross Abstract: Generative planners based on diffusion/flow matching can learn to synthesize long-horizon trajectories from demonstrations. However, real-world deploy

Safeguarding Mutual Correction in Source-Free Domain Adaptation via Cut Statistics

arXiv:2610.02981v1 Announce Type: new Abstract: Source-Free Domain Adaptation (SFDA) aims to adapt a source-pretrained model to an unlabeled target domain without access to the original source domain.

Sam Altman says he's 'very uncomfortable' with attributing religious force to AI, calling it 'a real safety issue', as Anthropic engages with religious leaders (Ben Berkowitz/Axios)

Ben Berkowitz / Axios: Sam Altman says he's “very uncomfortable” with attributing religious force to AI, calling it “a real safety issue”, as Anthropic engages with religious leaders — OpenAI CEO Sam

safetytechmeme

Industry

Sam Altman says people need to accept ‘some bad things’ are going to happen if they want AI

OpenAI Group PBC Chief Executive Sam Altman insists that the security risks of artificial intelligence need to be taken seriously. But unlike his counterpart Dario Amodei of Anthropic PBC, he believes

industrysiliconangle

Industry

Sam Altman says ‘some bad things’ will happen, but AI is totally worth it

Sam Altman thinks that the benefits of AI will be so great that 'the world should accept some bad things happening' along the way. The OpenAI CEO pointed to hacks, scams, and 'other bad things' as cos

industrythe-verge-ai

Sample complexity of variance-reduced policy gradient: weaker assumptions and lower bounds

model-releasesarxiv-cs-lg

arXiv:2610.03165v1 Announce Type: new Abstract: Several variance-reduced versions of REINFORCE based on importance sampling achieve an improved O(epsilon^{-3}) sample complexity to find an epsilon-sta

SARI: Phase-Split Sim-Real Co-Training for Contact-Rich Manipulation

arXiv:2610.02804v1 Announce Type: new Abstract: Vision-language-action (VLA) models often require costly real-world demonstrations to adapt to contact-rich manipulation tasks, particularly when genera

safetyarxiv-cs-ro

SCAD: Structured Credit Assignment and Distillation for Long-Horizon Agents

arXiv:2610.03372v1 Announce Type: new Abstract: Training long-horizon agents to solve complex tasks requires effective supervision over extended interaction sequences. However, sparse terminal rewards

Scalar Federated Learning for Linear Quadratic Regulator

arXiv:2604.05088v2 Announce Type: replace-cross Abstract: We propose ScalarFedLQR, a communication-efficient federated algorithm for model-free learning of a common policy in linear quadratic regulato

Local Ai

Scale-Recursive Rectified Flows for Few-Step Precipitation Ensembles

arXiv:2610.02611v1 Announce Type: cross Abstract: Fine-resolution precipitation estimates support flood risk assessment and water management, but coarse satellite products cannot resolve rainfall with

local-aiarxiv-cs-cv

Scaling Trajectories for Complex Tasks through Recursive Self-Rewrite

arXiv:2610.02826v1 Announce Type: new Abstract: Successful trajectories on difficult tasks provide valuable supervision for model improvement, but specialized harnesses introduce interventions that ma

SceneFactory-3D: Lifting 2D Traffic Scenes into 3D Physical Counterfactuals for Scalable Physically Grounded Safety Evaluation

arXiv:2610.02874v1 Announce Type: new Abstract: Scalable driving simulators typically execute vehicle commands using prescribed behavioral or kinematic rules, overlooking the physics of tire-road inte

safetyarxiv-cs-ro