Search: “safety”

Awomo-SimDataEngine: Agentic Simulation-ReadyWorld Generation

arXiv:2610.02274v1 Announce Type: new Abstract: Generating useful robot-training data requires more than visually plausiblescenes: objects must support interaction, placements must remain physicallyva

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Beyond Reward Hacking: Proxy Divergence Across Four Layers of a Staged Humanoid Learning Pipeline

DGX agent

arXiv:2610.03196v1 Announce Type: new Abstract: A reinforcement-learning (RL) pipeline for a legged robot is assembled from proxies. A reward stands in for intended behaviour, a curriculum gate stands

safetyarxiv-cs-ro

5 Oct 2026

Bidirectional Voronoi-biased Exploration Curriculum for Reinforcement Learning

arXiv:2610.03395v1 Announce Type: new Abstract: Long-horizon tasks with sparse rewards pose an exploration bottleneck for goal-conditioned reinforcement learning: a policy started from the initial sta

CHASE-VLA: Post-Training Quantization Framework for Vision-Language-Action Models with Chunk-Aware Scale Estimation

arXiv:2610.02666v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models map visual observations and language instructions to continuous robot actions, but a diffusion-based action expert (

CLIMB: Confidence-Guided Complementary Evidence for Multimodal Retrieval-Augmented Generation

arXiv:2610.03421v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) have shown strong visual reasoning abilities, but knowledge-intensive visual question answering often requires

ConvoDrift: A Multi-Turn Conversational Dataset for Modeling Stylistic Tone Evolution

arXiv:2610.02873v1 Announce Type: cross Abstract: The evolution of linguistic style in conversations is an underexplored issue in NLP. Most style-control datasets focus on sentences or assume a static

Local Ai

CORNAV: Construction-Aware Reasoning for Robot Navigation on Active Worksites

arXiv:2610.03622v1 Announce Type: new Abstract: The construction industry faces persistent labor shortages, low productivity that costs the global economy over $1.6 trillion annually, and one of the h

local-aiarxiv-cs-ro

Correcting Guided Diffusion Trajectories with Spectral Alignment

arXiv:2610.02753v1 Announce Type: cross Abstract: The practical success of conditional image generation hinges on fine-grained differences in condition alignment and visual fidelity. Classifier-free g

Coverage You Can Steer: Online Conformal Calibration for RL-Driven Hardware-Aware NAS

arXiv:2610.03127v1 Announce Type: new Abstract: Hardware-aware neural architecture search (NAS) is dominated by evaluation cost: every architecture must be trained before its reward is known. Conforma

Credit Where It Matters: Dependency-Aware Policy Optimization for Terminal Agents

arXiv:2610.03634v1 Announce Type: new Abstract: Terminal-using agents benefit from reinforcement learning (RL) in coding, debugging, and other multi-step terminal tasks. In these tasks, later commands

CuBEs: Culturally-Situated Behavioral Evaluations and the Limitations of Culture-Blind LLM Judges

arXiv:2610.02622v1 Announce Type: new Abstract: Evaluating the occurrence and triggers of large language model (LLM) behaviors - such as sycophancy, self-preference, or over-confidence - is critical f

CUEing User Simulators: Calibrated User Embeddings for Multi-Turn Benchmarking

arXiv:2610.02460v1 Announce Type: new Abstract: Recent benchmarks rely on user simulators to evaluate AI agents in multi-turn interaction. While existing simulation techniques demonstrate surface fide

DEPICT: Scoring Text-to-Image Alignment by Answer Agreement

arXiv:2610.03617v1 Announce Type: new Abstract: Image-text alignment is a core problem in computer vision with applications in caption evaluation, hallucination detection, data curation, and the bench

Detect and Suppress: A Mechanistic Defense against Adversarial Patches in VLA Models

arXiv:2610.03498v1 Announce Type: cross Abstract: Adversarial patches can disrupt Vision-Language-Action (VLA) models by manipulating visual observations, leading to failures in robot control. However

Differentiable Koopman Operator for Contrastive Learning on Dynamic Graphs

arXiv:2610.02990v1 Announce Type: new Abstract: Real-world interaction networks are inherently dynamic: edges form and dissolve as node behavior shifts over time. Most snapshot-based contrastive metho

Divergence controls entropy in distillation

arXiv:2610.03529v1 Announce Type: cross Abstract: Distillation has become a core primitive of large language model training, but its properties are not yet well understood. We take an entropic perspec

DriftTTS: Few-Step Text-to-Speech Without Distillation via Distribution-Matching Drift

arXiv:2610.03390v1 Announce Type: cross Abstract: Few-step neural text-to-speech models often rely on short- ened diffusion or flow-matching schedules, or on distillation from pretrained multi-step te

DuoMatching: Joint-Marginal Distribution Matching for Few-Step Video Generation

arXiv:2610.03543v1 Announce Type: new Abstract: Streaming video generation has benefited from distribution matching distillation (DMD), which matches the joint distribution of video frames to a video

Efficient Neural Field Learning via Adaptive Coverage and Focused Sampling

arXiv:2610.02410v1 Announce Type: cross Abstract: Implicit neural representations (INRs) provide a flexible framework for modeling high-dimensional continuous fields, but their training is often ineff

EpiWorld: Grounding LLM Policy Agents in Epidemiological World Models

arXiv:2610.02744v1 Announce Type: new Abstract: Epidemic intervention policies are textual artefacts that human decision-makers interpret, justify, and revise through natural language, making large la

Equivariant Visual-Tactile Diffusion Policy for Contact-Rich Manipulation

arXiv:2610.03333v1 Announce Type: cross Abstract: Imitation learning for contact-rich manipulation requires high-quality expert data that is expensive to obtain. This makes learning a sample-efficient

Evaluating LLM-as-a-Judge Beyond Score Alignment: A Psychometric Analysis of Residual Judging Difficulty

arXiv:2610.02877v1 Announce Type: new Abstract: Large language models (LLMs) are widely used as automatic judges, with validity typically assessed via alignment with human scores. However, aggregate a

EVEWorld: Physical Evolution Supervision for Embodied World Models

arXiv:2610.03374v1 Announce Type: new Abstract: Embodied world models enable scalable simulation of embodied interactions for robot learning. However, existing models are prone to Model Laziness, as t

EVOL: Simulator-Guided Evolutionary Expert Synthesis for Deployment-Free Learning Path Recommendation

arXiv:2610.03273v1 Announce Type: new Abstract: Reinforcement learning (RL) for learning path recommendation (LPR) faces two coupled obstacles. First, the policy must commit to a sequence of L concept

EyeRobot 2.0: Active Gaze for Precise Manipulation without Wrist Cameras

arXiv:2610.03710v1 Announce Type: cross Abstract: Inspired by human vision, we introduce a framework using active gaze to enable fine-grained bimanual manipulation with only a single stereo camera. Ey

FastOPD: On-Policy Distillation for Lightweight VLA Deployment

arXiv:2610.02832v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) foundation models have scaled rapidly to enhance manipulation performance and generalizability, but this scaling incurs h

FlowHMR: Physically Plausible Motion Capture from Video

arXiv:2610.03691v1 Announce Type: new Abstract: We present FlowHMR, a framework for recovering physically plausible global 3D human motion from monocular video. Previous learning-based methods typical

Follow the Winners: Conservative Policy Improvement with the Cross-Entropy Method for Critic-Free RFT

arXiv:2610.03361v1 Announce Type: cross Abstract: Critic-free reinforcement fine-tuning (RFT) for agentic large language models is often done through GRPO-style methods, which compute a group baseline

Forecasting from Counterfactual Simulator Rollouts: A Sim2Real Evaluation

arXiv:2610.03662v1 Announce Type: new Abstract: Deploying a new decision policy creates a cold-start problem for prediction models whose targets depend on the policy's actions: historical observations

FSPO: Policy-Consistent Risk and Pareto-Feasible Control for Budgeted LLM RL Post-Training

arXiv:2610.02828v1 Announce Type: new Abstract: Adaptive LLM reinforcement-learning post-training changes multiple training actuators online, including rollout temperature, group size, clipping, KL re

Gains and Collapse in On-Policy Distillation:A Reinforcement Learning Perspective

arXiv:2610.03185v1 Announce Type: new Abstract: On-policy distillation (OPD) has become an important approach to language model post-training. However, despite its performance gains, OPD can also coll

GeoScaffold: Learning Compact Geometric Latents via Reconstruction for Efficient Vision-Language Navigation

arXiv:2610.02697v1 Announce Type: cross Abstract: Recent vision-and-language navigation (VLN) systems increasingly adopt streaming Video-LLM policies that map egocentric RGB observations and instructi

GTDD: Generative Test-Driven Development for AI Coding Agents with Adversarial Testing

arXiv:2610.02952v1 Announce Type: cross Abstract: Test-driven development gives AI coding agents executable requirements for implementing software. Because these agents can adapt their implementations

hacktrace: behavior-supervised detection of reward hacking during code generation

arXiv:2610.03055v1 Announce Type: new Abstract: A coding agent can earn a passing grade by fixing its code, or by deleting the test that exposes the bug. Detecting such reward hacking requires recogni

Heads, Tails, and AI Fails: LLMs, Randomness, and Human Judgments

arXiv:2406.00092v2 Announce Type: replace Abstract: Randomness is central to human cognition and to many applications in which large language models are deployed, yet probabilistic token generation do

How Causality Bridges the Semantic Gap

arXiv:2610.02594v1 Announce Type: cross Abstract: Numerical measurements capture how a system behaves, but often leave the meanings of its variables unspecified. Some variables are measured but never

IDRF: Inverse-Distilled Reward Fine-tuning of Masked Discrete Diffusion Models

arXiv:2610.03641v1 Announce Type: new Abstract: Masked discrete diffusion models offer a promising alternative to autoregressive generation, but iterative sampling can be costly, and intractable seque

Imagine the Future, Internalize the Gist: Efficient VLA Reasoning via Internalized Spatiotemporal Imagination

arXiv:2610.02626v1 Announce Type: new Abstract: Vision-language-action (VLA) models increasingly incorporate intermediate reasoning to improve robotic manipulation, yet existing approaches primarily r

Interpreting at Write Time: A Policy Ablation for Multi-Goal Agent Memory

arXiv:2610.02897v1 Announce Type: new Abstract: A long-running assistant cannot keep everything it has seen, so it summarises. Summarising is not neutral: what is kept is chosen against some notion of

Iterating Consistency Models: Stability, Error Bounds and Noise Schedules

arXiv:2610.03414v1 Announce Type: cross Abstract: Consistency models (CMs) have become a leading approach for generating high-quality samples in few steps. However, adding steps can improve or degrade

KungfuAthleteBot: learning high-dynamic humanoid motion from video with unified robust recovery

arXiv:2610.03388v1 Announce Type: new Abstract: Video is an abundant, inexpensive source of human motion data that is rich in extreme athletic behaviors. Making it usable for humanoid robots, however,

safetyarxiv-cs-ro

Labels Override Definitions in Jev-Style Typed Decision Models

arXiv:2610.02586v1 Announce Type: new Abstract: A typed decision model answers a fixed question about an input by returning a probability for each of several caller-defined options. Each option carrie

LatticeSMC: Where to Spend Inference-Time Compute in Chunked Sequence Generators

arXiv:2610.02774v1 Announce Type: new Abstract: Long-form generators for music, motion and video produce sequences chunk by chunk, with each chunk generated by iterative denoising while rewards are de

Learning from Evolving Errors: Adaptive Iterative Repair for On-Policy Distillation

arXiv:2610.02700v1 Announce Type: cross Abstract: On-policy self-distillation (OPSD) supplies dense token-level feedback on trajectories sampled from the student's own policy, a richer training signal

Learning Reflexive Behavior for Contact-Rich Manipulation

arXiv:2610.02811v1 Announce Type: new Abstract: During contact-rich manipulation, interactions between a robot and its environment carry information about local geometry: a surface prevents penetratio

safetyarxiv-cs-ro

Learning Style, Forgetting Semantics: A Case Study of SFT and RFT on Classification Tasks

arXiv:2610.02437v1 Announce Type: cross Abstract: Why does supervised fine-tuning (SFT) lead to more forgetting than reinforcement fine-tuning (RFT), even when all teacher demonstrations are semantica

Learning to Revise Reasoning with Segment-wise On-Policy Distillation

arXiv:2610.02703v1 Announce Type: new Abstract: On-policy distillation (OPD) improves large language model reasoning by training students on their own rollouts with dense token-wise supervision from t

Learning Transferable Policies from Action-free Time Series Through Dynamical Embeddings

arXiv:2610.03065v1 Announce Type: new Abstract: Learning control from action-free recordings is challenging because intervention effects are unobserved and policies may exploit errors in reconstructed