Search: “safety”

Seek-and-View Reasoning for Multi-View Spatial Understanding

arXiv:2610.11810v1 Announce Type: new Abstract: Existing approaches to multi-view spatial reasoning operate largely on sparse input views. Vision-language models (VLMs) are thus restricted to understa

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SimVLA: Zero-Shot Sim-to-Real VLA Learning for Mobile Manipulation

DGX agent

arXiv:2610.11248v1 Announce Type: new Abstract: Large-scale, diverse datasets have driven the success of LLMs and VLMs. But VLAs for robotics remain limited by the cost and complexity of real-world da

safetyarxiv-cs-ro

9 Oct 2026

SkillWeave: Weaving Heterogeneous Demonstrations into Long-Horizon Manipulation Skills

arXiv:2610.12046v1 Announce Type: new Abstract: Dexterous manipulation requires both large-scale task progression and precise contact-rich interaction, making it challenging to collect demonstrations

Spatial-Frequency-Aware Implicit Neural Representation of Multidimensional Signals via MLP-KAN Fusion

arXiv:2610.11296v1 Announce Type: new Abstract: Implicit Neural Representations (INRs) have emerged as a compelling paradigm for modeling multidimensional signals by mapping continuous coordinates to

SpatialHarness: Test-Time Spatial Scaffolding for Fine Robotic Manipulation

arXiv:2610.12457v1 Announce Type: new Abstract: Frontier multimodal foundation models (e.g., GPT-6 Astra) have recently shown strong potential for direct robotic control, yet their performance on fine

SpatialOPSD: Self-Distilling Spatial Intelligence from Verified Coding Agent Traces

arXiv:2610.11366v1 Announce Type: new Abstract: Spatial coding agents significantly improve spatial reasoning in Multimodal Large Language Models (MLLMs) by using external tools to generate verified e

SPLIT-RL: Staged Perception-Language Reasoning Training with Claim-Level Advantages

arXiv:2610.10889v1 Announce Type: new Abstract: Vision-Language (VL) reasoning requires a model to both extract relevant and accurate information from an image (visual reasoning, VR), and to infer the

SynCo: Data Synthesis Co-Training for Self-Evolving LLMs via Multi-Agent Reinforcement Learning

arXiv:2610.11345v1 Announce Type: new Abstract: Self-evolving LLM agents promise to improve autonomously through continual interaction and learning, reducing their dependence on manually curated super

Synthesis Through Simulation: Generating Coherent Enterprise Data via Scalable Agent-System Interaction

arXiv:2610.10549v1 Announce Type: new Abstract: Tool-calling agents have become central to enterprise AI, yet training and evaluating them at scale remains severely constrained due to business and leg

TacHair: Tactile Contact-Distribution Guided Online Correction for Robotic Hair Stroking and Perception

arXiv:2610.10637v1 Announce Type: new Abstract: Hair stroking is common in daily grooming and personal care, and is also widely used in hair-product evaluation, motivating robots with similar physical

TACROSS: An Efficient and Low-Cost Scalable Human Touch System Across Heterogeneous Tactile Sensors for Dexterous Robot Learning

arXiv:2610.11945v1 Announce Type: cross Abstract: Collecting tactile demonstrations on robots is costly and slow, motivating the use of lower-cost human tactile gloves for scalable data collection. Ho

Teaching a Robot Dog New Tricks: Diverse Quadruped Skills via Combined Reinforcement and Imitation Learning with Adversarial Task Selection

arXiv:2610.10601v1 Announce Type: new Abstract: Reinforcement Learning (RL) has enabled legged robots to perform a range of skills in single-task settings. However, applications such as farm robotics

Teaching PPG How not Who: Fixed-Effects Distillation from ECG

arXiv:2610.10662v1 Announce Type: new Abstract: ECG is widely used to teach PPG-only models, yet what it teaches is unexamined. Wearables are valued for tracking how a person's cardiovascular state ch

The Polytopal Neural Network

arXiv:2610.12004v1 Announce Type: cross Abstract: Understanding how deep neural networks process information remains a central challenge. Existing interpretability methods often compromise structural

The Review Lottery: Benchmarking an Observational Estimator of Peer-Review Noise (ICLR 2017-2025)

arXiv:2610.06591v2 Announce Type: replace Abstract: How much of a conference accept/reject decision would change if the same paper were reviewed by a different set of reviewers? Running a second indep

Toward Optimal Regret in Adversarial MDPs with Stochastic Hard Constraints

arXiv:2610.12153v1 Announce Type: new Abstract: We study episodic constrained Markov decision processes with adversarial losses under stochastic hard constraints. Specifically, starting from a known s

TRACE: A Governance Framework for Measuring Explainability Debt in Production AI Systems

arXiv:2610.10957v1 Announce Type: cross Abstract: Production AI systems deployed in high-stakes domains accumulate a governance liability that existing monitoring frameworks fail to detect: the progre

Unifying Policy Learning and State Prediction through Spatial Language Modeling

arXiv:2610.12172v1 Announce Type: cross Abstract: Learning how actions change scene geometry can provide complementary supervision for goal-directed manipulation. We introduce Spatial Language Modelin

VAMR: Multi-Question Agentic Reasoning for Efficient Long-Form Video Understanding

arXiv:2610.11171v1 Announce Type: cross Abstract: Long-form video understanding often involves multiple questions about different aspects of the same recording. Yet existing video agents typically pro

Verdict Without the Rule: Diagnosing and Auditing Regulatory Rule Sensitivity in LLM Compliance Systems

arXiv:2610.12313v1 Announce Type: new Abstract: Large language model compliance systems are deployed on the assumption that a verdict depends on the regulatory rule it is given. We test this directly

Verification and Self-Improvement in Agentic AI: Foundations and Limits

arXiv:2610.10611v1 Announce Type: new Abstract: Agentic AI systems can improve by searching longer, receiving additional support, or modifying how they propose and verify outputs. A performance score

ViSkill: Reinforcing VLM Agents with Evolving Visual-Native Skills

arXiv:2610.12403v1 Announce Type: cross Abstract: Skill-augmented agents improve sample efficiency by distilling successful trajectories into reusable strategies. Yet most existing approaches remain t

safetyarxiv-cs-cl

WAM-Cache: Staleness-Bounded KV Reuse for Efficient World Action Models

arXiv:2610.11401v1 Announce Type: cross Abstract: World Action Models (WAMs) enable generalist robot manipulation by conditioning an action expert on representations from a pretrained video Diffusion

WAND: Learning Robust Navigation under Complex Wind Disturbances and Dense Obstacles for Quadrotors

arXiv:2610.11809v1 Announce Type: new Abstract: Robust navigation in cluttered environments remains a fundamental challenge for quadrotors, particularly when strong wind disturbances arise, which pert

When Do We Need On-Policy Distillation? Distilling on Offline Student Rollouts Is Often Better

arXiv:2610.11291v1 Announce Type: new Abstract: On-policy distillation (OPD) has become increasingly popular for transferring teacher capabilities to student models. In this work, we ask a critical re

safetyarxiv-cs-cl

When KL Regularization Misfires in Group Policy Optimization

arXiv:2610.12161v1 Announce Type: cross Abstract: Why does removing reference-policy KL regularization sometimes improve group policy optimization? This motivates studying how reference-policy informa

safetyarxiv-cs-cl

When Listening Becomes Easier: Scrubbing Visual Cues for Shortcut-Free VLAs

arXiv:2610.10912v1 Announce Type: cross Abstract: Shortcut learning is a prevalent issue in robot learning. The limited diversity of robot demonstration datasets can mislead policies into exploiting s

When Should Agents Think? Adaptive Reasoning via Cross-Turn Estimation

arXiv:2610.12061v1 Announce Type: new Abstract: Large language model (LLM)-based agents have demonstrated strong capabilities on complex tasks. They typically perform reasoning before each action thro

When to Intervene? State-Aware Sparse Manipulation in Federated Reinforcement Learning

arXiv:2610.11523v1 Announce Type: new Abstract: Federated reinforcement learning (FRL) enables distributed agents to collaboratively train decision-making policies, but its decentralized training proc

World-Model Policy Arbiter for Goal-Conditioned Reinforcement Learning

arXiv:2610.10932v1 Announce Type: new Abstract: Offline goal-conditioned reinforcement learning (GCRL) has produced a diverse set of goal-reaching algorithms, yet no single algorithm performs best acr

WorldAlign: Decoupled 4D Reward for World-Consistent Video Generation

arXiv:2610.12382v1 Announce Type: new Abstract: Faithful visual world simulation requires generated videos to maintain 4D world consistency, encompassing both static and dynamic consistency. Static co

A Closed-Loop Non-Asymptotic Convergence Analysis of PPO with Learned Critics and Clipping

arXiv:2610.10273v1 Announce Type: new Abstract: Despite its widespread use, Proximal Policy Optimization with clipping (PPO-Clip) remains difficult to tune, and the interactions among critic learning,

A Framework for the Systematic Review of ML Assets in AI Registries

arXiv:2610.09551v1 Announce Type: new Abstract: Background: Modern software systems increasingly rely on Machine Learning (ML) assets (i.e., pre-trained models, datasets, benchmarks) for building, eva

A Good Self-Teacher Meets the Student Where They Are: Joint On-Policy Learning and Teaching

arXiv:2610.10447v1 Announce Type: new Abstract: Reinforcement Learning (RL) from outcome rewards suffers from sparse supervision, particularly on difficult, long-horizon tasks where successful traject

A Strength-Monotonic Law for Domain Alignment in Frozen-Embedding Bioacoustic Classification

arXiv:2610.09737v1 Announce Type: cross Abstract: When does distribution alignment help a frozen foundation-model embedding generalize across acoustic domains? For cross-domain mosquito-species classi

Adversarial RL for Port-Scan Evasion: Attacker Feature Visibility in Edge-Deployed IDS

arXiv:2610.08864v1 Announce Type: cross Abstract: Machine learning-based intrusion detection systems (IDS) are increasingly used in resource-constrained Internet of Things (IoT) environments, yet thei

Agentic Scene Policies

arXiv:2509.19571v2 Announce Type: replace-cross Abstract: Designing or learning robot policies that generalize zero-shot across a range of language instructions and objects is a core problem in roboti

Allocation Stability and Wald Inference under Variance-Aware UCB

arXiv:2412.08843v3 Announce Type: replace-cross Abstract: Allocation stability is often used to justify Gaussian inference from bandit data, but when is it necessary? In this paper, we address this qu

An Informational Curse of Horizon in Goal-Conditioned Policy Learning

arXiv:2610.09247v1 Announce Type: new Abstract: The difficulty of learning goal-reaching policies is often attributed to a 'curse of horizon' that manifests as bias accumulation in temporal-difference

Ask the Expert: LLM-Guided Reinforcement Learning for Autonomous Cyber Defense

arXiv:2610.09337v1 Announce Type: cross Abstract: Policy-based reinforcement learning (RL) approaches have produced promising results for autonomous cyber defense; however, they are sample-inefficient

Average-Reward Reinforcement Learning for Multichain MDPs: A Hierarchical Decomposition Approach

arXiv:2610.10326v1 Announce Type: new Abstract: We study learning optimal policies in average-reward multichain Markov decision processes (MDPs), where the optimal gain may depend on the initial state

Beyond Masks and Trajectories: Flow-Guided Latent Action Injection for Stable Surgical Video Generation

arXiv:2610.09800v1 Announce Type: new Abstract: Surgical video generation holds substantial potential for surgical education, simulation, and data augmentation, yet generating surgical videos with rea

Beyond Reconstruction: What Matters in Action Tokenization for Robot Policies?

arXiv:2610.09170v1 Announce Type: new Abstract: Autoregressive action-token policies such as vision-language-action models require action tokenizers to translate discrete token sequences into precise

Boundary-aware Reinforcement Learning for Hypercube State Spaces via Deterministic Policy Gradient

arXiv:2610.09712v1 Announce Type: cross Abstract: We develop a continuous-time deterministic policy gradient framework for reinforcement learning with reflected state dynamics, where the state process

CAP: Codebook-Aligned Prediction for Tokenized Robot Policies

arXiv:2610.09178v1 Announce Type: new Abstract: Action tokenization converts continuous robot actions into discrete symbols that can be modeled autoregressively. However, existing tokenizer-based poli

CM-DPO: Constraint-Margin Direct Preference Optimization for LLM Planning

arXiv:2610.09219v1 Announce Type: cross Abstract: Direct Preference Optimization (DPO) treats all constraint violations equally: a 1 budget overshoot and a 1,000 overshoot induce the same training sig

CMP-IRRT*: A Perception-Assisted Height-Adaptive Planner for Quadruped Robots

arXiv:2610.10470v1 Announce Type: new Abstract: Quadruped robots can traverse low obstacles, but many 2D planning pipelines still model obstacles as binary occupied regions and rely on sampling-based

Co-Evolving Robot Orchestrators and Policies through Deployment

arXiv:2610.09228v1 Announce Type: new Abstract: Vision-language-action (VLA) policies trained on large datasets are capable within their training domains, yet they still fail to generalize to the vari