safety

ROOT: Discovering Rewards for User-Specified Embodied Behaviors

arXiv:2610.04250v1 Announce Type: new Abstract: Reinforcement learning for embodied control remains constrained by the difficulty of reward specification. Although recent large language model (LLM)-ba

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Safe Ergodic Control for Multi-Robot Systems via Quadratic Programming

DGX agent

arXiv:2610.05258v1 Announce Type: new Abstract: Ergodic control drives robots to spend time in each region in proportion to a spatial distribution of interest, making it well suited for dense spatiote

safetyarxiv-cs-ro

6 Oct 2026

Scenario-Based Compositional Statistical Model Checking for Safety Specifications

arXiv:2610.05571v1 Announce Type: cross Abstract: In safety-critical domains such as autonomous driving, systems must be evaluated across a large number of environment conditions, often represented as

Score-Calibrated Flow for Sampling from Unnormalized Densities with Applications to Generative Online Reinforcement Learning

arXiv:2610.04696v1 Announce Type: new Abstract: Diffusion and flow models provide expressive policy classes for online reinforcement learning (RL), enabling multimodal behaviors and improved performan

SEER: Self-Evolving Event Reasoning and Retrieval for Time Series Forecasting

arXiv:2610.04109v1 Announce Type: new Abstract: Real-world time series are frequently driven by exogenous events and structural shifts, rendering conventional forecasting based solely on historical nu

Selecting Long-Horizon Trajectories for Reliable and Efficient Terminal-Agent Training

arXiv:2610.05831v1 Announce Type: new Abstract: Terminal agents are commonly trained by imitating long teacher trajectories, yet how much of each trajectory to supervise remains unexplored. We study t

Sharp Convergence and Sample Complexity of Policy Mirror Descent for Average-Reward MDPs

arXiv:2610.04117v1 Announce Type: new Abstract: Policy mirror descent (PMD) has a mature finite-time theory in discounted Markov decision processes (MDPs), but less is known in the average-reward sett

Sharpen Without Search: On-Policy Distillation of Sequence-Level Power Distribution

arXiv:2610.06804v1 Announce Type: new Abstract: A language model can give a correct answer more probability than any single incorrect answer and still usually sample an incorrect one, because the inco

SimForcing: Distilling Simulation Motion Priors into Real-Domain Robot World Models

arXiv:2610.06598v1 Announce Type: cross Abstract: Action-conditioned robot world models must respond precisely to robot trajectories while preserving realistic visual dynamics, yet learning both from

Social Navigation for Tour-guide Robot

arXiv:2610.05455v1 Announce Type: new Abstract: We propose a force-based model for social navigation of a tour-guide robot. Social forces due to various factors like obstacles, user position and headi

SPDAlign: Interpretable Riemannian Alignment for EEG Forward Modeling Shifts

arXiv:2610.06315v1 Announce Type: new Abstract: Electroencephalography (EEG) based brain-computer interfaces enable direct brain-to-device communication for applications such as rehabilitation and com

STC-MPM: Coupled Deformation, Progressive Damage, and Cut Formation in Soft-Tissue Cutting

arXiv:2610.06198v1 Announce Type: new Abstract: Cutting is a recurring operation in robotic au tomation, from food preparation to surgical tissue resection. For highly compliant targets, blade motion

StepCAD: Mesh-to-CAD Code Generation via LLM Policy and Geometry-Guided Search

arXiv:2610.03799v1 Announce Type: new Abstract: Recovering executable CAD programs from 3D meshes is challenging due to the compositional nature of CAD construction and the interaction between discret

Strategic Multi-Agent Learning for Interpretable Action Valuation of All Players in Football

arXiv:2610.05961v1 Announce Type: new Abstract: Valuing player actions in football requires accounting for strategic interactions among 22 players, including off-ball movements and defensive positioni

Streaming Multi-Track Timeline Control for 3D Human Motion Generation

arXiv:2610.03873v1 Announce Type: new Abstract: Text-driven human motion generation has advanced substantially, yet most methods assume instructions are available before synthesis. Interactive applica

Structured Representation Learning for Behavior Cloning: How can we learn to safely control a nuclear power plant?

arXiv:2610.06211v1 Announce Type: new Abstract: Learned models for industrial control are usually judged by aggregate accuracy, but accuracy at the component level does not guarantee safety once it is

SUAVE: Unified Video-Action Models via Masked Diffusion

arXiv:2610.04009v1 Announce Type: cross Abstract: Vision-language-action models (VLAs) inherit strong semantic grounding from pretrained vision-language backbones but are typically optimized for predi

TACET: Context-Appropriate Acoustic-Social Navigation for Quadrupeds

arXiv:2610.03828v1 Announce Type: new Abstract: Quadruped robots entering hospitals, care homes, and quiet offices must be context-appropriate not only in where they move but in how loudly they move:

TacOT: Learning Contact-Rich Dexterous Manipulation from Human Demonstrations via Tactile-Guided Optimal Transport

arXiv:2610.04363v1 Announce Type: new Abstract: Learning contact-rich dexterous manipulation from human demonstrations provides a scalable source of interaction data, yet transferring such skills to r

TAME:Topology-Aware Text-Driven Motion Editing across Heterogeneous Humanoid Skeletons

arXiv:2610.04529v1 Announce Type: new Abstract: Text-driven motion editing modifies an existing motion sequence according to a text instruction while preserving the content of the source motion. Exist

Target-free Latent Safety Alignment

arXiv:2610.04467v1 Announce Type: new Abstract: Large language models (LLMs) remain highly vulnerable to jailbreak attacks that induce harmful behaviors and circumvent safety alignment. To defend agai

Targeted Active Learning for Preference-Based Treatment Effects on Multivariate Outcomes

arXiv:2610.03824v1 Announce Type: cross Abstract: Treatment efficacy is traditionally demonstrated on the basis of a single primary outcome. However, clinical decision-making usually requires consider

Task Inference Beyond Least Squares in Behavioral Foundation Models

arXiv:2610.05350v1 Announce Type: new Abstract: Behavioral Foundation Models (BFMs) aim to solve a wide range of downstream tasks without test-time policy learning by inferring a task vector from the

Task-Sensitive Geometry of Representation Transfer for Object Detection under Image Degradation

arXiv:2610.04627v1 Announce Type: new Abstract: Object detection under image degradation can benefit from clean-image supervision, but aggregate gains do not imply that transferred representation chan

TDC: Sim-to-Real Transferable Directional Compliance for Contact-Rich Manipulation

arXiv:2602.14174v2 Announce Type: replace Abstract: Contact-rich manipulation requires robots to regulate both motion and interaction forces, yet achieving adaptive compliance remains a fundamental ch

Teaching Agents to Code Reliably

arXiv:2610.03984v1 Announce Type: cross Abstract: Autonomous coding agents solve repository issues by reading code, running commands, editing files, and submitting patches. Extra inference-time comput

Test-Time Training as Residual Memory for Robot Policies

arXiv:2610.04701v1 Announce Type: new Abstract: Memory is essential for long-horizon robotic manipulation, where successful actions may depend on past events that are no longer recoverable from the cu

The Arbitrary-Placement Problem in Entropy-Minimizing Selection, and a Residual-Entropy Formulation

arXiv:2610.05925v1 Announce Type: new Abstract: Entropy-based selection objectives suffer from a fundamental degeneracy: minimizing Shannon entropy H(p_A) rewards confident selection regardless of whe

The Score Is Not the Structure: Brain Alignment and Cross-Lingual Transfer

arXiv:2610.03827v1 Announce Type: cross Abstract: Researchers often support the claim that a model shares structure with the brain, or across languages, by reporting a similarity score. We ask what su

The Unexpired Plan: A Free Monitor for Accelerated Diffusion Policies

arXiv:2610.05747v1 Announce Type: new Abstract: Training-free acceleration of a diffusion policy is accepted when an internal similarity signal reports that the shortcut changed nothing. We price ever

ThunderSyncRL: Lossless Acceleration of Agentic Reinforcement Learning

arXiv:2610.05935v1 Announce Type: new Abstract: Language models are moving beyond generating answers to pursuing long-horizon goals in interactive environments. Post-training these agents requires lon

Towards causal effect estimation with learned instrument representations

arXiv:2602.10370v3 Announce Type: replace-cross Abstract: Instrumental variable (IV) methods mitigate bias from unobserved confounding in observational causal inference but rely on the availability of

Towards Safer Autonomous Driving in an Open World: A Dual-Process Approach

arXiv:2610.04088v1 Announce Type: cross Abstract: Before autonomous driving systems can be deployed on public roads, it is vital that these systems comply with safety standards, traffic rules, and soc

Transfer-Stratified On-Policy Distillation for RL-Improved Reasoning Teachers

arXiv:2610.05974v1 Announce Type: new Abstract: Reinforcement learning can substantially improve a reasoning teacher, but it is unclear which of those improvements survive when the teacher supervises

Transporting Unsecured Stacked Payloads with a Quadrupedal Robot via Multi-Objective Reinforcement Learning

arXiv:2610.05819v1 Announce Type: cross Abstract: Transporting unsecured payloads with legged robots over uneven terrain requires balancing locomotion performance and payload stability, since aggressi

TRIM-ReID: Duplication-Aware Token Reduction and Modality-Aligned Interaction for Multi-Modal Object Re-Identification

arXiv:2610.04361v1 Announce Type: new Abstract: Multi-modal object re-identification exploits complementary RGB, near-infrared (NIR), and thermal-infrared (TIR) observations to retrieve target objects

TUCO: Curating Simulation Demonstrations for Sim-to-Real Robot Policy Co-Training

arXiv:2610.05407v1 Announce Type: new Abstract: Simulation demonstrations can supplement scarce real-world data for robot policy co-training. However, the value of using data curation to actively sele

TurboPairFormer: Fast and Stable Protein Folding Model Training with an Optimized Triangle Attention Kernel

arXiv:2610.05854v1 Announce Type: new Abstract: Triangular attention is a core computation in AlphaFold3-style biomolecular models, with cubic cost in token count. Its shared pair bias adds a gradient

Universality and Convergence of Generative Flows

arXiv:2610.05490v1 Announce Type: new Abstract: Generative flows sample from an unnormalized target by training a flow to be balanced, and the training loss is the signal a practitioner watches. We as

VAMPS: Visual and Motor Policies from Sampling-Based Planning

arXiv:2610.05331v1 Announce Type: cross Abstract: Learning robot policies directly on physical systems remains difficult because data collection is costly and policy exploration can be unsafe. We intr

Variance-Aware Fine-Grained Gap-Dependent Bounds for Online Reinforcement Learning

arXiv:2610.04752v1 Announce Type: cross Abstract: We study model-free online reinforcement learning (RL) for episodic tabular Markov decision processes, focusing on both gap-dependent regret and polic

VGGT-Bridge: Beyond Sequential Pose Graphs via Coarse-Stride Skip Edges

arXiv:2610.06594v1 Announce Type: new Abstract: Feed-forward visual geometry transformers such as VGGT reconstruct dense 3D structure from images in a single forward pass, simplifying multi-view 3D re

Vision-enabled detection of safety helmet compliance in construction zones

arXiv:2610.05756v1 Announce Type: new Abstract: In the rapidly evolving field of construction management, worker safety remains a top priority. This paper introduces an innovative vision-based system

Visual Grounding Safety in Vision-Language Models

arXiv:2610.05637v1 Announce Type: cross Abstract: Vision-language models (VLMs) are increasingly trained to generate structured outputs like points and bounding boxes that downstream interfaces, agent

Visual Swarm Navigation via Deep Reinforcement Learning and Evolutionary Hybrid Design

arXiv:2610.06400v1 Announce Type: new Abstract: Swarm robotics presents a robust and cost-effective paradigm for advanced automation in complex, dynamic environments, such as those encountered in sear

WASP: Weakly Aligned Spatiotemporal Pairs for Fetal Brain MRI-Ultrasound Learning

arXiv:2610.04601v1 Announce Type: new Abstract: Magnetic Resonance Imaging (MRI) is widely regarded as the optimal sensor for fetal brain analysis due to its superior soft-tissue contrast and anatomic

Watermarking: from Impossibility to Auditable Compliance

arXiv:2610.06317v1 Announce Type: cross Abstract: Article 50 (2) of the EU Artificial Intelligence Act requires providers of generative systems to make synthetic outputs machine-readable and detectabl

When to Switch: Reliable Action-Chunk Extension for Vision-Language-Action Models

arXiv:2610.05719v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models serve as unified policies for robotic manipulation, yet their expensive inference forces robots to pause between pol