Search: “safety”

Multi-Agent Reinforcement Learning for Safe Autonomous Driving Under Pedestrian Behavioral Uncertainty

arXiv:2605.20255v1 Announce Type: new Abstract: Simulation-based testing of self-driving cars (SDCs) typically relies on scripted or simplified pedestrian models that do not capture the heterogeneity

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Safety

Preference-aware Influence-function-based Data Selection Method for Efficient Fine-Tuning

DGX agent

arXiv:2605.21422v1 Announce Type: new Abstract: As LLMs continue to scale, improving training efficiency increasingly depends on using data more effectively. Data selection addresses this problem by a

safetyarxiv-cs-lg

21 May 2026

Quadratic Characterizations for Reachability Analysis of Neural Networks

arXiv:2605.20482v1 Announce Type: new Abstract: Quadratic constraints (QCs) are widely used to characterize nonlinearities and uncertainties, but generic analytical characterizations can be conservati

Spacetime Optimal-Transport Attention for Visuo-Haptic Imitation Learning of Contact-Rich Manipulation

arXiv:2605.20433v1 Announce Type: new Abstract: Contact-rich manipulation tasks such as tight-clearance insertion, connector mating, polishing, and surface-conforming wiping remain difficult for data-

SurgOnAir: Hierarchy-Aware Real-Time Surgical Video Commentary

arXiv:2605.21132v1 Announce Type: new Abstract: Understanding surgical workflow in real time is fundamental for intelligent surgical embodiment, where AI systems continuously perceive and respond as s

safetyarxiv-cs-cv

Trusted Weights, Treacherous Optimizations? Optimization-Triggered Backdoor Attacks on LLMs

arXiv:2605.20641v1 Announce Type: cross Abstract: Inference optimization is a vital technique for deploying LLMs at scale. Compilation is the most widely adopted optimization technique for LLMs. While

When AI Gets it Wrong: Reliability and Risk in AI-Assisted Medication Decision Systems

arXiv:2604.01449v3 Announce Type: replace-cross Abstract: Artificial intelligence (AI) systems are increasingly integrated into healthcare and pharmacy workflows, supporting tasks such as medication r

ARC-RL: A Reinforcement Learning Playground Inspired by ARC Raiders

arXiv:2605.19503v1 Announce Type: cross Abstract: Reinforcement learning for legged locomotion has matured into a stack of multi-component reward functions and physics-engine benchmarks whose morpholo

Attention-Guided Reward for Reinforcement Learning-based Jailbreak against Large Reasoning Models

arXiv:2605.19485v1 Announce Type: new Abstract: Large Reasoning Models (LRMs) have demonstrated remarkable capabilities in solving complex problems by generating structured, step-by-step reasoning con

Backtracking When It Strays: Mitigating Dual Exposure Biases in LLM Reasoning Distillation

arXiv:2605.19433v1 Announce Type: cross Abstract: Large language models (LLMs) have achieved remarkable success in complex reasoning tasks via long chain-of-thought (CoT), yet their immense computatio

Be Kind, Rewrite: Benign Projections via Rewriting Defend Against LLM Data Poisoning Attacks

arXiv:2605.19147v1 Announce Type: cross Abstract: Large language models (LLMs) are highly susceptible to backdoor attacks (BAs), wherein training samples are poisoned using trigger-based harmful conte

Model Releases

Beyond Imitation: Learning Safe End-to-End Autonomous Driving from Hard Negatives

model-releasesarxiv-cs-cv

arXiv:2605.19771v1 Announce Type: cross Abstract: Existing imitation learning methods for end-to-end autonomous driving predominantly learn from successful demonstrations by minimizing geometric devia

⚠️👇 🚨Breaking ⚠️ If we can’t make AI agents follow rules, we are screwed. New study from METR reports that “when the agents were faced wit…

⚠️👇 🚨Breaking ⚠️ If we can’t make AI agents follow rules, we are screwed. New study from METR reports that “when the agents were faced with hard tasks, they routinely violated constraints” This—routin

safetygary-marcus--x

CADENet: Condition-Adaptive Asynchronous Dual-Stream Enhancement Network for Adverse Weather Perception in Autonomous Driving

arXiv:2605.19837v1 Announce Type: cross Abstract: Adverse weather (rain, fog, sand, and snow) degrades camera-based object detection in autonomous vehicles. Existing enhancement-then-detect approaches

CEPO: RLVR Self-Distillation using Contrastive Evidence Policy Optimization

arXiv:2605.19436v1 Announce Type: cross Abstract: When a model produces a correct solution under reinforcement learning with verifiable rewards (RLVR), every token receives the same reward signal rega

safetyarxiv-cs-cl

Compliant Explicit Reference Governor for Contact Friendly Robotic Manipulators

arXiv:2504.09188v2 Announce Type: replace Abstract: This paper introduces the Compliant Explicit Reference Governor (CERG), a modular reference management system that enables robots to interact physic

Model Releases

Contrastive Reasoning Alignment: Reinforcement Learning from Hidden Representations

model-releasesarxiv-cs-ai

arXiv:2603.17305v2 Announce Type: replace Abstract: We propose CRAFT, a red-teaming alignment framework that leverages model reasoning capabilities and hidden representations to improve robustness aga

ESLD (External Surrogate Latent Defense): A Latent-Space Architecture for Faster, Stronger Prompt-Injection Defense

arXiv:2605.18918v1 Announce Type: cross Abstract: Modern AI assistants are agentic. To answer a single user request, the underlying language model pulls in information from many sources, such as web s

Model Releases

Explainable Wastewater Digital Twins: Adaptive Context-Conditioned Structured Simulators with Self-Falsifying Decision Support

model-releasesarxiv-cs-ai

arXiv:2605.19826v1 Announce Type: new Abstract: Operators of safety-critical industrial processes increasingly rely on digital twins to screen control interventions, but such simulators rarely carry c

Graph Neural Planning and Predictive Control for Multi-Robot Communication-Constrained Unlabeled Motion Planning

arXiv:2605.19209v1 Announce Type: new Abstract: The multi-robot unlabeled motion planning problem of concurrently assigning robots to goals and generating safe trajectories is central in many collabor

Hamilton--Jacobi Reachability for Spacecraft Collision Avoidance

arXiv:2605.20138v1 Announce Type: new Abstract: This article presents a Hamilton--Jacobi (HJ) reachability framework for a two--satellite collision avoidance problem operating in the same circular orb

Local Ai

KIO-planner: Attention-Guided Single-Stage Motion Planning with Dual Mapping for UAV Navigation

arXiv:2605.19703v1 Announce Type: new Abstract: Autonomous UAV flight in confined, wall-dense environments requires low-latency and reliable motion planning under strict safety constraints. Traditiona

local-aiarxiv-cs-ro

Knowing When Not to Predict: Self Supervised Learning and Abstention for Safer DR Screening

arXiv:2605.19133v1 Announce Type: cross Abstract: Self-supervised learning (SSL) is now a standard way to pretrain medical image models, but performance is still mostly judged by downstream accuracy.

Position: Uncertainty Quantification in LLMs is Just Unsupervised Clustering

arXiv:2605.19220v1 Announce Type: cross Abstract: Uncertainty Quantification (UQ) is widely regarded as the primary safeguard for deploying Large Language Models (LLMs) in high-stakes domains. However

Probabilistic Recursively Feasible Motion Planning Under Uncertain Environments

arXiv:2605.19015v1 Announce Type: cross Abstract: Safe motion planning in uncertain, time-varying environments is challenging because the safe region can change unpredictably across planning steps, of

Pseudocode-Guided Structured Reasoning for Automating Reliable Inference in Vision-Language Models

arXiv:2605.19663v1 Announce Type: new Abstract: Vision-Language Models (VLMs) are becoming the cornerstone of high-level reasoning for robotic automation, enabling robots to parse natural language com

Robotics-Inspired Guardrails for Foundation Models in Socially Sensitive Domains

arXiv:2605.19940v1 Announce Type: new Abstract: Foundation models are increasingly deployed in socially sensitive domains such as education, mental health, and caregiving, where failures are often cum

Augmenting Human Evaluation with LLM Judges: How Many Human Reviews Do You Need?

arXiv:2605.16354v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used as automated evaluators of AI systems, including in high-stakes applications. In this role, LLMs ar

Code as Agent Harness

arXiv:2605.18747v1 Announce Type: cross Abstract: Recent large language models (LLMs) have demonstrated strong capabilities in understanding and generating code, from competitive programming to reposi

Ethical Hyper-Velocity (EHV): A Provably Deterministic Governance-Aware JIT Compiler Architecture for Agentic Systems

arXiv:2605.17909v1 Announce Type: new Abstract: As autonomous agentic systems scale across regulated critical infrastructures, the lack of mechanistic, hardware-rooted enforcement for high-frequency p

Event-Grounded Sparse Autoencoders for Vision-Language-Action Policies

arXiv:2605.17204v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) policies translate language and visual inputs into robot actions, where their hidden representations directly shape close

Experiment-as-Code Labs: A Declarative Stack for AI-Driven Scientific Discovery

arXiv:2605.04375v2 Announce Type: replace-cross Abstract: To unleash the full potential of AI for Science, we must untether the agents from a purely digital environment. The agent's ability to control

For a long time, academic researchers being at the cutting edge of new technologies has been a great social equilibrium. Neutral, unbiased t…

For a long time, academic researchers being at the cutting edge of new technologies has been a great social equilibrium. Neutral, unbiased technologists have been the people to spread new ideas to the

safetyclem-delangue--x

Guided Reinforcement Learning for Omnidirectional 3D Jumping in Quadruped Robots

arXiv:2507.16481v3 Announce Type: replace Abstract: Jumping poses a significant challenge for quadruped robots, despite being crucial for many operational scenarios. While optimisation methods exist f

Helping Customers in Distress: An LLM-powered Agent that Converses, Probes, and Routes

arXiv:2605.16268v1 Announce Type: cross Abstract: Banks receive millions of reports of fraud, scams, and disputed transactions every year, making it challenging to accurately direct customers to the a

Learning-Based Adaptive Control for Surgical Robotic Exposure Task on Deformable Tissues

arXiv:2605.17927v1 Announce Type: new Abstract: In various surgical procedures, regions of interest (ROIs) such as organs or lesions are often occluded by overlying tissues, requiring surgeons to achi

On Safer Reinforcement Learning for Sedation and Analgesia in Intensive Care

arXiv:2601.23154v2 Announce Type: replace-cross Abstract: Pain management in intensive care usually involves complex trade-offs, since both inadequate and excessive treatment can compromise patient sa

Online Learnability of Chain-of-Thought Verifiers: Soundness and Completeness Trade-offs

arXiv:2603.03538v3 Announce Type: replace Abstract: Large Language Models (LLMs) with chain-of-thought generation have demonstrated great potential for solving complex reasoning and planning tasks. Ho

PQR: A Framework to Generate Diverse and Realistic User Queries that Elicit QA Agent Failures

arXiv:2605.16551v1 Announce Type: new Abstract: Evaluating LLM-based agents remains challenging because identifying meaningful failure cases often requires substantial human effort to design realistic

safetyarxiv-cs-cl

Real2Sim via Active Perception with Behavior Trees Automatically Generated by VLMs

arXiv:2601.08454v2 Announce Type: replace Abstract: Constructing physically accurate simulation environments (Real2Sim) traditionally relies on manual system identification or rigid, exhaustive explor

SuReNav: Superpixel Graph-based Constraint Relaxation for Navigation in Over-constrained Environments

arXiv:2602.06807v2 Announce Type: replace-cross Abstract: We address the over-constrained planning problem in semi-static environments. The planning objective is to find a best-effort solution that av

TClone: Low-Latency Forking of Live GUI Environments for Computer-Use Agents

arXiv:2605.17320v1 Announce Type: cross Abstract: Computer-use agents increasingly operate inside live personal workspaces, where their actions can modify files, applications, GUI state, credentials,

Temporal Task Diversity: Inductive Biases Under Non-Stationarity in Synthetic Sequence Modelling

arXiv:2605.18281v1 Announce Type: new Abstract: Modern deep learning science often assumes that neural networks learn from a fixed data distribution. However, many practically important learning probl

The Capability Paradox: How Smarter Auditors Make Multi-Agent Systems Less Secure

arXiv:2605.17480v1 Announce Type: new Abstract: Multi-agent systems extend large language models (LLMs) by decomposing tasks among specialized agents, but their distributed decision process creates ne

Transformer-Based MCS Prediction for 5G Multicast-Broadcast Services (MBS)

arXiv:2605.16735v1 Announce Type: cross Abstract: The deployment of 5G Multicast-Broadcast Services (MBS) is emerging as a critical technology for spectral-efficient UHD content delivery and serving a

Unleashing the Potential of Diffusion Models for End-to-End Autonomous Driving

arXiv:2602.22801v2 Announce Type: replace-cross Abstract: Diffusion models have become a popular choice for decision-making tasks in robotics, and more recently, are also being considered for solving

What we find most useful about CNA is that the intervention is simple yet powerful. The steering is a multiplicative ablation on a sparse se…

What we find most useful about CNA is that the intervention is simple yet powerful. The steering is a multiplicative ablation on a sparse set of MLP neurons, which makes CNA a clean addition on top of

safetynous-research--x

AI Consciousness and Existential Risk

arXiv:2511.19115v2 Announce Type: replace Abstract: In AI, the existential risk denotes the hypothetical threat posed by an artificial system that would possess both the capability and the objective,