Search: “safety”

The Autocorrelation Blind Spot: Why 42% of Turn-Level Findings in LLM Conversation Analysis May Be Spurious

arXiv:2604.14414v1 Announce Type: new Abstract: Turn-level metrics are widely used to evaluate properties of multi-turn human-LLM conversations, from safety and sycophancy to dialogue quality. However

17 Apr 2026

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C^2T: Captioning-Structure and LLM-Aligned Common-Sense Reward Learning for Traffic--Vehicle Coordination

DGX agent

arXiv:2604.13098v1 Announce Type: cross Abstract: State-of-the-art (SOTA) urban traffic control increasingly employs Multi-Agent Reinforcement Learning (MARL) to coordinate Traffic Light Controllers (

safetyarxiv-cs-cv

16 Apr 2026

From Where Words Come: Efficient Regularization of Code Tokenizers Through Source Attribution

arXiv:2604.14053v1 Announce Type: new Abstract: Efficiency and safety of Large Language Models (LLMs), among other factors, rely on the quality of tokenization. A good tokenizer not only improves infe

Learning Probabilistic Responsibility Allocations for Multi-Agent Interactions

arXiv:2604.13128v1 Announce Type: cross Abstract: Human behavior in interactive settings is shaped not only by individual objectives but also by shared constraints with others, such as safety. Underst

safetyarxiv-cs-lg

Robust Energy-Aware Routing for Air-Ground Cooperative Multi-UAV Delivery in Wind-Uncertain Environments

arXiv:2604.13441v1 Announce Type: new Abstract: Ensuring energy feasibility under wind uncertainty is critical for the safety and reliability of UAV delivery missions. In realistic truck-drone logisti

See&Say: Vision Language Guided Safe Zone Detection for Autonomous Package Delivery Drones

arXiv:2604.13292v1 Announce Type: new Abstract: Autonomous drone delivery systems are rapidly advancing, but ensuring safe and reliable package drop-offs remains highly challenging in cluttered urban

Model Releases

Synthesis and Deployment of Maximal Robust Control Barrier Functions through Adversarial Reinforcement Learning

model-releasesarxiv-cs-ro

arXiv:2604.13192v1 Announce Type: cross Abstract: Robust control barrier functions (CBFs) provide a principled mechanism for smooth safety enforcement under worst-case disturbances. However, existing

ZK-APEX: Zero-Knowledge Approximate Personalized Unlearning with Executable Proofs

arXiv:2512.09953v2 Announce Type: replace-cross Abstract: Machine unlearning aims to remove the influence of specific data points from a trained model to satisfy privacy, copyright, and safety require

safetyarxiv-cs-lg

Fragile Reconstruction: Adversarial Vulnerability of Reconstruction-Based Detectors for Diffusion-Generated Images

arXiv:2604.12781v1 Announce Type: new Abstract: Recently, detecting AI-generated images produced by diffusion-based models has attracted increasing attention due to their potential threat to safety. A

LogicEval: A Systematic Framework for Evaluating Automated Repair Techniques for Logical Vulnerabilities in Real-World Software

arXiv:2604.12994v1 Announce Type: cross Abstract: Logical vulnerabilities in software stem from flaws in program logic rather than memory safety, which can lead to critical security failures. Although

Parallax: Why AI Agents That Think Must Never Act

arXiv:2604.12986v1 Announce Type: cross Abstract: Autonomous AI agents are rapidly transitioning from experimental tools to operational infrastructure, with projections that 80% of enterprise applicat

Skill-informed Data-driven Haptic Nudges for High-dimensional Human Motor Learning

arXiv:2603.12583v2 Announce Type: replace Abstract: In this work, we propose a data-driven framework to design optimal haptic nudge feedback leveraging the learner's estimated skill to address the cha

A Mamba-Based Multimodal Network for Multiscale Blast-Induced Rapid Structural Damage Assessment

arXiv:2604.11709v1 Announce Type: new Abstract: Accurate and rapid structural damage assessment (SDA) is crucial for post-disaster management, helping responders prioritise resources, plan rescues, an

AI Organizations are More Effective but Less Aligned than Individual Agents

arXiv:2604.10290v1 Announce Type: new Abstract: AI is increasingly deployed in multi-agent systems; however, most research considers only the behavior of individual models. We experimentally show that

Cross-Cultural Value Awareness in Large Vision-Language Models

arXiv:2604.09945v1 Announce Type: cross Abstract: The rapid adoption of large vision-language models (LVLMs) in recent years has been accompanied by growing fairness concerns due to their propensity t

ExpressMM: Expressive Mobile Manipulation Behaviors in Human-Robot Interactions

arXiv:2604.05320v2 Announce Type: replace Abstract: Mobile manipulators are increasingly deployed in human-centered environments to perform tasks. While completing such tasks, they should also be able

Latent Structure of Affective Representations in Large Language Models

arXiv:2604.07382v2 Announce Type: replace-cross Abstract: The geometric structure of latent representations in large language models (LLMs) is an active area of research, driven in part by its implica

Legal2LogicICL: Improving Generalization in Transforming Legal Cases to Logical Formulas via Diverse Few-Shot Learning

arXiv:2604.11699v1 Announce Type: cross Abstract: This work aims to improve the generalization of logic-based legal reasoning systems by integrating recent advances in NLP with legal-domain adaptive f

Rebooting Microreboot: Architectural Support for Safe, Parallel Recovery in Microservice Systems

arXiv:2604.09963v1 Announce Type: cross Abstract: Microreboot enables fast recovery by restarting only the failing component, but in modern microservices naive restarts are unsafe: dense dependencies

Self-Organizing Dual-Buffer Adaptive Clustering Experience Replay (SODACER) for Safe Reinforcement Learning in Optimal Control

arXiv:2601.06540v2 Announce Type: replace-cross Abstract: This paper proposes a novel reinforcement learning framework, named Self-Organizing Dual-buffer Adaptive Clustering Experience Replay (SODACER

From Dispersion to Attraction: Spectral Dynamics of Hallucination Across Whisper Model Scales

arXiv:2604.08591v1 Announce Type: cross Abstract: Hallucinations in large ASR models present a critical safety risk. In this work, we propose the extit{Spectral Sensitivity Theorem}, which predicts a

13 Apr 2026

Multimodal Anomaly Detection for Human-Robot Interaction

arXiv:2604.09326v1 Announce Type: cross Abstract: Ensuring safety and reliability in human-robot interaction (HRI) requires the timely detection of unexpected events that could lead to system failures

13 Apr 2026

Towards Responsible Multimodal Medical Reasoning via Context-Aligned Vision-Language Models

arXiv:2604.08815v1 Announce Type: new Abstract: Medical vision-language models (VLMs) show strong performance on radiology tasks but often produce fluent yet weakly grounded conclusions due to over-re

13 Apr 2026

Before Humans Join the Team: Diagnosing Coordination Failures in Healthcare Robot Team Simulation

arXiv:2508.04691v2 Announce Type: replace-cross Abstract: As humans move toward collaborating with coordinated robot teams, understanding how these teams coordinate and fail is essential for building

Distributional Open-Ended Evaluation of LLM Cultural Value Alignment Based on Value Codebook

arXiv:2604.06210v2 Announce Type: cross Abstract: As LLMs are globally deployed, aligning their cultural value orientations is critical for safety and user engagement. However, existing benchmarks fac

EMMa: End-Effector Stability-Oriented Mobile Manipulation for Tracked Rescue Robots

arXiv:2604.08292v1 Announce Type: new Abstract: The autonomous operation of tracked mobile manipulators in rescue missions requires not only ensuring the reachability and safety of robot motion but al

Evaluation as Evolution: Transforming Adversarial Diffusion into Closed-Loop Curricula for Autonomous Vehicles

arXiv:2604.07378v1 Announce Type: new Abstract: Autonomous vehicles in interactive traffic environments are often limited by the scarcity of safety-critical tail events in static datasets, which biase

Event-Centric World Modeling with Memory-Augmented Retrieval for Embodied Decision-Making

arXiv:2604.07392v1 Announce Type: cross Abstract: Autonomous agents operating in dynamic and safety-critical environments require decision-making frameworks that are both computationally efficient and

Event-Level Detection of Surgical Instrument Handovers in Videos with Interpretable Vision Models

arXiv:2604.07577v1 Announce Type: new Abstract: Reliable monitoring of surgical instrument exchanges is essential for maintaining procedural efficiency and patient safety in the operating room. Automa

Approve What, Exactly? Effect Binding and Trusted Rendering for Agent Step-Up Authorization

arXiv:2610.21063v1 Announce Type: cross Abstract: AI agents now pause for approval before they send money, delete files or email outsiders, and that approval is usually treated as a second factor. We

CarryOn: A Dual-Loop Harness for Personalized and Reliable Robot Assistance

arXiv:2610.21453v1 Announce Type: cross Abstract: While generalist vision-language-action (VLA) models have enabled diverse open-world manipulation, real-world robotic assistance requires two critical

Decentralized LLM Workflows via Sequential Auctions

arXiv:2610.22201v1 Announce Type: cross Abstract: We present a scalable framework for solving multi-step tasks through a decentralized market of self-interested agents. Prior theoretical work demonstr

Following Delegation Traces to Study Human Oversight in Interaction with Multimodal AI

arXiv:2610.21948v1 Announce Type: cross Abstract: As multimodal AI systems are becoming our everyday thought partners, the shift towards studying how users transfer control to these systems becomes es

Towards Continual Learning: Mitigating Forgetting With Subspace Tuning

arXiv:2610.22293v1 Announce Type: cross Abstract: Large language models (LLMs) acquire new skills through fine-tuning, but it can destroy capabilities they already have: catastrophic forgetting remain

Don't Run with Scissors: Pruning Breaks VLA Models but They Can Be Recovered

arXiv:2510.08464v2 Announce Type: replace-cross Abstract: Vision-Language-Action (VLA) models are a promising paradigm for field robotics, but their deployment is bottlenecked by strict edge SWaP-C co

safetyarxiv-cs-lg

End-to-End Vision-Language-Action Policies for Autonomous Lunar Rover Docking

arXiv:2610.19224v1 Announce Type: new Abstract: Surface navigation on the Moon is subject to extreme illumination contrast and deployment-time perceptual disturbances. While modular navigation pipelin

Frustratingly Easy Majority Voting Using Length Conditioning

arXiv:2610.19890v1 Announce Type: new Abstract: Majority Voting (MV) is a simple and widely used strategy which improves the reasoning accuracy of Large Language Models (LLMs) by allocating additional

Heterogeneous Agentic Coevolution

arXiv:2610.16078v1 Announce Type: new Abstract: Recent advances in self-improving LLM agents have demonstrated the potential of AI systems that drive their own intelligence. However, many domain-speci

Hidden in Plain Sight: Advancing Harmful Content Understanding via Perception-Aware Adversarial Self-Play

arXiv:2610.15449v2 Announce Type: new Abstract: Despite significant advances in language understanding, Large Language Models (LLMs) can still miss meaning hidden in plain sight, failing to identify h

Holding Agents Accountable with Verifiable Agent Insurance

arXiv:2610.18230v1 Announce Type: cross Abstract: While AI agents are useful for many tasks, they regularly misbehave in destructive ways, such as deleting important data. While prior work has focused

LLM-Based Policy Compliance Verification of Loan Applications

arXiv:2610.20155v1 Announce Type: cross Abstract: Banks rely on extensive internal policy frameworks to ensure that corporate lending decisions comply with regulatory requirements and internal risk st

On Symmetry for Characterizing Agent Heterogeneity in Multi-Agent Systems

arXiv:2610.18538v1 Announce Type: cross Abstract: Understanding agent heterogeneity can inform the design of shared and agent-specific policy representations, however existing criteria provide no unif

RM-OPD: On-Policy Distillation for Generative Reward Models

arXiv:2610.20174v1 Announce Type: cross Abstract: Reinforcement Learning on Mixed-domain preference data (Mix-RL) has become the mainstream approach for training general-purpose Generative Reward Mode

Single-Sample Decoding of a Two-Layer Sound-Word Canvas Model for Thai Klon Pad

arXiv:2610.14980v1 Announce Type: new Abstract: We present a generator of Thai klon pad verse, one stanza from a short topic, and evaluate it against human verse on held-out topics. The generator is a

Superintelligence and the Triple Risk of an AI Race

arXiv:2610.17085v1 Announce Type: cross Abstract: On many estimates, artificial general intelligence (AGI) is likely to emerge within the next few decades or even sooner. Once it does, it may quickly

Verification and Forward Invariance of Control Barrier Functions for Differential-Algebraic Systems

arXiv:2603.13509v2 Announce Type: replace-cross Abstract: Differential-algebraic equations (DAEs) arise in power networks, chemical processes, and multibody systems, where algebraic constraints encode

A Hardware-Partitioned micro-ROS Mini-Vehicle for Operator-Supervised Neural Driving

arXiv:2610.15966v1 Announce Type: new Abstract: We present a low-cost miniature Ackermann vehicle for embedded neural-driving research, organized as a hardware-partitioned perceive-decide-act agent: a

14 Oct 2026

AutoAgent0: An Agentic Runtime for Safe Closed-Loop Driving

arXiv:2610.18866v1 Announce Type: new Abstract: Safe autonomous driving requires more than accurate planning at a single frame: it demands reliable closed-loop execution in dynamic environments. Altho