Search: “safety”

Hyp2Former: Hierarchy-Aware Hyperbolic Embeddings for Open-Set Panoptic Segmentation

arXiv:2605.02580v1 Announce Type: new Abstract: Recognizing unknown objects is crucial for safety-critical applications such as autonomous driving and robotics. Open-Set Panoptic Segmentation (OPS) ai

5 May 2026

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Safety

Less Precise Can Be More Reliable: A Systematic Evaluation of Quantization's Impact on VLMs Beyond Accuracy

DGX agent

arXiv:2509.21173v5 Announce Type: replace Abstract: Vision-Language Models (VLMs) such as CLIP have revolutionized zero-shot classification and safety-critical tasks, including Out-of-Distribution (OO

safetyarxiv-cs-cv

5 May 2026

Parking Assistance for Trailer-Truck Transport Vehicles Using Sensor Fusion and Motion Planning

arXiv:2605.02716v1 Announce Type: new Abstract: Autonomous driving technology has rapidly evolved over the past decade, offering significant improvements in transportation efficiency, safety, and cost

5 May 2026

UCATSC: Uncertainty-Aware Constrained Traffic Signal Control Under Vision-Based Partial Observability

arXiv:2602.07784v3 Announce Type: replace Abstract: Camera-based adaptive traffic signal control is inherently partially observable: detections can be missed, vehicle speeds and distances can be noisy

5 May 2026

Dynamic-TD3: A Novel Algorithm for UAV Path Planning with Dynamic Obstacle Trajectory Prediction

arXiv:2605.00059v1 Announce Type: new Abstract: Deep reinforcement learning (DRL) finds extensive application in autonomous drone navigation within complex, high-risk environments. However, its practi

Impact of Task Phrasing on Presumptions in Large Language Models

arXiv:2605.00436v1 Announce Type: new Abstract: Concerns with the safety and reliability of applying large-language models (LLMs) in unpredictable real-world applications motivate this study, which ex

Stable-GFlowNet: Toward Diverse and Robust LLM Red-Teaming via Contrastive Trajectory Balance

arXiv:2605.00553v1 Announce Type: new Abstract: Large Language Model (LLM) Red-Teaming, which proactively identifies vulnerabilities of LLMs, is an essential process for ensuring safety. Finding effec

Unlearning What Matters: Token-Level Attribution for Precise Language Model Unlearning

arXiv:2605.00364v1 Announce Type: new Abstract: Machine unlearning has emerged as a critical capability for addressing privacy, safety, and regulatory concerns in large language models (LLMs). Existin

Dynamic Adversarial Fine-Tuning Reorganizes Refusal Geometry

arXiv:2604.27019v1 Announce Type: cross Abstract: Safety-aligned language models must refuse harmful requests without collapsing into broad over-refusal, but the training-time mechanisms behind this t

Efficient Preimage Approximation for Neural Network Certification

arXiv:2505.22798v3 Announce Type: replace-cross Abstract: The growing reliance on artificial intelligence in safety- and security-critical applications is raising concerns about the robustness of neur

Model Releases

OR-VSKC: Resolving Visual-Semantic Knowledge Conflicts in Operating Rooms with Synthetic Data-Guided Alignment

model-releasesarxiv-cs-ai

arXiv:2506.22500v2 Announce Type: replace-cross Abstract: Automated identification of surgical safety risks is critical for improving patient outcomes; however, Multimodal Large Language Models (MLLMs

The Field of Safe Motion: Operationalizing Affordances in the Field of Safe Travel Using Reachability Analysis

arXiv:2604.27168v1 Announce Type: new Abstract: We present the Field of Safe Motion (FSM), a quantitative safety model for determining whether a driver maintains a collision-free escape route, or 'out

A Scaled Three-Vehicle Platooning Platform

arXiv:2604.25963v1 Announce Type: new Abstract: Vehicle platooning has attracted increasing attention as a promising approach to improve traffic efficiency, energy consumption, and roadway safety thro

Adversarial Robustness of NTK Neural Networks

arXiv:2604.25965v1 Announce Type: cross Abstract: Deep learning models are widely deployed in safety-critical domains, but remain vulnerable to adversarial attacks. In this paper, we study the adversa

Crime Hotspot Prediction Using Deep Graph Convolutional Networks

arXiv:2506.13116v2 Announce Type: replace-cross Abstract: Crime hotspot prediction is critical for ensuring urban safety and effective law enforcement, it remains challenging due to complex spatial de

Improving Bayesian Optimization for Portfolio Management with an Adaptive Scheduling

arXiv:2504.13529v4 Announce Type: replace Abstract: Existing black-box portfolio management systems are prevalent in the financial industry due to commercial and safety constraints, though their perfo

Uncertainty-Aware Information Pursuit for Interpretable and Reliable Medical Image Analysis

arXiv:2506.16742v3 Announce Type: replace Abstract: To be adopted in safety-critical domains like medical image analysis, AI systems must provide human-interpretable decisions. Variational Information

BARRED: Synthetic Training of Custom Policy Guardrails via Asymmetric Debate

arXiv:2604.25203v1 Announce Type: new Abstract: Deploying guardrails for custom policies remains challenging, as generic safety models fail to capture task-specific requirements, while prompting LLMs

29 Apr 2026

Learning-Based Automated Adversarial Red-Teaming for Robustness Evaluation of Large Language Models

arXiv:2512.20677v4 Announce Type: replace-cross Abstract: The increasing deployment of large language models (LLMs) in safety-critical applications raises fundamental challenges in systematically eval

29 Apr 2026

RISE: Self-Improving Robot Policy with Compositional World Model

arXiv:2602.11075v2 Announce Type: replace Abstract: Despite the sustained scaling on model capacity and data acquisition, Vision-Language-Action (VLA) models remain brittle in contact-rich and dynamic

29 Apr 2026

An Automatic Ground Collision Avoidance System with Reinforcement Learning

arXiv:2604.24403v1 Announce Type: new Abstract: This article evaluates an artificial intelligence (AI)-based Automatic Ground Collision Avoidance System (AGCAS) designed for advanced jet trainers to e

An Information-Geometric Framework for Stability Analysis of Large Language Models under Entropic Stress

arXiv:2604.24076v1 Announce Type: new Abstract: As large language models (LLMs) are increasingly deployed in high-stakes and operational settings, evaluation strategies based solely on aggregate accur

Beyond Binary Out-of-Distribution Detection: Characterizing Distributional Shifts with Multi-Statistic Diffusion Trajectories

arXiv:2510.17381v2 Announce Type: replace Abstract: Detecting out-of-distribution (OOD) data is critical for machine learning, be it for safety reasons or to enable open-ended learning. However, beyon

Cooperative Informative Sensing for Monitoring Dynamic Indoor Environments via Multi-Agent Reinforcement Learning

arXiv:2604.23179v1 Announce Type: cross Abstract: Monitoring human activity in indoor environments is important for applications such as facility management, safety assessment, and space utilization a

Extending Precipitation Nowcasting Horizons via Spectral Fusion of Radar Observations and Foundation Model Priors

arXiv:2603.21768v3 Announce Type: replace-cross Abstract: Precipitation nowcasting is critical for disaster mitigation and aviation safety. However, radar-only models frequently suffer from a lack of

IndustryAssetEQA: A Neurosymbolic Operational Intelligence System for Embodied Question Answering in Industrial Asset Maintenance

arXiv:2604.23446v1 Announce Type: new Abstract: Industrial maintenance environments increasingly rely on AI systems to assist operators in understanding asset behavior, diagnosing failures, and evalua

iWatchRoad: Scalable Detection and Geospatial Visualization of Potholes for Smart Cities

arXiv:2508.10945v2 Announce Type: replace Abstract: Potholes on the roads are a serious hazard and maintenance burden. This poses a significant threat to road safety and vehicle longevity, especially

Large Language Model based Interactive Decision-Making for Autonomous Driving

arXiv:2604.23513v1 Announce Type: new Abstract: In high-conflict mixed-traffic scenarios involving human-driven and autonomous vehicles, most existing autonomous driving systems default to overly cons

Recognition Without Authorization: LLMs and the Moral Order of Online Advice

arXiv:2604.22143v1 Announce Type: cross Abstract: Large language models are increasingly used to mediate everyday interpersonal dilemmas, yet how their advisory defaults interact with the concentrated

RedVLA: Physical Red Teaming for Vision-Language-Action Models

arXiv:2604.22591v1 Announce Type: new Abstract: The real-world deployment of Vision-Language-Action (VLA) models remains limited by the risk of unpredictable and irreversible physical harm. However, w

Transferable Physical-World Adversarial Patches Against Pedestrian Detection Models

arXiv:2604.22552v1 Announce Type: new Abstract: Physical adversarial patch attacks critically threaten pedestrian detection, causing surveillance and autonomous driving systems to miss pedestrians and

V-STC: A Time-Efficient Multi-Vehicle Coordinated Trajectory Planning Approach

arXiv:2604.22196v1 Announce Type: new Abstract: Coordinating the motions of multiple autonomous vehicles (AVs) requires planning frameworks that ensure safety while making efficient use of space and t

Align Generative Artificial Intelligence with Human Preferences: A Novel Large Language Model Fine-Tuning Method for Online Review Management

arXiv:2604.21209v1 Announce Type: new Abstract: Online reviews have played a pivotal role in consumers' decision-making processes. Existing research has highlighted the significant impact of manageria

Clinical Reasoning AI for Oncology Treatment Planning: A Multi-Specialty Case-Based Evaluation

arXiv:2604.20869v1 Announce Type: cross Abstract: Background: More than 80% of U.S. cancer care is delivered in community settings, where survival remains worse than at academic centers. Clinicians mu

FryNet: Dual-Stream Adversarial Fusion for Non-Destructive Frying Oil Oxidation Assessment

arXiv:2604.21321v1 Announce Type: new Abstract: Monitoring frying oil degradation is critical for food safety, yet current practice relies on destructive wet-chemistry assays that provide no spatial i

Seeing Without Eyes: 4D Human-Scene Understanding from Wearable IMUs

arXiv:2604.21926v1 Announce Type: new Abstract: Understanding human activities and their surrounding environments typically relies on visual perception, yet cameras pose persistent challenges in priva

Value-Conflict Diagnostics Reveal Widespread Alignment Faking in Language Models

arXiv:2604.20995v1 Announce Type: new Abstract: Alignment faking, where a model behaves aligned with developer policy when monitored but reverts to its own preferences when unobserved, is a concerning

DAIRE: A lightweight AI model for real-time detection of Controller Area Network attacks in the Internet of Vehicles

arXiv:2604.20771v1 Announce Type: cross Abstract: The Internet of Vehicles (IoV) is advancing modern transportation by improving safety, efficiency, and intelligence. However, the reliance on the Cont

Diagnosing CFG Interpretation in LLMs

arXiv:2604.20811v1 Announce Type: new Abstract: As LLMs are increasingly integrated into agentic systems, they must adhere to dynamically defined, machine-interpretable interfaces. We evaluate LLMs as

Evaluating Assurance Cases as Text-Attributed Graphs for Structure and Provenance Analysis

arXiv:2604.20577v1 Announce Type: cross Abstract: An assurance case is a structured argument document that justifies claims about a system's requirements or properties, which are supported by evidence

Generative Augmentation of Imbalanced Flight Records for Flight Diversion Prediction: A Multi-objective Optimisation Framework

arXiv:2604.20288v1 Announce Type: new Abstract: Flight diversions are rare but high-impact events in aviation, making their reliable prediction vital for both safety and operational efficiency. Howeve

Graph2Counsel: Clinically Grounded Synthetic Counseling Dialogue Generation from Client Psychological Graphs

arXiv:2604.20382v1 Announce Type: new Abstract: Rising demand for mental health support has increased interest in using Large Language Models (LLMs) for counseling. However, adapting LLMs to this high

Toward Cooperative Driving in Mixed Traffic: An Adaptive Potential Game-Based Approach with Field Test Verification

arXiv:2604.20231v1 Announce Type: new Abstract: Connected autonomous vehicles (CAVs), which represent a significant advancement in autonomous driving technology, have the potential to greatly increase

AutoAWG: Adverse Weather Generation with Adaptive Multi-Controls for Automotive Videos

arXiv:2604.18993v1 Announce Type: cross Abstract: Perception robustness under adverse weather remains a critical challenge for autonomous driving, with the core bottleneck being the scarcity of real-w

Benchmarking Misuse Mitigation Against Covert Adversaries

arXiv:2506.06414v2 Announce Type: replace-cross Abstract: Existing language model safety evaluations focus on overt attacks and low-stakes tasks. In reality, an attacker can easily subvert existing sa

Ensembling Pruned Attention Heads For Uncertainty-Aware Efficient Transformers

arXiv:2510.18358v2 Announce Type: replace-cross Abstract: Uncertainty quantification (UQ) is essential for deploying deep neural networks in safety-critical settings. Although methods like Deep Ensemb

From Particles to Perils: SVGD-Based Hazardous Scenario Generation for Autonomous Driving Systems Testing

arXiv:2604.18918v1 Announce Type: cross Abstract: Simulation-based testing of autonomous driving systems (ADS) must uncover realistic and diverse failures in dense, heterogeneous traffic. However, exi

Mind2Drive: Predicting Driver Intentions from EEG in Real-world On-Road Driving

arXiv:2604.19368v1 Announce Type: new Abstract: Predicting driver intention from neurophysiological signals offers a promising pathway for enhancing proactive safety in advanced driver assistance syst