Search: “safety”

Low-Magnification SEM May Suffice: Interpretable Deep Learning for Multi-Scale Fracture-Cause Classification in Zirconia-Toughened Alumina

arXiv:2605.29798v1 Announce Type: new Abstract: Reliable identification of fracture origins in alumina matrix composite hip and knee implants is critical for quality assurance and patient safety, yet

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Safety

Masked Diffusion Modeling for Anomaly Detection

DGX agent

arXiv:2605.30046v1 Announce Type: cross Abstract: Anomaly detection aims to identify samples that deviate from the nominal data distribution and is central to many safety-critical applications. Howeve

safetyarxiv-cs-ai

29 May 2026

Multi-Resolution End-to-End Deep Neural Network for Optimizing Latency-Accuracy Tradeoff in Autonomous Driving

arXiv:2605.29138v1 Announce Type: cross Abstract: Latency-accuracy tradeoffs are fundamental in real-time applications of deep neural networks (DNNs) for cyber-physical systems. In autonomous driving,

SafeRx-Agent: A Knowledge-Grounded Multi-Agent Framework for Safe and Explainable Medication Recommendation

arXiv:2605.29146v1 Announce Type: cross Abstract: Medication recommendation predicts medications for patient visits, but existing methods still face two key challenges. At the model level, traditional

SigmaMedStat: Temporal Signal Modeling for ICU False Alarm Reduction

arXiv:2605.29236v1 Announce Type: new Abstract: Alarm fatigue in intensive care units (ICUs) is a well documented patient safety crisis. Clinical monitors generate 350 or more alarms per patient per d

V2XCrafter: Learning to Generate Driving Scene Across Agents

arXiv:2605.29471v1 Announce Type: new Abstract: Collaborative driving systems leverage vehicle-to-everything (V2X) communication for multi-agent collaborative perception to enhance driving safety, yet

AOE: Exhaustive Out-of-Distribution Detection via Recalibrating Outlier Labels

arXiv:2605.28021v1 Announce Type: new Abstract: Out-of-distribution (OOD) detection is essential for deploying machine learning models in open-world and safety-critical scenarios, where test inputs ma

COTTA: Context-Aware Transfer Adaptation for Trajectory Prediction in Autonomous Driving

arXiv:2604.00402v2 Announce Type: replace-cross Abstract: Developing robust models to accurately predict the trajectories of surrounding agents is fundamental to autonomous driving safety. However, mo

Disentangling Adversarial Prompts: A Semantic-Graph Defense for Robust LLM Security

arXiv:2605.27823v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly vulnerable to adversarial prompts that exploit semantic ambiguities to bypass safety mechanisms, resulti

Provably Guaranteed Polytopic Uncertainty Quantification for SLAM

arXiv:2605.28172v1 Announce Type: new Abstract: In safety-critical robotics applications, guaranteed and practical uncertainty quantification (UQ) in perception is vital. Many existing works either of

safetyarxiv-cs-ro

Voluntary Collusion with Secret Tools in Competing LLM Agents

arXiv:2605.27593v1 Announce Type: new Abstract: Even when a tool is explicitly described as unfair and harmful to others, ostensibly safety-aligned LLM agents still voluntarily engage in secret collus

CmIVTP: Cross-modal Interaction-based Vessel Trajectory Prediction for Maritime Intelligence

arXiv:2605.26524v1 Announce Type: cross Abstract: Maritime intelligent transportation systems (MITS) are essential for ensuring navigation safety and efficiency in busy waterways. However, accurate ve

27 May 2026

Certified Robustness from Approximate Gaussian Mixture Structures in Pretrained Latent Spaces

arXiv:2605.25352v1 Announce Type: cross Abstract: Deep learning models are vulnerable to adversarial perturbations, raising important concerns for safety-critical deployment. Empirical defenses can ac

Measuring the Depth of LLM Unlearning via Activation Patching

arXiv:2605.24614v1 Announce Type: cross Abstract: Large language model (LLM) unlearning has emerged as a crucial post-hoc mechanism for privacy protection and AI safety, yet auditing whether target kn

Model Releases

Reflect-Guard: Enhancing LLM Safeguards against Adversarial Prompts via Logical Self-Reflection

model-releasesarxiv-cs-ai

arXiv:2605.24834v1 Announce Type: cross Abstract: Large language model (LLM) safety classifiers such as Llama Guard are effective at detecting overtly harmful prompts but remain vulnerable to adversar

Steering Beyond the Support: Adversarial Training on Unsupervised Jailbroken Activation Simulation

arXiv:2605.24535v1 Announce Type: cross Abstract: Jailbreak prompts can trigger harmful completions on aligned LLMs, In accordance, safety steering has been proposed: test-time activation intervention

TorchLean: Formalizing Neural Networks in Lean

arXiv:2602.22631v2 Announce Type: replace-cross Abstract: Neural networks are increasingly deployed in scientific, safety critical, and mission critical pipelines, yet verification and analysis are of

Graph-based Complexity Forecasts in UK En Route Airspace Using Relevant Aircraft Interactions

arXiv:2605.23696v1 Announce Type: new Abstract: Effectively managing Air Traffic Control Officer (ATCO) workload is crucial in maintaining operational safety. Group supervisors use tools that estimate

Human-in-the-Loop Multi-Agent Ventilator Decision Support with Contextual Bandit Preference Learning

arXiv:2605.23320v1 Announce Type: new Abstract: Ventilator decision support requires sequential decisions that track evolving physiology and disease trajectories while respecting safety boundaries and

Relevant Walk Search for Explaining Graph Neural Networks

arXiv:2605.23673v1 Announce Type: new Abstract: Graph Neural Networks (GNNs) have become important machine learning tools for graph analysis, and its explainability is crucial for safety, fairness, an

UfM: Uncertainty from Motion for DNN Depth Estimation Using Gaussians

arXiv:2605.23098v1 Announce Type: new Abstract: Reliable uncertainty estimation is critical for deploying monocular depth deep neural networks (DNNs) in safety-critical robotic systems. Conventional u

safetyarxiv-cs-ro

Expectation Consistency Loss: Rethink Confidence Calibration under Covariate Shift

arXiv:2605.21552v1 Announce Type: new Abstract: Confidence calibration for classification models is vital in safety-critical decision-making scenarios and has received extensive attention. General con

MambaGaze: Bidirectional Mamba with Explicit Missing Data Modeling for Cognitive Load Assessment from Eye-Gaze Tracking Data

arXiv:2605.22775v1 Announce Type: new Abstract: Real-time cognitive load assessment from eye-tracking signals could potentially enable adaptive human-centered-AI such as safety-critical applications s

The Matching Principle: A Geometric Theory of Loss Functions for Nuisance-Robust Representation Learning

arXiv:2605.22800v1 Announce Type: new Abstract: Robustness, domain adaptation, photometric and occlusion invariance, compositional generalisation, temporal robustness, alignment safety, and classical

Visibility nowcasting in South Korea: a machine learning approach to class imbalance and distribution shift

arXiv:2605.21507v1 Announce Type: cross Abstract: Atmospheric visibility is a critical variable for transportation safety and air quality management, however, accurate prediction remains challenging d

Bounding-Box Trajectories Matter for Video Anomaly Detection

arXiv:2605.21957v1 Announce Type: new Abstract: Video anomaly detection is critical for public safety and security, yet remains highly challenging despite extensive research due to large variations in

22 May 2026

LACO: Adaptive Latent Communication for Collaborative Driving

arXiv:2605.22504v1 Announce Type: cross Abstract: Collaborative driving aims to improve safety and efficiency by enabling connected vehicles to coordinate under partial observability. Recent approache

22 May 2026

ScenePilot: Controllable Boundary-Driven Critical Scenario Generation for Autonomous Driving

arXiv:2605.21168v1 Announce Type: new Abstract: Safety-critical scenarios are central to evaluating autonomous driving systems, yet their rarity in naturalistic logs makes simulation-based stress test

22 May 2026

Domain-Adaptable Reinforcement Learning for Code Generation with Dense Rewards

arXiv:2605.21180v1 Announce Type: new Abstract: Large language models show strong potential for automated code generation, but lack guarantees for correctness, quality, safety, and domain-specific con

21 May 2026

LASH: Adaptive Semantic Hybridization for Black-Box Jailbreaking of Large Language Models

arXiv:2605.21362v1 Announce Type: new Abstract: Jailbreak attacks expose a persistent gap between the intended safety behavior of aligned large language models and their behavior under adversarial pro

safetyarxiv-cs-cl

21 May 2026

Proximal State Nudging: Reducing Skill Atrophy from AI Assistance

arXiv:2605.20355v1 Announce Type: cross Abstract: Skill atrophy, the gradual decline of human capability under AI assistance, poses a safety risk in shared-control of semi-autonomous systems, where op

21 May 2026

Faster-GCG: Efficient Discrete Optimization Jailbreak Attacks against Aligned Large Language Models

arXiv:2410.15362v2 Announce Type: replace-cross Abstract: Aligned Large Language Models (LLMs) have attracted significant attention for their safety, particularly in the context of jailbreak attacks t

FlowErase-RL: Rethinking Concept Erasure as Reward Optimization in Flow Matching Models

arXiv:2605.19739v1 Announce Type: new Abstract: Recent advances in flow matching models have significantly improved text-to-image generation quality, but also introduce growing safety risks due to the

Hard-Label Black-Box Attacks on 3D Point Clouds

arXiv:2412.00404v2 Announce Type: replace Abstract: With the maturity of depth sensors in various 3D safety-critical applications, 3D point cloud models have been shown to be vulnerable to adversarial

Implicit Action Chunking for Smooth Continuous Control

arXiv:2605.19592v1 Announce Type: cross Abstract: Reinforcement learning often produces high-frequency oscillatory control signals that undermine the safety and stability required for physical deploym

Improved visual-information-driven model for crowd simulation and its modular application

arXiv:2504.03758v4 Announce Type: replace-cross Abstract: Crowd movement simulation is crucial for pedestrian safety management and facility design. Data-driven models offer the potential to improve r

Sampling-Based Safe Reinforcement Learning

arXiv:2605.19469v1 Announce Type: cross Abstract: Safe exploration remains a fundamental challenge in reinforcement learning (RL), limiting the deployment of RL agents in the real world. We propose Sa

Activation Steering with a Feedback Controller

arXiv:2510.04309v3 Announce Type: replace Abstract: Controlling the behaviors of large language models (LLM) is fundamental to their safety alignment and reliable deployment. However, existing steerin

Assessing Localization Technologies for Pedestrian Collision Avoidance

arXiv:2605.18295v1 Announce Type: new Abstract: Robust pedestrian safety is crucial to the next-generation of intelligent transportation systems. Such systems rely on active pedestrian localization an

safetyarxiv-cs-ro

Assured autonomy: How operations research powers and orchestrates generative AI systems

arXiv:2512.23978v2 Announce Type: replace Abstract: Generative artificial intelligence (GenAI) is shifting from conversational assistants toward agentic systems -- autonomous decision-making systems t

Model Releases

Constrained Policy Optimization via Sampling-Based Weight-Space Projection

model-releasesarxiv-cs-lg

arXiv:2512.13788v2 Announce Type: replace Abstract: Safety-critical learning requires policies that improve performance without leaving the safe operating regime. We study constrained policy learning

Density-Ratio Weighted Behavioral Cloning: Learning Control Policies from Corrupted Datasets

arXiv:2510.01479v2 Announce Type: replace Abstract: Offline reinforcement learning (RL) enables policy optimization from fixed datasets, making it suitable for safety-critical applications where onlin

Evaluating AI Alignment in LLMs: Output Analysis of Value Priorities Across 75 Models with Human Benchmarking

arXiv:2506.12617v4 Announce Type: replace Abstract: Large language models (LLMs) are increasingly used in human-AI interaction research and practice, yet existing capability and safety benchmarks reve

Forgetting is Competition: Rethinking Unlearning as Representation Interference in Diffusion Models

arXiv:2603.00975v2 Announce Type: replace-cross Abstract: Deployed text-to-image diffusion models increasingly require post-hoc concept unlearning for copyright claims, artist opt-outs, safety updates

ISEP: Implicit Support Expansion for Offline Reinforcement Learning via Stochastic Policy Optimization

arXiv:2605.18320v1 Announce Type: cross Abstract: Offline reinforcement learning methods typically enforce strict constraints to ensure safety; yet this rigidity often prevents the discovery of optima

Keeping an Eye on AI: A Framework for Effective Human Oversight of AI Systems

arXiv:2605.16278v1 Announce Type: cross Abstract: The use of Artificial Intelligence (AI) in high-risk, decision-making scenarios presents technical, safety, and normative challenges; problems that ma

Meltdown: Circuits and Bifurcations in Point-Cloud-Conditioned 3D Diffusion Transformers

arXiv:2602.11130v2 Announce Type: replace-cross Abstract: Sparse point clouds are a common input modality for 3D surface reconstruction, including in safety-critical settings such as surgical navigati

Semantic Smoothing via Novel View Synthesis for Robust SAR Image Classification

arXiv:2605.16440v1 Announce Type: cross Abstract: Deep neural networks are vulnerable to adversarial perturbations, limiting deployment in safety-critical applications such as synthetic aperture radar