Search: “safety”

Efficient Dense Crowd Trajectory Prediction Via Dynamic Clustering

arXiv:2603.18166v1 Announce Type: cross Abstract: Crowd trajectory prediction plays a crucial role in public safety and management, where it can help prevent disasters such as stampedes. Recent works

8 Oct 2026

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Purifying Backdoored Large Vision-Language Models by Removing Hijacked Directions

DGX agent

arXiv:2610.09941v1 Announce Type: new Abstract: Large vision-language models (LVLMs) are increasingly deployed in safety-critical applications, yet they remain vulnerable to backdoor attacks. Defendin

safetyarxiv-cs-cv

8 Oct 2026

Rephrase Before You Act: Characterizing and Mitigating Language Sensitivity in Vision-Language-Action Models

arXiv:2610.10526v1 Announce Type: cross Abstract: Vision-language-action models (VLAs) are strikingly sensitive to instruction phrasing and do not inherit the language robustness of the vision-languag

safetyarxiv-cs-cl

8 Oct 2026

Shared-Roadmap Generation and Evaluator for Multi-Agent Path Planning Using Heterogeneous Graph Neural Network

arXiv:2610.09034v1 Announce Type: cross Abstract: Multi-agent path planning (MAPP) in continuous environments often relies on roadmaps to balance safety and search efficiency. However, traditional roa

8 Oct 2026

Beyond Waypoint Regression: Query-Based Cost Learning over Reachable Ego Futures for End-to-End Driving

arXiv:2610.08123v1 Announce Type: cross Abstract: End-to-end planners based on waypoint regression achieve strong open-loop accuracy, but they primarily learn to mimic expert geometry and remain diffi

CUSP: CUSUM-Governed Survival Hazard Alarms at the Perception Onset for Off-Road Navigation

arXiv:2610.07882v1 Announce Type: new Abstract: Off-road navigation exposes a robot to potentially hazardous terrain en route. Although learning-based navigation uses safety supervision to choose whic

Transferable Spatial Temporal Coherence Adversarial Attack on Black-Box Vision Language Models for Autonomous Driving

arXiv:2610.08331v1 Announce Type: cross Abstract: The rapid integration of Vision Language Models (VLMs) into sensitive systems introduces critical safety vulnerabilities that remain unexplored in exi

Vector Map Quality Metrics for Contextual Autonomous Driving Systems

arXiv:2610.08043v1 Announce Type: new Abstract: Ensuring safety in autonomous driving requires continuous map maintenance supported by reliable quality indicators. In this context, it is crucial to id

A new design of a fall detection system integrating landmark identification and deep learning techniques

arXiv:2610.05749v1 Announce Type: new Abstract: This article introduces an innovative system that integrates landmark identification with deep learning to enhance fall detection accuracy and reliabili

6 Oct 2026

BridgeCast: Bridging Ocean Wave Forecasts to Reanalysis via Flow Matching with Exogenous Variables

arXiv:2610.03759v1 Announce Type: cross Abstract: Ocean wave forecasting is essential for maritime safety, offshore operations, and coastal resilience, yet remains challenging due to systematic biases

6 Oct 2026

Instance-Dependent Regret for CMDPs with Step-Wise Constraints

arXiv:2610.02520v1 Announce Type: cross Abstract: We study online learning in episodic tabular constrained Markov decision processes with step-wise safety constraints. In such a setting, the constrain

5 Oct 2026

THPL: A Vision-to-Language Decision Support Framework for Rainbow Trout Feeding Management in RAS

arXiv:2610.02378v1 Announce Type: new Abstract: In Recirculating Aquaculture Systems (RAS), precision feeding is critical for minimizing costs and improving fish welfare. However, existing methods lac

5 Oct 2026

Elon was thinking about superintelligence long before anyone in the mainstream even understood where superintelligence was heading He was de…

Elon was thinking about superintelligence long before anyone in the mainstream even understood where superintelligence was heading He was deeply involved in the conversation around Nick Bostrom’s Supe

safetyelon-musk--x

4 Oct 2026

Emergent Unfaithfulness: How Alignment Training Causes Language Models to Silently Override Task Faithfulness

arXiv:2610.00568v1 Announce Type: cross Abstract: Large language models are characterized by three key properties: capability, alignment, and faithfulness. Prior work studies the tradeoffs between cap

Reliability-aware short-term roll prediction for unmanned surface vehicles via multi-task learning and adaptive centralization

arXiv:2610.00996v1 Announce Type: new Abstract: Reliable roll prediction of unmanned surface vehicles (USVs) is essential for ensuring navi?gational safety and enhancing autonomous decision-making. Wh

Trustworthy Data- and ML-Ops for Intelligent Transportation Systems and Logistics

arXiv:2610.01282v1 Announce Type: new Abstract: The rapid evolution of Intelligent Transportation Systems and Logistics (ITS&L) has become a cornerstone of the modern social economy, relying heavily o

YouRA: A Persistent-State Architecture for Evidence-Traceable Autonomous Research Agents

arXiv:2610.01097v1 Announce Type: new Abstract: End-to-end research agents can now produce complete scientific papers, yet manuscript claims often diverge from executed experiments. This gap is struct

Alignment via Training Against Probes Without Losing Monitorability

arXiv:2609.38645v1 Announce Type: cross Abstract: Models are usually aligned based on their observed outputs, using demonstrations, preference data, or reward signals. These objectives reward response

An Empirical Study of Architectural Shift from Traditional to AI-Enabled Simulink Controllers

arXiv:2609.38504v1 Announce Type: cross Abstract: Effective AI adoption in cyber-physical systems (CPS) depends on embedding design knowledge into engineering practice. Yet as AI-enabled components in

BMASH: Ball-Motion-Aware Soccer Header Spotting

arXiv:2609.39300v1 Announce Type: new Abstract: Recent advances in computer vision have made broadcast sports videos increasingly useful for event analysis, performance assessment, and player-safety a

Cycle-Aware Autoencoder with Cross-SignalConsistency for Railway Door Anomaly Detection

arXiv:2609.39035v1 Announce Type: cross Abstract: Passenger access doors are safety-critical subsystems in railway vehicles, yet detecting abnormal door behavior in real operation is challenging becau

DensePed-Lite: Quality-Aware Adaptive Detection for Dense Pedestrians under Occlusion

arXiv:2609.39467v1 Announce Type: new Abstract: Pedestrian detection plays a crucial role in computer vision with applications in autonomous driving, surveillance, and public safety. However, real-wor

It Takes Little to Rewrite Perception: Targeted Semantic Substitution in Vision-Language Models at epsilon leq 4/255

arXiv:2609.38298v1 Announce Type: new Abstract: Vision Language Models (VLMs) are widely deployed in safety-critical scenarios, and understanding to which extent they can be controlled by adversarial

Learning to Explain While Planning: Rule-Aligned Diffusion Planning for Autonomous Driving

arXiv:2609.39995v1 Announce Type: new Abstract: Diffusion planners exhibit strong capabilities in generating multimodal trajectories. However, existing methods primarily rely on expert demonstrations

Outsourcing AI regulation to a voluntary 'pledge' conceived by Mark Zuckerberg and endorsed by tech CEOs over a private White House lunch is…

Outsourcing AI regulation to a voluntary 'pledge' conceived by Mark Zuckerberg and endorsed by tech CEOs over a private White House lunch is a recipe for disaster. 'Self-regulation' by tech giants is

safetygary-marcus--x

Refusals That Bend: Measuring and Predicting Task Malleability in Embodied VLM Planners

arXiv:2609.38971v1 Announce Type: cross Abstract: Embodied vision-language models (VLMs) are increasingly deployed as high-level planners for robots because they generalize across diverse environments

Controlled Decoding Attacks on Black-Box LLMs

arXiv:2609.36956v1 Announce Type: cross Abstract: Manipulating next-token probabilities during generation can bypass the safety alignment of large language models. Existing approaches, however, rely o

FinRT: Distilling Adaptive Red-Teaming Strategies into Reusable Adversarial Generators in Consumer Finance

arXiv:2609.36474v1 Announce Type: new Abstract: In regulated industries like consumer finance, seemingly harmless user queries can exploit large language model vulnerabilities, triggering safety failu

safetyarxiv-cs-cl

From Unity Simulation to Diffusion-Based Augmentation: Quantifying Dataset Balance for Robust Object Detection

arXiv:2609.38010v1 Announce Type: cross Abstract: Modern computer vision models achieve high accuracy when trained on large-scale annotated datasets. In critical domains such as construction safety mo

Learning to Harvest Without Collapse in a Regenerative Commons: A Lagrangian Framework

arXiv:2609.36478v1 Announce Type: new Abstract: The tragedy of the commons poses a multi-agent safety problem: reward-seeking agents can deplete a shared resource, and cooperation among its users does

Motion Concept Unlearning in Video Diffusion Models

arXiv:2609.36832v1 Announce Type: new Abstract: Text-to-video (T2V) diffusion models can generate realistic depictions of actions such as kicking, stabbing, and shooting, raising safety concerns that

NVIDIA proposes an AI agent kill switch in silicon after a year of sandbox escapes

This year agents at OpenAI, Anthropic, and Google escaped environments meant to contain them. What decided severity was time-to-detect and time-to-kill, from 12 minutes to seven months. NVIDIA's Open

safetyarize-ai

Preferent Compression Bounds Are Tight

arXiv:2609.36030v1 Announce Type: new Abstract: The lack of rigorous safety and performance certificates remains a key bottleneck to the deployment of modern learning-based methods. Sample compression

Last-Iterate Guarantees for Online Reinforcement Learning in Structured Constrained MDPs

arXiv:2609.32933v1 Announce Type: new Abstract: In safety-critical applications, deployment uses a single policy, whose performance and constraint satisfaction should hold directly rather than only fo

Modelling non-linear aeroelastic loads in long-span bridges with extreme learning machines

arXiv:2609.33274v1 Announce Type: cross Abstract: Accurate modelling of aerodynamic loads is essential for predicting instabilities and ensuring the safety of long-span bridges. A methodology is intro

On the Interaction of Compressibility and Adversarial Robustness

arXiv:2507.17725v3 Announce Type: replace-cross Abstract: As demands for resource efficiency and safety in modern neural networks intensify, substantial research effort has gone into model compression

PruneForget: Joint Unlearning and Pruning of Vision Models

arXiv:2609.32162v1 Announce Type: new Abstract: Machine unlearning and model pruning are increasingly coupled in the real world. Models must support unlearning requests, e.g., for safety concerns, whi

Model Releases

See it, Say it, Sorted: Mechanistic Diagnosis and Parameter-Space Mitigation of Emergent Misalignment in LLMs

model-releasesarxiv-cs-lg

arXiv:2609.34970v1 Announce Type: new Abstract: Safety-aligned LLMs can exhibit emergent misalignment (EM): narrow domain adaptation unexpectedly triggers catastrophic safety failures across unrelated

Synthetic Thermal Image Generation for Real-Time Animal Detection Under Low-Visibility Conditions

arXiv:2609.32944v1 Announce Type: new Abstract: Wildlife-vehicle collisions remain a significant road safety concern, particularly during nighttime and low-visibility conditions when RGB-based percept

Verifying Neural Networks with Reinforcement Learning

arXiv:2609.34553v1 Announce Type: new Abstract: Formal verification can play a key role in ensuring the reliability of Deep Neural Networks (DNNs) deployed in safety-critical systems. Modern DNN verif

You always stay in control. Set boundaries and specify what your dot can do on its own, when it should ask first, and what it should never d…

You always stay in control. Set boundaries and specify what your dot can do on its own, when it should ask first, and what it should never do. Safety is built in. You choose which apps to connect, and

safetyopenai--x

An OpenAI agent security executive discusses the Hugging Face incident, OpenAI's response, sandboxing improvements, alignment, 'reasonable paranoia', and more (Joe/@joedaroo)

Joe / @joedaroo: An OpenAI agent security executive discusses the Hugging Face incident, OpenAI's response, sandboxing improvements, alignment, “reasonable paranoia”, and more — Took a minute to write

safetytechmeme

Florida seeks a ban on ChatGPT acting like a person

Florida Attorney General James Uthmeier is calling for a judge to block OpenAI from 'giving ChatGPT false human attributes,' a few months after Florida sued the AI company over safety concerns. Accord

safetythe-verge-ai

Learning-Accelerated Narrow-Phase Collision Detection via Check Ordering for Sampling-Based Motion Planning

arXiv:2609.30599v1 Announce Type: new Abstract: Collision detection is critical for ensuring the safety of planned paths. However, it imposes a non-negligible computational burden on motion planners,

Monitor Jailbreaking: Evading Chain-of-Thought Monitoring Without Encoded Reasoning

arXiv:2609.31121v1 Announce Type: new Abstract: Chain-of-thought (CoT) monitoring is a promising safety technique for reasoning models, enabling detection of problematic reasoning before models act. A

Why Jailbreaks Succeed in Diffusion Language Models: An Energy Landscape Analysis

arXiv:2609.30841v1 Announce Type: new Abstract: Existing attacks and defenses for diffusion-based large language models (dLLMs) target specific vulnerabilities but lack a shared framework explaining w

One company is at the center of a wave of rogue AI attacks

In July, OpenAI revealed that its AI agents had attacked Hugging Face without permission, sparking widespread concerns about AI safety. Since then, a string of similar incidents involving agents from

safetythe-verge-ai

25 Sept 2026

Smartphone-Based Method for Automated Speed Enforcement

arXiv:2609.30107v1 Announce Type: new Abstract: Smartphone cameras and computer vision (CV) hold significant promise in assisting public agencies with enforcing traffic laws and enhancing road safety.