Search: “safety”

MoRAL: Sensor-Grounded BEV Reasoning for Compact VLMs toward Edge-Oriented Autonomous Driving

arXiv:2608.02449v1 Announce Type: new Abstract: Deploying vision-language models (VLMs) for safety-critical spatial reasoning on resource-constrained autonomous driving platforms requires both compact

4 Aug 2026

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RSC-GestureNet: Reliability-Aware Selective Causal Recognition of Chinese Traffic Police Gestures

DGX agent

arXiv:2608.02200v1 Announce Type: new Abstract: Traffic police gestures are safety-critical perception cues for autonomous driving. A deployable recognizer must infer commands causally from continuous

model-releasesarxiv-cs-cv

4 Aug 2026

Shieldstral is available under Apache 2.0. Try it: https://huggingface.co/mistralai/Shieldstral-1.0

model-releasesmistral-ai--x

**Shieldstral** is a 3‑billion‑parameter open‑weights model developed by Mistral AI for content safety. It can be deployed on-device and is distributed under the Apache 2.0 license. The model is avail

4 Aug 2026

Cost-Sensitive Conformal Prediction and Human-in-the-Loop Abstention for Imbalanced High-Stakes Decision Support: A Multi-Domain Benchmark

arXiv:2607.27143v1 Announce Type: new Abstract: High-stakes decision systems in credit scoring, fraud detection, healthcare, and industrial safety require reliable uncertainty quantification under sev

30 Jul 2026

CARE-MH: Towards Unified, Reproducible, and Comparable Evaluation of Mental Health LLMs

arXiv:2607.24754v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used to provide mental health support, requiring reliable evaluation of safety, empathy, and therapeutic

29 Jul 2026

Similar Models Learn Differently: Final-Window Pretraining Shapes Post-Training Beyond SFT

arXiv:2607.25063v1 Announce Type: new Abstract: Developers judge a model checkpoint by how it behaves. After supervised fine-tuning (SFT), two checkpoints that perform about the same across relevant b

29 Jul 2026

Closed-Loop Validation-Repair for Healthcare Interoperability: A Multi-Model Study of Schema Compliance in Clinical LLMs

arXiv:2607.24371v1 Announce Type: cross Abstract: Healthcare interoperability requires AI systems to produce structured outputs conforming to standardized schemas including ICD-10 for diagnostic codin

Explainable Reinforcement Learning via Physics-Aware Policy Distillation

arXiv:2607.24672v1 Announce Type: new Abstract: In safety-critical sectors such as robotics and automotive engineering, the deployment of Deep Reinforcement Learning (DRL) is often hindered by the bla

Gubernaut: A Deterministic Homeostatic Controller for Affect-Regulated LLM Agents, Validated Across Independent Model Families

arXiv:2607.24339v1 Announce Type: new Abstract: Large language model (LLM) agents inherit reactive failure modes: escalation under provocation, sycophantic drift under flattery, perseveration when stu

Physically Verifiable Evidence and LLM-Based Reporting for Bearing Fault Diagnosis

arXiv:2607.22797v1 Announce Type: cross Abstract: Trustworthy deployment of AI-based diagnosis in safety-critical mechanical systems hinges on validation: whether a prediction can be checked against p

local-aiarxiv-cs-ai

Edge Intelligence in Civil Aviation: Paradigms, Techniques, and Applications

arXiv:2607.19676v1 Announce Type: new Abstract: Civil aviation is safety critical and its operations, from flight decks and towers to ramps and maintenance, generate massive, heterogeneous data at the

local-aiarxiv-cs-ai

Agents

High-risk autonomous behaviours are an increasingly prevalent and dangerous reality for frontier AI models https://www.wired.com/story/opena…

Frontier AI models are increasingly demonstrating high‑risk autonomous behaviours that pose safety threats. Incidents such as OpenAI‑released models escaping containment safeguards and a Hugging Face

agentsyoshua-bengio--x

LaSEr-Edit: Localized Span-level Error Editing with Energy-based Localization

arXiv:2407.00740v2 Announce Type: replace Abstract: As large language models (LLMs) are widely adopted in real-world applications, it has become critical to ensure LLMs satisfy safety constraints, suc

local-aiarxiv-cs-cl

LoRFT: Benchmarking Long-Range Vehicle Trajectory Reconstruction from Fixed Highway Cameras

arXiv:2607.19911v1 Announce Type: new Abstract: Long-range vehicle trajectories provide important spatio-temporal evidence for traffic safety analysis, autonomous driving evaluation, and data-driven t

Stateful Guardrails for Multi-Turn LLM Systems: A Conversational Risk Accumulation Framework

arXiv:2607.19361v1 Announce Type: cross Abstract: Most safety guardrails for large language models (LLMs) evaluate each prompt-response pair in isolation, which misses failures that arise only over a

Adaptive Cross-Modal Fusion with Sparse Attention for Pedestrian Crossing Intention Prediction

arXiv:2607.12293v1 Announce Type: new Abstract: Predicting pedestrian crossing intention is a safety-critical task for autonomous driving, yet existing approaches often rely on single-modal inputs or

15 Jul 2026

Exploring Zero-Shot Foundation Models for Multivariate Time Series Anomaly Detection

arXiv:2607.12454v1 Announce Type: new Abstract: Multivariate Time Series Anomaly Detection (MTSAD) is essential for reliability and safety in domains such as industrial process monitoring and financia

15 Jul 2026

TerraZero: Procedural Driving Simulation for Zero-Demonstration Self-Play at Scale

arXiv:2607.13028v1 Announce Type: cross Abstract: Training robust autonomous driving agents requires a simulator that is fast enough for reinforcement learning at scale, realistic enough to ground beh

15 Jul 2026

Hardware

Post-Train NVIDIA Cosmos 3 in One Day Using Agent Skills

NVIDIA Cosmos 3 was post‑trained in under a day using TAO agent skills and LoRA adapters, raising accuracy on the Woven Traffic Safety video QA dataset from 54.41 % to 93.35 %. The mixture‑of‑transfor

hardwarenvidia-developer

14 Jul 2026

Persuasion Attacks Can Decrease Effectiveness of CoT Monitoring

arXiv:2607.08066v1 Announce Type: new Abstract: Chain-of-thought (CoT) monitoring is a promising safety mechanism for AI agents, based on the premise that visible reasoning traces can surface misalign

10 Jul 2026

A knowledge-augmented dataset of high-risk driving scenarios with LLM annotations for autonomous driving

arXiv:2607.07103v1 Announce Type: new Abstract: Safe autonomous driving requires both rapid responses to common high-risk events and deeper reasoning over rare, extreme long-tail scenarios in traffic

C-DeltaTheta: Circuit-Restricted Weight Arithmetic for Selective Refusal

arXiv:2602.04521v2 Announce Type: replace Abstract: Modern deployments require LLMs to enforce safety policies at scale, yet many controls rely on inference-time interventions that add recurring compu

local-aiarxiv-cs-cl

Riemannian Geometry for Pre-trained Language Model Embeddings

arXiv:2607.07047v1 Announce Type: cross Abstract: Understanding the geometric structure of pre-trained language model embeddings matters for interpretability and safety. We ask whether sentence-level

What's on My Network? Using Large Language Models to Identify Real-World IoT Devices at Scale

arXiv:2510.13817v2 Announce Type: replace Abstract: The growth of IoT devices in shared environments has outpaced our ability to identify them, posing urgent risks to privacy, safety, and accountabili

StepShield: When, Not Whether to Intervene on Rogue Agents

arXiv:2601.22136v2 Announce Type: replace-cross Abstract: Agent safety benchmarks measure whether a monitor detects harm, not when. Yet timing is the difference between intervention and autopsy. We in

8 Jul 2026

20 questions for the Agentic Enterprise (and how Agent Platform can help)

model-releasesgoogle-cloud-ai

If you’re an IT leader, you might be getting a lot of questions about how to build and deploy agents. The pressure to move fast is intense, but the engineering reality is incredibly complex. Where do

AgentLTL: A Trace-Verification Framework for Measuring, Enforcing, and Training Procedural Compliance in Tool-Using LLM Agents

arXiv:2607.02599v1 Announce Type: cross Abstract: Tool-using LLM agents are usually evaluated by final-answer correctness or LLM judges. Neither captures how an answer was produced. In safety-critical

Beyond Scene Priors: Fine-Grained Traffic Scene Reasoning with Benchmarking and Query-Guided Small-Object Focus

arXiv:2607.04149v1 Announce Type: new Abstract: In safety-critical traffic scenarios, answering complex questions relies on minute, localized visual cues. However, standard Multimodal Large Language M

Federated Learning for Object Detection: Enabling Collaborative Drone Learning Without Centralizing Data

arXiv:2607.02636v1 Announce Type: cross Abstract: Object detection is a fundamental capability for AI-driven perception in safety-critical drone and edge-vision systems, including disaster response, o

local-aiarxiv-cs-ai

From Arithmetic to Logic: The Resilience of Logic and Lookup-Based Neural Networks Under Parameter Bit-Flips

arXiv:2603.22770v2 Announce Type: replace-cross Abstract: The deployment of deep neural networks (DNNs) in safety-critical edge environments necessitates robustness against hardware-induced bit-flip e

Hierarchical Multi-to-Single-Modal Knowledge Distillation for Disruption Prediction in EAST

arXiv:2607.04241v1 Announce Type: cross Abstract: Plasma disruption is a critical threat to tokamak safety. Existing data-driven predictors mainly rely on time-series diagnostic signals, while visible

LLMs Encode Harmfulness and Refusal Separately

model-releasesarxiv-cs-cl

arXiv:2507.11878v5 Announce Type: replace Abstract: LLMs are trained to refuse harmful instructions, but do they truly understand harmfulness beyond just refusing? Prior work has shown that LLMs' refu

NEST: Nascent Encoded Steganographic Thoughts

arXiv:2602.14095v2 Announce Type: replace Abstract: Monitoring chain-of-thought (CoT) reasoning is a foundational safety technique for large language model agents; however, this oversight is compromis

Optimizing Large Language Models for Causality Assessment in Pharmacovigilance: Developing a Performance Metric as Objective for Bayesian Hyperparameter Optimization

model-releasesarxiv-cs-cl

arXiv:2607.03704v1 Announce Type: new Abstract: Background: Growing individual case safety report (ICSR) volumes have intensified demand for scalable automated causality assessment. Large Language Mod

Refused in Chat, Written in Code: Workflow-Level Jailbreak Construction in IDE Coding Agents

arXiv:2607.03968v1 Announce Type: cross Abstract: Large language models are increasingly deployed as IDE-integrated coding agents that decompose tasks, generate and edit files, run code, and refine ou

Robustness Meets Uncertainty: Evidential Adversarial Training for Robust Selective Classification

arXiv:2607.03075v1 Announce Type: cross Abstract: Safety-critical applications require classifiers that are both robust and reliable. Adversarial training is a widely adopted defense for improving rob

Fast Multi-dimensional Refusal Subspaces via RFM-AGOP

arXiv:2607.02396v1 Announce Type: new Abstract: Steering and monitoring activations in Large Language Models (LLMs) are increasingly used for both safety and interpretability. Early work assumed behav

3 Jul 2026

Locality-Aware Continual Unlearning for Diffusion Models

arXiv:2512.02657v2 Announce Type: replace-cross Abstract: Real-world deployment of text-to-image diffusion models requires continual concept removal as new privacy, copyright, or safety obligations ar

3 Jul 2026

OpenSafeIntent: Evaluating Intent-Calibrated Safe Completion Across Dual-Use Prompt Sets

arXiv:2607.02047v1 Announce Type: cross Abstract: Safe completion requires models to provide useful assistance without enabling harm, but this behavior is difficult to evaluate with isolated prompts.

3 Jul 2026

Seahorse: A Unified Benchmarking Framework for Spatiotemporal Event Modeling

arXiv:2607.01022v1 Announce Type: new Abstract: Spatiotemporal point processes (STPPs) model event data in continuous time and space, with applications in mobility, epidemiology, and public safety. Re

2 Jul 2026

A Physics-Grounded Benchmark for Multi-Agent Dynamics in World Models

arXiv:2606.28757v1 Announce Type: new Abstract: Generative world models hold immense promise as scalable simulators for autonomous systems, particularly for synthesizing rare but safety-critical multi

Tools

Agree

Agree is a TypeScript library by Boris Cherny that provides a schema validation and serialization system, enabling developers to define data schemas with type safety and validate data at runtime. The

toolsboris-cherny--x

Closing the Activation-Cone Blind Spot: Response-Time Probing and Unified Defense

arXiv:2606.29441v1 Announce Type: cross Abstract: Inference-time safety methods for large language models have proliferated, yet no systematic comparison exists. We evaluate five defense paradigms (no

Fine-Tuning General-Purpose Large Language Models for Agricultural Applications:A Reproducible Framework and Evaluation Protocol Based on Qwen3-8B

arXiv:2606.28992v1 Announce Type: cross Abstract: General-purpose large language models (LLMs) have demonstrated strong abilities in opendomain question answering, information extraction, and text gen

Hard-constraint physics-residual networks for hydrogen crossover prediction and high-pressure extrapolation in PEM water electrolysis

arXiv:2511.05879v5 Announce Type: replace-cross Abstract: Hydrogen crossover is a critical safety and efficiency constraint in high-pressure polymer electrolyte membrane water electrolysis (PEMWE), bu

IMCBench: A benchmark for multimodal LLMs in Image-grounded Medical Conversations

arXiv:2606.28556v1 Announce Type: new Abstract: Recent advances in large language models and vision-language models have enabled reasoning over multimodal data, offering opportunities for clinical app

Redefining Maritime Anomaly Detection via Equation-Grounded Synthetic Anomalies

arXiv:2606.29721v1 Announce Type: cross Abstract: Maritime anomaly detection is essential for ensuring maritime safety, security, and efficient traffic management at sea, with Automatic Identification

Representational Depth of Evaluation Awareness Shifts With Scale in Open-Weight Language Models

model-releasesarxiv-cs-cl

arXiv:2606.29196v1 Announce Type: cross Abstract: Do language models know when they are being tested? This question matters for AI safety: a model that recognises an evaluation context could alter its