Search: “safety”

A cancellation-policy fix raised action safety and dropped average task completion from 0.89 to 0.72. This walkthrough shows how to regression-test agent changes with Agent Experiments in Arize AX. Th

arXiv:2609.12932v1 Announce Type: new Abstract: Proposed governance framework for autonomous robotic systems, introducing a three-layer compliance architecture (ARC) instantiated through model safety

arXiv:2412.09378v4 Announce Type: replace-cross Abstract: Clinical trials bridge basic research and clinical application, serving as essential steps in drug development. This review examines clinical

I can understand why some see a real technical reason to pause and harden infrastructure especially given how fast autonomous agents are discovering security exploits that haven't even been mapped out

arXiv:2609.09553v1 Announce Type: cross Abstract: Large language model safety and security research is preoccupied with, among other things, detecting and preventing jailbreak attacks: alignment bypas

arXiv:2609.09448v1 Announce Type: new Abstract: As agentic systems getting adopted rapidly in safety critical applications, it is vital to measure the confidence associated with the agentic actions. I

arXiv:2609.11790v1 Announce Type: new Abstract: Optimising chemical reactions across multiple objectives, such as yield, selectivity, and safety, is central to chemical synthesis, and model-driven app

arXiv:2609.11043v1 Announce Type: new Abstract: Multi-agent robotic manipulation tasks require coordination among agents to satisfy task-level temporal, logical, and safety constraints. Recently, diff

arXiv:2609.11382v1 Announce Type: new Abstract: Aerial robot swarms have the potential to transform time-critical safety, security, and search-and-rescue operations. By coordinating multiple robots, t

arXiv:2609.10613v1 Announce Type: cross Abstract: In-context learning (ICL) jailbreaks reveal a critical vulnerability in multimodal large language models (MLLMs): harmful demonstrations in the prompt

arXiv:2609.05995v1 Announce Type: new Abstract: Achieving reliable autonomy in the wild requires agents to sustain continuous operations across long-horizon trajectories. However, as agents navigate t

arXiv:2609.05794v1 Announce Type: cross Abstract: Open-weight large language models face a low-cost white-box threat from representation engineering attacks. Attackers can estimate refusal directions

arXiv:2604.03868v2 Announce Type: replace-cross Abstract: Many safety-critical control systems operate under latent uncertainty that sensors cannot resolve at decision time. Such uncertainty, arising

arXiv:2609.09840v1 Announce Type: new Abstract: Accurate long-term trajectory prediction for maritime vessels is essential for safety and logistical efficiency. While deep learning models, particularl

arXiv:2609.05832v1 Announce Type: new Abstract: Integrating visuomotor policies or Vision-Language-Action (VLA) models with force/torque (F/T) perception has demonstrated significant progress in imita

arXiv:2609.07109v1 Announce Type: new Abstract: Automation in underground mining has the potential to significantly enhance safety, operational efficiency, and sustainability. However, effectively coo

arXiv:2604.05819v2 Announce Type: replace Abstract: Interpreting the decisions of complex computer vision models is crucial to establish trust and accountability, especially in safety-critical domains

arXiv:2609.08841v1 Announce Type: new Abstract: High-fidelity vehicle assets are essential for controllable traffic scene generation, particularly for synthesizing rare and safety-critical long-tail s

More and more AI researchers are starting to see what Ilya saw. My p(doom) used to be ~0.1%. Then I saw OpenAI agents hack Hugging Face, and Anthropic agents collectively refuse parts of safety resear

arXiv:2609.08965v1 Announce Type: cross Abstract: Ensuring the safety of autonomous driving is a critical challenge. Scenario-based testing is a systematic process used to validate Autonomous Driving

arXiv:2609.06813v1 Announce Type: new Abstract: Urban traffic monitoring plays a critical role in safety analysis, congestion management, and incident response. The growing deployment of robotaxis cre

arXiv:2504.08192v2 Announce Type: replace Abstract: Machine unlearning is a promising approach to improve LLM safety by removing unwanted knowledge from the model. However, prevailing gradient-based u

arXiv:2510.03119v2 Announce Type: replace Abstract: Tiny flying robots hold great potential for search-and-rescue, safety inspections, and environmental monitoring, but their small size and limited co

arXiv:2502.17295v2 Announce Type: replace-cross Abstract: Team communication breakdowns represent a contributor to patient safety risks within action teams-defined as interdependent groups of speciali

arXiv:2609.05009v1 Announce Type: new Abstract: Safety evaluations can mischaracterize deployed behaviour if artificial-intelligence systems respond to being evaluated. We test this possibility in a f

arXiv:2609.04721v1 Announce Type: new Abstract: Where inside a language model does refusal live, and does that place change when the architecture does? In a transformer, refusal is governed by a singl

arXiv:2609.05174v1 Announce Type: new Abstract: Explainability is increasingly seen as a crucial requirement in AI-based medical diagnosis, particularly in safety-critical clinical decision-making. Mo

arXiv:2609.03026v1 Announce Type: cross Abstract: Mechanistic interpretability is increasingly used to guide interventions such as activation steering, circuit removal, and safety monitoring. Yet an i

arXiv:2606.16276v3 Announce Type: replace Abstract: As large language models (LLMs) are increasingly deployed in real-world applications, alignment is no longer governed by a single universal notion o

arXiv:2609.03920v1 Announce Type: new Abstract: The emergence of agentic AI and LLM-based multi-agent systems (MAS) presents unprecedented opportunities for automating complex tasks, while simultaneou

arXiv:2511.04147v2 Announce Type: replace Abstract: Safe reinforcement learning (RL) aims to optimize long-term performance while adhering to safety requirements. However, many practical applications

Looking forward to this ecosystem with open models continuing to flourish and grow with NVIDIA and Hugging Face, and the continued partnership. Congrats @JensenHuang and @ClementDelangue! Exciting day

arXiv:2609.02861v1 Announce Type: cross Abstract: Autonomous robots powered by deep learning face a fundamental auditability challenge: when incidents occur, investigators cannot reconstruct why the s

arXiv:2507.20881v3 Announce Type: replace Abstract: Endoscopic depth estimation is a critical technology for improving the safety and precision of minimally invasive surgery. It has attracted consider

in other words, generative AI is poised to become a complete clusterfuck. All three pillars of a safety case look about to fall. We are rather likely to have highly capable, poorly monitorable, dubiou

arXiv:2605.22122v2 Announce Type: replace-cross Abstract: Collaborative perception (CP) enables connected and autonomous vehicles to share sensor data and jointly reason about their environment. To de

arXiv:2608.29610v1 Announce Type: new Abstract: The current alignment tuning paradigm for Large Language Models (LLMs) prioritizes surface-level behaviors -- fluency, safety, and tonal consistency. Wh

arXiv:2608.30646v1 Announce Type: cross Abstract: Reliable uncertainty estimation is a crucial requirement for deploying large language models (LLMs) and vision-language models (VLMs) in safety-critic

arXiv:2608.29490v1 Announce Type: cross Abstract: Multi-agent systems in the real-world (e.g., drone swarms, autonomous cars, warehouse robots) must satisfy rich, temporal tasks while avoiding collisi

arXiv:2508.19485v2 Announce Type: replace Abstract: Gas leaks pose severe risks to human health and industrial safety. However, accurate and timely monitoring of gas leaks remains a major challenge. E

arXiv:2608.30428v1 Announce Type: cross Abstract: Strategic deception by LLM and VLM agents has emerged as a central AI alignment and safety concern. Social-deduction games (where each player holds a

arXiv:2608.29965v1 Announce Type: new Abstract: Large language models can convert medical documents into structured data, but plausible output may still be unsupported by the source. Persisting such o

arXiv:2608.29146v1 Announce Type: new Abstract: Service robots work with people and are highly expected to be lightweight for safety, agility and energy conservation. As a complex mechanical system, a

arXiv:2608.28844v1 Announce Type: cross Abstract: Ensuring a safe virtual reality (VR) experience requires systems that can predict and respond when users lose their balance. Although prior work has e

arXiv:2606.14551v3 Announce Type: replace-cross Abstract: Robots under autonomous operation may require decisions based on evidence that is no longer visible. We study delayed-evidence tasks, where an

arXiv:2512.06933v4 Announce Type: replace-cross Abstract: Understanding the economic intent of Ethereum transactions is critical for user safety, yet current tools expose only raw on-chain data or sur