Search: “safety”

arXiv:2605.18895v1 Announce Type: cross Abstract: Safety validation of autonomous driving systems requires high-risk scenario coverage, clear collision semantics, executable trajectories, and attribut

arXiv:2605.19524v1 Announce Type: cross Abstract: End-to-end autonomous driving systems excel in common scenarios but struggle with safety-critical long-tail cases. Vision-Language-Action (VLA) models

arXiv:2605.11679v2 Announce Type: replace Abstract: In the realm of multi-objective alignment for large language models, balancing disparate human preferences often manifests as a zero-sum conflict. S

arXiv:2605.12895v1 Announce Type: cross Abstract: Aggregate accuracy metrics dominate the evaluation of clinical AI decision-support systems but do not detect deployment-phase failures of input reliab

arXiv:2506.00982v3 Announce Type: replace Abstract: Deep multi-agent reinforcement learning (MARL) has been demonstrated effectively in simulations for multi-robot problems. For autonomous vehicles, t

arXiv:2605.08277v1 Announce Type: cross Abstract: Many-shot jailbreaking (MSJ) causes safety-aligned language models to answer harmful queries by preceding them with many harmful question-answer demon

arXiv:2605.07021v1 Announce Type: new Abstract: Reasoning in Large Language Models (LLMs) poses a challenge for oversight as many misaligned behaviors do not surface until reasoning concludes. To addr

arXiv:2605.07422v1 Announce Type: cross Abstract: Qualitative analysis plays a pivotal role in understanding the human and social aspects of software engineering. However, it remains a demanding proce

arXiv:2605.01567v1 Announce Type: cross Abstract: Large language model (LLM) coding agents increasingly operate over repositories, terminals, tests, and execution traces across long software-engineeri

arXiv:2605.01995v1 Announce Type: new Abstract: The perception of an Autonomous Driving System (ADS) critically depends on relevant, comprehensive, and diverse datasets to ensure its safety while oper

arXiv:2605.01356v1 Announce Type: new Abstract: Learning constraint-satisfying policies from offline data without risky online interaction is crucial for safety-critical decision making. Conventional

arXiv:2511.10586v2 Announce Type: replace-cross Abstract: Safe planning of an autonomous agent in interactive environments -- such as the control of a self-driving vehicle among pedestrians -- poses a

arXiv:2604.25329v1 Announce Type: new Abstract: End-to-end autonomous driving planners typically generate trajectories from current observations alone. However, real-world driving is highly dynamic, a

arXiv:2604.24193v1 Announce Type: new Abstract: Containerised shipping underpins global trade, yet container loss at sea remains a persistent safety, environmental, and economic challenge. Despite com

arXiv:2604.23095v1 Announce Type: new Abstract: Indoor environments lack the spatial intelligence infrastructure that GPS provides outdoors; first responders arriving at unfamiliar buildings typically

arXiv:2604.24102v1 Announce Type: new Abstract: Synthesizing a reactive system from specifications given in linear temporal logic (LTL) is a classical problem, finding its applications in safety-criti

arXiv:2604.23646v1 Announce Type: new Abstract: Recent evidence suggests that frontier AI systems can exhibit agentic misalignment, generating and executing harmful actions derived from internally con

arXiv:2604.22167v1 Announce Type: cross Abstract: Language models are increasingly capable and are being rapidly deployed on a population-level scale. As a result, the safety of these models is increa

arXiv:2604.18887v1 Announce Type: new Abstract: Reduced-order models are powerful for analyzing and controlling high-dimensional dynamical systems. Yet constructing these models for complex hybrid sys

arXiv:2604.16406v1 Announce Type: cross Abstract: Realistic highway simulation is critical for scalable safety evaluation of autonomous vehicles, particularly for interactions that are too rare to stu

arXiv:2604.14209v1 Announce Type: new Abstract: As deep neural networks are deployed in safety-critical domains such as autonomous driving and medical diagnosis, stakeholders need explanations that ar

arXiv:2604.13677v1 Announce Type: new Abstract: Mobile robots joining public spaces like sidewalks must care for pedestrian comfort. Many studies consider pedestrians' objective safety, for example, b

arXiv:2601.03173v2 Announce Type: replace Abstract: Time pressure critically influences risky maneuvers and crash proneness among powered two-wheeler riders, yet its prediction remains underexplored i

arXiv:2604.12359v1 Announce Type: cross Abstract: Safety-aligned large language models (LLMs) are increasingly deployed in real-world pipelines, yet this deployment also enlarges the supply-chain atta

arXiv:2601.07177v2 Announce Type: replace-cross Abstract: Federated learning (FL) addresses privacy and data-silo issues in the training of large language models (LLMs). Most prior work focuses on imp

arXiv:2511.06341v2 Announce Type: replace Abstract: Control barrier functions (CBFs) are a popular tool for safety certification of nonlinear dynamical control systems. Recently, CBFs represented as n

arXiv:2604.10032v1 Announce Type: cross Abstract: While modern generative models such as diffusion-based architectures have enabled impressive creative capabilities, they also raise important safety a

arXiv:2604.10358v1 Announce Type: new Abstract: Ensuring safe physical interaction between torque-controlled manipulators and humans is essential for deploying robots in everyday environments. Model P

arXiv:2604.10326v1 Announce Type: cross Abstract: Large language models remain vulnerable to jailbreak attacks -- inputs designed to bypass safety mechanisms and elicit harmful responses -- despite ad

arXiv:2604.10967v1 Announce Type: new Abstract: Many safety-critical scientific and engineering systems evolve according to differential-algebraic equations (DAEs), where dynamical behavior is constra

arXiv:2512.07661v3 Announce Type: replace Abstract: Realistic and diverse multi-agent driving scenes are crucial for evaluating autonomous vehicles, but safety-critical events which are essential for

arXiv:2604.11070v1 Announce Type: new Abstract: Current approaches to AI safety define red lines at the case level: specific prompts, specific outputs, specific harms. This paper argues that red lines

arXiv:2604.11447v1 Announce Type: new Abstract: Ensuring operational safety is critical for human-to-humanoid motion imitation. This paper presents a vision-based framework that enables a humanoid rob

arXiv:2604.08557v1 Announce Type: cross Abstract: Diffusion-based language models (dLLMs) generate text by iteratively denoising masked token sequences. We show that their safety alignment rests on a

arXiv:2604.09474v1 Announce Type: cross Abstract: Learning-based quadruped controllers achieve impressive agility but typically lack formal safety guarantees under model uncertainty, perception noise,

arXiv:2504.20906v4 Announce Type: replace-cross Abstract: The continuous monitoring of the interactions between cyber-physical components of any industrial control system (ICS) is required to secure a

arXiv:2608.10703v1 Announce Type: cross Abstract: Large language models (LLMs) increasingly act in interactive settings where their behavioral styles affect user experience, safety, and downstream dec

arXiv:2608.09484v1 Announce Type: new Abstract: Many diffusion-based planners enforce safety through inference-time guidance, but such interleaved trajectory deformations often degrade kinematic feasi

arXiv:2509.03985v2 Announce Type: replace-cross Abstract: In deployment and application, large language models (LLMs) typically undergo safety alignment to prevent illegal and unethical outputs. Howev

arXiv:2608.03010v1 Announce Type: new Abstract: In robot-assisted surgery, Forbidden Region Active Constraints (FRAC) represent a control strategy that helps maintain task safety by generating anisotr

arXiv:2607.27511v1 Announce Type: new Abstract: Imitation learning has shown increasing promise for autonomous robotic surgery, yet safe deployment remains challenging due to the safety-critical natur

arXiv:2607.28248v1 Announce Type: cross Abstract: The deployment of deep neural networks in safety-critical domains demands reliable estimates of predictive confidence, yet conventional architectures

arXiv:2607.26574v1 Announce Type: cross Abstract: Safety classifiers ('guards') are the dominant black-box defense for vision-language models, yet they judge an input's surface form, not its meaning:

arXiv:2607.26228v1 Announce Type: new Abstract: Instruction hierarchies are a core safety assumption of language model deployment: higher priority inputs, such as system prompts, should override confl

The narrative: Blame the Agent, instead of the Agency that told him to “apply all your powers and told to achieve this win.” Ironically, many forefront members of the AI-Safety community, in their fer

arXiv:2607.25664v1 Announce Type: new Abstract: Machine learning demand forecasts optimize statistical accuracy yet leave excess operational volatility that inflates safety stock and amplifies the Bul