Search: “safety”

arXiv:2609.25945v1 Announce Type: new Abstract: The field of Artificial Intelligence has been adopted for many application domains. Vision Language Models are one of the recently advanced AI technique

arXiv:2609.24250v1 Announce Type: new Abstract: The shipping industry has a significant impact on the global economy, emphasizing the need for operational availability and safety through the use of ef

arXiv:2609.24376v1 Announce Type: new Abstract: We propose a safety-critical framework for the cooperative transportation of passive targets in microgravity, where a team of chaser robots acts through

arXiv:2609.23100v1 Announce Type: new Abstract: Where to look and how to move? A robot navigating an unmapped environment must do both at once, and the two goals pull against each other. The regions m

arXiv:2609.23071v1 Announce Type: cross Abstract: This thesis proposes a framework for 'responsible intelligence' to address AI's critical challenges in safety, ethics, and cultural sensitivity. It ad

arXiv:2609.20822v1 Announce Type: cross Abstract: Coding agents have emerged as a promising paradigm for robot manipulation: a language model writes the robot controller as a program, and agents built

arXiv:2609.19330v1 Announce Type: new Abstract: Autonomous robots operating in partially observed environments must navigate safely while acquiring observations that improve future planning. Existing

arXiv:2609.19449v1 Announce Type: new Abstract: Robots must complete their tasks and maintain the achieved outcomes while avoiding safety failures at all times. Strict reach-avoid-stay (sRAS) formaliz

arXiv:2609.18128v1 Announce Type: new Abstract: Large language model (LLM) agents augmented by tools can automate complex, multi-step tasks, such as web navigation, code generation, and workflow orche

arXiv:2609.17247v1 Announce Type: new Abstract: An agentic AI driver-assistance framework that integrates perception, compliance reasoning, vehicle-state interpretation, and safety arbitration into a

arXiv:2609.17499v1 Announce Type: cross Abstract: Uncertainty estimation for Vision-Language-Navigation (VLN) models is a critical task since it can help identify ambiguous and unreliable predictions,

arXiv:2609.16454v1 Announce Type: new Abstract: Recent work by Doshi and Hauser (2024), Bisbee et al. (2024), and Xie et al. (2026) raises concerns that outputs from large language models (LLMs) tend

arXiv:2507.23136v2 Announce Type: replace Abstract: Many prediction tasks can admit multiple models that can perform almost equally well. This phenomenon can undermine interpretability and safety when

arXiv:2609.15134v1 Announce Type: new Abstract: Computer-use agents increasingly interact with browsers, terminals, file systems, and external services, introducing safety risks that emerge through ru

arXiv:2603.09460v2 Announce Type: replace Abstract: Efficiently learning safe and agile quadruped navigation in densely cluttered environments remains difficult: existing methods often lack safety and

arXiv:2609.14258v1 Announce Type: new Abstract: Vision-language models (VLMs) remain vulnerable to jailbreaks that distribute harmful intent across text and images, making unimodal safety mechanisms i

arXiv:2609.12831v1 Announce Type: cross Abstract: As the number of autonomous robots continues to grow, safety becomes increasingly important. Control barrier functions (CBFs) provide a theoretically

arXiv:2609.11697v1 Announce Type: new Abstract: Vision-Language-Action (VLA) and World-Action Models (WAMs) have demonstrated strong capabilities in general-purpose robotic manipulation, yet their gen

arXiv:2609.08256v1 Announce Type: cross Abstract: Automated red-team attacks and blue-team defenses for large language models (LLMs) are advancing quickly. However, attackers and defenders are built a

arXiv:2609.06036v1 Announce Type: new Abstract: Proposal-based controllers---learned policies, language-model planners, and other black-box generators---are increasingly deployed behind runtime verifi

arXiv:2609.05749v1 Announce Type: new Abstract: Work on the risks of artificial intelligence has focused predominantly on capability risk: the danger that systems become too powerful, too autonomous,

arXiv:2508.11854v3 Announce Type: replace Abstract: As 3D Gaussian Splatting (3DGS) gains rapid adoption in safety-critical tasks for efficient novel-view synthesis from static images, how might an ad

arXiv:2609.05525v1 Announce Type: new Abstract: Large vision-language models (VLMs) are increasingly deployed in safety-critical settings, yet existing visual jailbreak research has focused almost exc

arXiv:2609.06009v1 Announce Type: new Abstract: Multi-fingered dexterous manipulation remains a frontier for real-world reinforcement learning (RL) due to the high-dimensional action space and the pro

arXiv:2609.08375v1 Announce Type: new Abstract: Industrial process monitoring is fundamental to the safety and economic performance of modern process plants. Current practice remains a one-task-one-mo

arXiv:2603.06356v2 Announce Type: replace Abstract: Cooperative transport and manipulation of heavy or bulky payloads by multiple manipulators requires coordinated formation tracking, while simultaneo

arXiv:2609.04807v1 Announce Type: cross Abstract: Multi-vehicle cooperative autonomous driving enhances the safety and reliability of autonomous driving systems through information sharing among conne

arXiv:2608.30234v1 Announce Type: new Abstract: Automatic medical coding assigns ICD codes to clinical notes, but it remains challenging due to long documents, imbalanced label distributions, and dive

arXiv:2608.30065v1 Announce Type: cross Abstract: Large language models (LLMs) demonstrate strong Helpfulness, Harmlessness, and Honesty (3H) alignment in English-centric settings, but these gains tra

arXiv:2608.29772v1 Announce Type: new Abstract: Learning-based autonomous driving (AD) systems can perform reliably in familiar conditions, yet rare distribution shifts and long-tail events remain a m

arXiv:2608.28305v1 Announce Type: new Abstract: Industrial panel operation is knowledge-intensive and safety-critical. Beyond control recognition and action generation, execution must satisfy constrai

arXiv:2608.26669v1 Announce Type: cross Abstract: Testing of commercial Advanced Driver Assistance Systems is essential to ensure safety and compliance during type approval and in service operation. H

arXiv:2410.03086v2 Announce Type: replace Abstract: Robot-assisted ultrasound scanning promises to advance autonomous and accessible medical imaging. However, ensuring patient safety and compliant hum

arXiv:2603.14603v2 Announce Type: replace Abstract: In safety-critical Cyber-Physical Systems (CPS), trajectory prediction guides downstream planning and control. Deep learning models forecast well on

arXiv:2601.05882v2 Announce Type: replace-cross Abstract: Preference tuning aligns base language models to human judgments of quality, helpfulness, or safety by optimizing over explicit preference sig

arXiv:2608.22284v1 Announce Type: cross Abstract: Deep Reinforcement Learning (DRL) has achieved significant success in complex decision-making problems. As DRL systems are increasingly deployed in re

arXiv:2608.21387v1 Announce Type: new Abstract: Service robots are increasingly deployed in elderly-care facilities to alleviate caregiver workload and enhance the quality of daily care. However, most

arXiv:2608.21175v1 Announce Type: cross Abstract: Safe and efficient shape-aware navigation in heterogeneous crowds and robot fleets remains challenging. Traditional approaches often assume homogeneou

arXiv:2608.20378v1 Announce Type: new Abstract: Safety alignment in Large Language Models (LLMs) is often superficial, relying on refusal mechanisms that trigger only at the final stages of generation

arXiv:2608.20218v1 Announce Type: new Abstract: Electronic Navigational Charts (ENCs) are geospatial vector datasets used in maritime navigation systems that represent hydrographic and navigational in

arXiv:2608.18086v1 Announce Type: new Abstract: The growth of open-weight foundation models (OWFMs) has prompted the AI community to re-evaluate strategies for effective downstream governance. Althoug

arXiv:2608.17067v1 Announce Type: new Abstract: As text-to-image generative models advance, they raise critical safety concerns, particularly the generation of Not-Safe-For-Work (NSFW) content such as

arXiv:2608.17726v1 Announce Type: new Abstract: Bridge structures are regularly inspected for structural damage such as cracks and corrosion in order to ensure public safety and reduce maintenance cos

arXiv:2608.15375v1 Announce Type: cross Abstract: This paper develops Admissibility-Preserving Control (APC), a realization-centered safety-critical control framework for strict-feedback systems subje

arXiv:2507.19894v2 Announce Type: replace Abstract: With the rapid advancement of generative models, privacy, copyright, safety, and reliability risks have attracted growing attention. To mitigate the

arXiv:2608.14565v1 Announce Type: new Abstract: AI safety research has mainly focused on two areas: technical alignment (ensuring AI systems produce human-aligned outputs) and the regulation of genera