Search: “safety”

arXiv:2610.09703v1 Announce Type: cross Abstract: Token-Pruning accelerates Vision-Language Models by removing redundant visual tokens, yet its safety implications remain underexplored. In this work,

arXiv:2610.07723v1 Announce Type: cross Abstract: Safety alignment in Large Language Models (LLMs) remains vulnerable to backdoor attacks. Existing LLM backdoors are almost all input-centric: activati

arXiv:2609.39594v1 Announce Type: new Abstract: As robots are increasingly deployed in everyday environments, ensuring their safety has become a central challenge. Existing methods often encode safety

arXiv:2609.24098v1 Announce Type: new Abstract: Visual Question Answering (VQA) with Multimodal Large Language Models (MLLMs) requires not only producing safe and effective responses, but also groundi

arXiv:2609.14115v1 Announce Type: cross Abstract: Safety-critical control of autonomous systems requires formal safety certificates, such as controlled invariant sets, that must be computed online as

arXiv:2609.03760v1 Announce Type: new Abstract: Simulation is increasingly used to support safety-related decision-making in road transport, particularly for the assessment and approval of automated d

arXiv:2609.00445v1 Announce Type: cross Abstract: Deployed language model safeguards (safety fine-tuning, filtering, unlearning) vary by principal only outside the model weights: filters are reconfigu

arXiv:2608.25459v1 Announce Type: new Abstract: Industrial aerial robotics demands safety guarantees for navigation in unstructured environments while optimizing performance and computational efficien

arXiv:2608.00180v1 Announce Type: new Abstract: Conflicting objectives are general in RL alignment, and training on them data-efficiently is hard. Training a safety guard with RL means optimizing two

arXiv:2607.20505v1 Announce Type: cross Abstract: Surrogate safety measures (SSMs) enable proactive traffic safety assessment, but many existing methods evaluate pairwise interactions independently or

arXiv:2606.28995v1 Announce Type: new Abstract: Robots deployed in safety-critical environments must execute motions that are simultaneously robust to disturbances and provably safe from collisions. D

arXiv:2603.23565v2 Announce Type: replace-cross Abstract: Safe reinforcement learning (RL) is a standard paradigm for safety-critical decision making. However, real-world safety constraints can be com

arXiv:2604.24153v1 Announce Type: new Abstract: Current AI systems increasingly operate in contexts where their outputs directly trigger real-world actions. Most existing approaches to AI safety, risk

arXiv:2508.04436v2 Announce Type: replace Abstract: Autonomous highway driving involves high-speed safety risks due to limited reaction time, where rare but dangerous events may lead to severe consequ

arXiv:2610.07935v1 Announce Type: new Abstract: LLM agents are increasingly capable of executing complex tasks and of recursively improving themselves on easy-to-verify objectives such as software eng

arXiv:2610.02920v1 Announce Type: new Abstract: Agent harnesses play a critical role in defenses by enforcing safety constraints to prevent unsafe actions. However, rapidly emerging attacks outpace ma

arXiv:2609.38291v1 Announce Type: cross Abstract: Large language model (LLM) agents are vulnerable to safety risks such as injected malicious instructions or misleading information, motivating runtime

arXiv:2609.36942v1 Announce Type: cross Abstract: Learning neural-network models of dynamical systems with safety guarantees is a fundamental requirement for their deployment in safety-critical settin

arXiv:2608.27340v1 Announce Type: new Abstract: Steering interventions targeting eval-awareness, a model's recognition that it is being tested, are increasingly used in safety evaluation pipelines, wh

arXiv:2509.23960v2 Announce Type: replace-cross Abstract: Co-optimizing safety and performance in large-scale multi-agent systems remains a fundamental challenge. Existing approaches based on multi-ag

arXiv:2606.30788v1 Announce Type: cross Abstract: Language models are often adapted in stages: a public skill phase, a private memory phase, and a later safety phase that learns to refuse outputs tied

arXiv:2606.06996v1 Announce Type: new Abstract: This paper studies runtime safety for autonomous driving when high-level driving commands become faulty or unreliable. Unlike conventional runtime-safet

arXiv:2602.10032v2 Announce Type: replace Abstract: Agents in cyber-physical systems are increasingly entrusted with safety-critical tasks. Ensuring safety of these agents often requires localizing th

arXiv:2509.12833v2 Announce Type: replace Abstract: Projection-based safety filters, which modify unsafe actions by mapping them to the closest safe alternative, are widely used to enforce safety cons

arXiv:2508.21322v2 Announce Type: replace Abstract: Achieving both safety guarantees and real-time performance in cooperative vehicle coordination remains a fundamental challenge, particularly in dyna

arXiv:2603.07427v3 Announce Type: replace Abstract: As Large Language Models (LLMs) evolve into autonomous agents, existing safety evaluations face a fundamental trade-off: manual benchmarks are costl

arXiv:2606.15420v2 Announce Type: replace-cross Abstract: Safety evaluations test a policy on prompts that omit the incentive information deployment supplies: a commission, a performance score, a dash

arXiv:2609.38253v1 Announce Type: cross Abstract: Text-to-image (T2I) models have substantially improved in language understanding, in-image text rendering, and visual composition, while their safety

arXiv:2606.15517v2 Announce Type: replace Abstract: Large language models often struggle with sensitive prompts. They may refuse outright, provide generic safety boilerplate, or fail to address the us

arXiv:2609.00498v1 Announce Type: new Abstract: Jailbreak robustness has become central to large language model (LLM) safety evaluation, yet prevailing methodologies rely primarily on refusal behavior

arXiv:2608.30319v1 Announce Type: cross Abstract: Large language models (LLMs) finetuned for specialized domains represent crucial high-impact applications. Inference-time alignment improves safety de

arXiv:2608.26860v1 Announce Type: cross Abstract: Connected and automated vehicle (CAV) platooning offers a promising approach to improving road safety and traffic capacity. However, platoon control i

arXiv:2608.20467v1 Announce Type: cross Abstract: Safety filters are an effective tool for enforcing constraints in safety-critical systems, but most existing methods assume perfect state information,

arXiv:2608.19802v1 Announce Type: new Abstract: LLM evaluation pipelines often have many candidate judges: general LLM-as-a-judge prompts, reward models, safety classifiers, confidence variants, and t

We will continue to offer Zero Data Retention for frontier models. As AI takes on longer, more autonomous work and delivers greater value to businesses, safety systems also need to identify risks acro

arXiv:2608.14481v1 Announce Type: cross Abstract: As heterogeneous robotic systems deploy across diverse urban zones, maintaining safety amid complex human-robot interactions remains a critical challe

arXiv:2603.06954v2 Announce Type: replace Abstract: This tutorial provides a critical review of the practical application of Control Barrier Functions (CBFs) in robotic safety. While the theoretical f

arXiv:2603.15136v2 Announce Type: replace-cross Abstract: Offline safe reinforcement learning (RL) seeks reward-maximizing policies from static datasets under strict safety constraints. Existing metho

arXiv:2606.31748v1 Announce Type: new Abstract: Safety training on language models often induces over-refusal: improved safety on harmful prompts at the cost of increased refusal on harmless ones. Tho

arXiv:2606.30935v1 Announce Type: cross Abstract: While neural network control policies are powerful, their deployment on safety critical systems depends on ensuring that they obey strict constraints.

arXiv:2501.15373v2 Announce Type: replace-cross Abstract: Merely pursuing performance may adversely affect safety, while a conservative policy for safe exploration will degrade the performance. How to

arXiv:2605.24550v1 Announce Type: new Abstract: Fine-tuning-as-a-Service (FaaS) enables personalization of large language models (LLMs), but it can weaken safety-alignment under harmful fine-tuning at

arXiv:2509.22292v2 Announce Type: replace-cross Abstract: Along with the rapid advancement of numerous Text-to-Video (T2V) models, growing concerns have emerged regarding their safety risks. While rec

arXiv:2605.00236v1 Announce Type: cross Abstract: Safety-aligned large language models rely on RLHF and instruction tuning to refuse harmful requests, yet the internal mechanisms implementing safety b

arXiv:2604.25379v1 Announce Type: new Abstract: Ensuring safety during reinforcement learning (RL) training is critical in real-world applications where unsafe exploration can lead to devastating outc

arXiv:2604.23039v1 Announce Type: new Abstract: Physical human-robot interaction offers the potential to leverage human intelligence and robot physical capabilities to enable a range of exciting appli