Knowledge catalogue
A car pulls up to the curb. The app says, “Your ride is here.” No one’s in the driver’s seat. For people who live in one of the dozens of cities now hosting robotaxi services, this is already a realit
This article discusses Claude Fable 5, likely exploring Anthropic's latest version of their AI model and examining new fables or narratives related to AI safety concepts. The piece probably analyzes h
arXiv:2606.07929v1 Announce Type: new Abstract: Large language models (LLMs) are entering clinical practice based on benchmark accuracy that may fail to detect safety-relevant failure modes. Here we p
Anthropic: Anthropic says Claude Fable 5 uses conservative safety classifiers that trigger a fallback to Claude Opus 4.8 in <5% of sessions, in areas like cybersecurity — Today we're launching Claude
arXiv:2605.25806v2 Announce Type: replace Abstract: Women's safety and security are paramount for a modern society. Crimes against women occur in daylight as well as in low-light conditions. Often, su
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:2606.00813v1 Announce Type: cross Abstract: Safety alignment in LLMs does not improve monotonically across model generations. Studying four generations of Google's Gemma family (7B-31B) with qua
arXiv:2606.02444v1 Announce Type: new Abstract: Recent evidence shows that people with eating disorders (EDs) are increasingly seeking guidance, advice, and emotional support from Large Language Model
arXiv:2512.10234v2 Announce Type: replace-cross Abstract: Even LLMs that appear safe during evaluation can still produce harmful responses in deployment. Because stochastic sampling yields different r
arXiv:2606.01970v1 Announce Type: new Abstract: Reliable autonomous UAV swarms in Search and Rescue (SAR) missions require fault-tolerant coordination capable of sustaining operations despite agent de
arXiv:2605.29224v1 Announce Type: cross Abstract: AI agents augment large language models with external tools such as web retrieval, enabling grounded and up-to-date responses. However, incorporating
arXiv:2603.08761v2 Announce Type: replace-cross Abstract: We argue that formal certification of AI alignment over open-ended or unbounded input domains is impossible under standard assumptions in comp
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:2605.22373v1 Announce Type: cross Abstract: Safety classifiers are essential safeguards within generative AI systems, filtering harmful content or identifying at-risk users when interacting with
arXiv:2605.16894v1 Announce Type: new Abstract: Reinforcement Learning (RL) uses rewards to guide learning, yet reward design is typically hand-crafted using heuristics that can be difficult to tune.
arXiv:2605.16892v1 Announce Type: cross Abstract: Comprehensive situational awareness is essential for autonomous vehicles operating in safety-critical environments, as it enables the identification a
arXiv:2605.14271v1 Announce Type: new Abstract: LLM agents increasingly run inside execution harnesses that dispatch tools, allocate resources, and route messages between specialized components. Howev
arXiv:2605.12729v1 Announce Type: cross Abstract: Large language models are increasingly being used to support network operations (NetOps) and artificial intelligence for IT operations (AIOps), includ
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:2605.12015v1 Announce Type: cross Abstract: Reusable skills are becoming a common interface for extending large language model agents, packaging procedural guidance with access to files, tools,
arXiv:2605.01415v1 Announce Type: new Abstract: Recent AI systems compress the distance between capability growth and capability deployment. Earlier high-risk technologies were slowed by capital inten
arXiv:2605.03441v1 Announce Type: cross Abstract: Large language models (LLMs) employ safety mechanisms to prevent harmful outputs, yet these defenses primarily rely on semantic pattern matching. We s
arXiv:2604.26857v1 Announce Type: new Abstract: Deploying accurate object detection for Vulnerable Road User (VRU) safety on edge hardware requires balancing model capacity against computational const
arXiv:2510.20956v2 Announce Type: replace-cross Abstract: We discover a novel and surprising phenomenon of unintentional misalignment in reasoning language models (RLMs), which we call self-jailbreaki
arXiv:2604.25102v1 Announce Type: new Abstract: Typographic prompt injection exploits vision language models' (VLMs) ability to read text rendered in images, posing a growing threat as VLMs power auto
arXiv:2604.22755v1 Announce Type: cross Abstract: Reliable decision support in nuclear engineering requires traceable, domain-grounded knowledge retrieval, yet safety and risk analysis workflows remai
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:2604.22156v1 Announce Type: cross Abstract: Purpose: Accurate assessment of the Critical View of Safety (CVS) during laparoscopic cholecystectomy is essential to prevent bile duct injury, a comp
arXiv:2604.20945v1 Announce Type: cross Abstract: Effective safety auditing of large language models (LLMs) demands tools that go beyond black-box probing and systematically uncover vulnerabilities ro
arXiv:2604.18658v1 Announce Type: cross Abstract: Existing AI agent safety benchmarks focus on generic criminal harm (cybercrime, harassment, weapon synthesis), leaving a systematic blind spot for a d
arXiv:2604.17046v1 Announce Type: new Abstract: Collisions between cyclists and pedestrians at urban intersections remain a persistent source of injuries, yet few systems attempt real-time warnings to
arXiv:2604.18463v1 Announce Type: cross Abstract: Large language models are increasingly used as planners for robotic systems, yet how safely they plan remains an open question. To evaluate safe plann
arXiv:2505.10846v3 Announce Type: replace Abstract: This paper presents AutoRAN, the first framework to automate the hijacking of internal safety reasoning in large reasoning models (LRMs). At its cor
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:2604.12447v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models inherit rich world knowledge from vision-language backbones and acquire executable skills via action demonstrations.
arXiv:2604.11806v1 Announce Type: new Abstract: To identify safety violations, auditors often search over large sets of agent traces. This search is difficult because failures are often rare, complex,
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: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:2604.08867v1 Announce Type: cross Abstract: Audio has rapidly become a primary interface for foundation models, powering real-time voice assistants. Ensuring safety in audio systems is inherentl
arXiv:2604.08987v1 Announce Type: new Abstract: As Large Language Models (LLMs) advance toward embodied AI agents operating in physical environments, a fundamental question emerges: can models trained
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:2607.04645v1 Announce Type: cross Abstract: Safety alignment in large language models is typically evaluated against direct, imperative harmful requests. We show that this alignment is highly co
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:2606.31644v1 Announce Type: new Abstract: As large language models take on morally consequential roles in healthcare, legal, and hiring contexts, we need to examine whether their ethical behavio
arXiv:2608.09653v1 Announce Type: cross Abstract: Recent advances in learning-based control have enabled impressive achievements in solving complex control problems in various domains. However, since
arXiv:2501.15790v2 Announce Type: replace Abstract: Synthetic minority oversampling is typically designed and evaluated against a predictive objective, generating samples that improve downstream class
arXiv:2608.06477v1 Announce Type: cross Abstract: Computer-use agents (CUAs) face a growing threat from indirect prompt injection, where adversarial instructions are planted in the environment such as
arXiv:2608.01388v1 Announce Type: cross Abstract: Runtime safety monitors based on Linear Temporal Logic (LTL) and finite automata (FSA) are increasingly deployed to intercept unsafe tool-call sequenc
arXiv:2607.28623v1 Announce Type: new Abstract: We present PAC-MAN, a perception-aware CBF-RL framework that couples control-barrier safety with deployment-realistic onboard sensing for whole-body hum
arXiv:2607.27849v1 Announce Type: cross Abstract: An open-weight LLM can write composition setpoints every five minutes. What a plant still needs is a hard check: named constraints, logged margins, an
Google announced Gemini Robotics 2.0 on July 30 2026, launching a family of three models that enhance robot dexterity, safety, and whole‑body intelligence for humanoid machinery. The publicly released
arXiv:2607.22692v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used for emotional support despite lacking mechanisms to safely govern evolving mental health risk. Existi
Another big reason to use combination of frontier models. Chain-of-thought monitoring is treated as a reliable safety layer for agents. This DeepMind-affiliated study shows the layer can be argued out
arXiv:2607.07184v1 Announce Type: cross Abstract: Pre-deployment safety evaluations aim to inform the downstream risks of releasing a new AI model. Yet most evaluations provide limited evidence about
This is our first time telling the story of how we first built and launched Claude Code, starting with its origins in Anthropic safety research. So much more to do. We are 1% done. We've put together
arXiv:2606.28843v1 Announce Type: cross Abstract: Fine-tuning a large language model is a ubiquitous method for enhancing its capability on a specific downstream task. However, prior work has shown th
GPT‑5.6 Sol launches with our most robust safety stack yet. We strengthened real-time protections against high-risk cyber activity and repeated misuse, then spent weeks hardening the system with human
arXiv:2606.25782v1 Announce Type: new Abstract: With the widespread adoption of large language models (LLMs) in chatbots and everyday applications, companies increasingly need guardrails that are effe
arXiv:2606.18936v2 Announce Type: replace Abstract: Large language models (LLMs) are increasingly embedded in AI for Science (AI4Science) workflows, from scientific question answering and literature a
arXiv:2606.11949v1 Announce Type: new Abstract: We present an online monitoring system for distributional shift in deployed safety classifiers, using calibrated sequential statistics to detect when a