Knowledge catalogue
arXiv:2607.01022v1 Announce Type: new Abstract: Spatiotemporal point processes (STPPs) model event data in continuous time and space, with applications in mobility, epidemiology, and public safety. Re
arXiv:2606.28757v1 Announce Type: new Abstract: Generative world models hold immense promise as scalable simulators for autonomous systems, particularly for synthesizing rare but safety-critical multi
Agree is a TypeScript library by Boris Cherny that provides a schema validation and serialization system, enabling developers to define data schemas with type safety and validate data at runtime. The
arXiv:2606.29441v1 Announce Type: cross Abstract: Inference-time safety methods for large language models have proliferated, yet no systematic comparison exists. We evaluate five defense paradigms (no
arXiv:2606.28992v1 Announce Type: cross Abstract: General-purpose large language models (LLMs) have demonstrated strong abilities in opendomain question answering, information extraction, and text gen
arXiv:2511.05879v5 Announce Type: replace-cross Abstract: Hydrogen crossover is a critical safety and efficiency constraint in high-pressure polymer electrolyte membrane water electrolysis (PEMWE), bu
arXiv:2606.28556v1 Announce Type: new Abstract: Recent advances in large language models and vision-language models have enabled reasoning over multimodal data, offering opportunities for clinical app
arXiv:2606.29721v1 Announce Type: cross Abstract: Maritime anomaly detection is essential for ensuring maritime safety, security, and efficient traffic management at sea, with Automatic Identification
arXiv:2606.29196v1 Announce Type: cross Abstract: Do language models know when they are being tested? This question matters for AI safety: a model that recognises an evaluation context could alter its
arXiv:2606.29623v1 Announce Type: new Abstract: Rare events govern the safety profile of modern AI systems, yet their probabilities are extremely difficult to estimate: direct Monte Carlo requires pro
arXiv:2606.28804v1 Announce Type: new Abstract: Embodied World Models (EWMs) have emerged as a scalable and risk-free paradigm for advancing embodied intelligence, enabling the safety-critical evaluat
arXiv:2606.27624v1 Announce Type: cross Abstract: Using robots to estimate the location of the radiation source is an effective way to improve efficiency and safety. Existing methods focus on planning
arXiv:2602.10179v2 Announce Type: replace-cross Abstract: Recent advances in large image editing models have shifted the paradigm from text-driven instructions to vision-prompt editing, where user int
arXiv:2606.26649v1 Announce Type: new Abstract: Agent safety in high-stakes domains requires formal policy enforcement, but most existing approaches either rely on probabilistic guardrails (fine-tuned
arXiv:2601.01701v2 Announce Type: replace-cross Abstract: Anomaly detection is increasingly becoming crucial for maintaining the safety, reliability, and efficiency of industrial systems. Recently, wi
arXiv:2606.26265v1 Announce Type: new Abstract: Robot autonomous navigation that accounts for surrounding human activities is crucial for ensuring both safety and natural human-robot interaction in re
arXiv:2606.26317v1 Announce Type: cross Abstract: Accurate fault diagnosis of rolling element bearings in rotating machinery is considered essential for ensuring industrial safety and enabling predict
arXiv:2606.25476v1 Announce Type: new Abstract: Large language models (LLMs) have demonstrated remarkable performance across natural language processing tasks, yet their deployment in high-stakes appl
arXiv:2606.25652v1 Announce Type: new Abstract: Reliable 3D perception of vulnerable road users (VRUs) such as cyclists and pedestrians is essential for their safety in urban traffic and a core requir
arXiv:2510.04773v2 Announce Type: replace Abstract: As Large Language Models (LLMs) demonstrate remarkable capabilities learned from vast corpora, concerns regarding data privacy and safety are receiv
arXiv:2606.26071v1 Announce Type: new Abstract: A central goal of safety research is determining whether a model is misaligned. Prior work has largely focused on detecting concerning behavior. But beh
arXiv:2606.24377v1 Announce Type: new Abstract: Compliant joints can embed safety and adaptability into dexterous hands, but achieving large-stroke anthropomorphic motion while maintaining joint-speci
arXiv:2606.21821v1 Announce Type: new Abstract: Understanding the causal structure of a language model's thought process is a problem of significant importance for both transparency and safety. In thi
arXiv:2606.21527v1 Announce Type: cross Abstract: Robust obstacle segmentation is essential for the safety of intelligent robots, where LiDAR-based perception systems play a fundamental role in the ro
arXiv:2606.20752v1 Announce Type: new Abstract: Deep neural network-based LiDAR 3D object detection serves as a critical perception component in safety-critical autonomous systems. However, recent stu
arXiv:2606.12040v1 Announce Type: new Abstract: The design of reinforced concrete highway barriers is a safety-critical process that requires strict compliance with regulatory provisions such as the A
arXiv:2505.08784v2 Announce Type: replace-cross Abstract: As machine learning (ML) enters high-stakes domains, trustworthy uncertainty quantification (UQ) is essential for safety. In this paper we int
arXiv:2606.11507v1 Announce Type: new Abstract: Mining hard, safety-critical scenes from driving logs is bottlenecked by the absence of difficulty labels, and no single proxy, collision risk, trajecto
This article from Latent Space discusses Anthropic's Claude Fable 5 model, examining its capabilities in handling creative and mythological content while maintaining safety guardrails, along with cove
Jeremy Howard argues that Anthropic's leadership should recognize that ensuring AI safety and positive outcomes requires collaborative effort across the industry rather than positioning any single org
Gary Marcus critiques Anthropic's rapid shift in positioning Claude from a model deemed too dangerous for public release to one made widely available with safety measures, suggesting this represents i
arXiv:2606.09142v1 Announce Type: cross Abstract: Egocentric vision offers a first-person view of human perception and decision making, yet its potential for traffic-safety prediction remains underexp
arXiv:2606.07970v1 Announce Type: cross Abstract: Current open-weight large language models (LLMs) are prone to malicious finetuning attacks, which could compromise the safety alignment of LLMs with o
arXiv:2606.09109v1 Announce Type: new Abstract: Video retrieval at scale is central to data curation and safety validation in autonomous driving, where users want to find not only scenes but also dyna
arXiv:2606.09746v1 Announce Type: cross Abstract: With AI increasingly deployed in safety-critical systems, providing formal robustness guarantees for the underlying models is essential. Existing veri
arXiv:2606.08136v1 Announce Type: new Abstract: Model Predictive Control (MPC) is widely used for autonomous-vehicle (AV) motion planning, but its real-time applicability is often limited by the need
arXiv:2606.07620v1 Announce Type: cross Abstract: With the growth of Vision Transformers in safety-critical domains like autonomous systems and medical imaging, ensuring their reliability against soft
arXiv:2606.06133v1 Announce Type: cross Abstract: TLA+ is a formal specification language for verifying distributed systems and safety-critical protocols. Large language models (LLMs) frequently produ
arXiv:2606.05647v1 Announce Type: cross Abstract: AI coding agents are increasingly embedded in real-world software development, collaborating with human developers while gaining broader access to cod
arXiv:2601.21288v2 Announce Type: replace-cross Abstract: Autonomous driving is an important and safety-critical task, and recent advances in LLMs/VLMs have opened new possibilities for reasoning and
arXiv:2606.04072v1 Announce Type: cross Abstract: Deep learning models are increasingly central to autonomous vehicle (AV) pipelines, yet their integration has traditionally followed a monolithic desi
arXiv:2606.04310v1 Announce Type: new Abstract: Deep Neural Networks (DNNs) are increasingly being deployed in security-critical and safety-sensitive applications, which makes rigorous testing essenti
Lukas Petersson and Axel Backlund of Andon Labs discuss their work on evaluation methods and benchmarking for AI systems, likely covering approaches to assessing AI capabilities and safety in real-wor
arXiv:2606.04296v1 Announce Type: new Abstract: As autonomous AI agents move from conversational systems to long-horizon software execution, runtime safety layers that decide when to interrupt an agen
arXiv:2606.03695v1 Announce Type: new Abstract: As language models are increasingly deployed in real-world applications, the ability to erase specific knowledge from them becomes critical for safety a
Sam Altman argues that US leadership in artificial intelligence requires three concurrent priorities: advancing cutting-edge AI model development, ensuring these models incorporate robust safety measu
Tesla's Waymo robotaxi service has expanded to cover the entire Austin metropolitan area without human safety drivers. This expansion represents a significant milestone in autonomous vehicle deploymen
arXiv:2606.01883v1 Announce Type: cross Abstract: Open-set recognition (OSR) requires a classifier to reject inputs from unseen classes which is essential in safety-critical settings such as medical i
arXiv:2606.02282v1 Announce Type: new Abstract: Orchestrating Large Language Models into Multi-Agent Systems (LLM-MAS) has unlocked remarkable reasoning capabilities, yet emergent failures and halluci
arXiv:2606.00059v1 Announce Type: cross Abstract: Informative excitation signals are critical for accurate system identification of mechatronic systems, yet classical system identification (SI) approa
arXiv:2606.00341v1 Announce Type: cross Abstract: As AI agents are increasingly deployed in real personal and corporate settings (email accounts, development workflows, company databases, etc.), safet
arXiv:2507.02983v3 Announce Type: replace-cross Abstract: Large Language Models (LLMs) hold significant promise for transforming digital health by enabling automated medical question answering. Howeve
arXiv:2606.00448v1 Announce Type: cross Abstract: LLM agents increasingly rely on community-contributed skills that expand an agent's operational capability set. We study a core safety problem in agen
McDonald's paused advertising on 𝕏 (formerly Twitter) in late 2024, likely due to concerns about content moderation and brand safety on the platform following Elon Musk's ownership changes. This move
arXiv:2509.12440v3 Announce Type: replace-cross Abstract: Deploying Large Language Models (LLMs) in medical applications requires fact-checking capabilities to ensure patient safety and regulatory com
arXiv:2605.31204v1 Announce Type: new Abstract: Accurate weather forecasts are essential across various domains and are safety-critical in extreme weather conditions. Compared to simulation-based fore
arXiv:2605.30854v1 Announce Type: cross Abstract: Language models fine-tuned with reinforcement learning typically optimize for task reward, ignoring multi-agent strategic structure. Because these age
arXiv:2605.30837v1 Announce Type: cross Abstract: Prompt-injection detectors are heterogeneous: each is strong on a different slice of attacks, and none is always reliable. Yet existing systems still
arXiv:2605.29901v1 Announce Type: cross Abstract: Large language models (LLMs) can detect software vulnerabilities, but how do they actually identify vulnerable code? We address this question using me
arXiv:2605.29827v1 Announce Type: new Abstract: Medical image analysis models can exhibit performance disparities across patient subgroups, threatening clinical safety and fairness. Existing methods t