Knowledge catalogue
arXiv:2604.10598v1 Announce Type: new Abstract: Human-in-the-loop (HITL) UAV operation is essential in complex and safety-critical aerial surveying environments, where human operators provide navigati
arXiv:2603.12277v3 Announce Type: replace-cross Abstract: Language models remain vulnerable to prompt injection attacks despite extensive safety training. We trace this failure to role confusion: mode
arXiv:2604.10933v1 Announce Type: cross Abstract: Deep neural networks remain highly vulnerable to adversarial perturbations, limiting their reliability in security- and safety-critical applications.
arXiv:2508.11290v3 Announce Type: replace Abstract: LLMs increasingly exhibit over-refusal behavior, where safety mechanisms cause models to reject benign instructions that seemingly resemble harmful
arXiv:2604.08846v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) have been shown to be vulnerable to malicious queries that can elicit unsafe responses. Recent work uses prom
arXiv:2510.00938v2 Announce Type: replace Abstract: Large reasoning models (LRMs) 'think' by generating structured chain-of-thought (CoT) before producing a final answer, yet they still lack the abili
arXiv:2604.07039v1 Announce Type: cross Abstract: Robotic systems lack a principled abstraction for organizing intelligence, capabilities, and execution in a unified manner. Existing approaches either
arXiv:2604.07457v1 Announce Type: new Abstract: While decoupled control schemes for legged mobile manipulators have shown robustness, learning holistic whole-body control policies for tracking global
arXiv:2604.07655v1 Announce Type: cross Abstract: Hard-gated safety checkers often over-refuse and misalign with a vendor's model spec; prevailing taxonomies also neglect robustness and honesty, yield
arXiv:2604.06571v1 Announce Type: cross Abstract: Missing-person and child-safety investigations rely on heterogeneous case documents, including structured forms, bulletin-style posters, and narrative
arXiv:2604.06502v1 Announce Type: new Abstract: Vision-Language Models (VLMs) face significant safety vulnerabilities from malicious prompt attacks due to weakened alignment during visual integration.
Enterprises are rapidly moving AI workloads from experimentation to production on Google Kubernetes Engine (GKE), using its scalability to serve powerful inference endpoints. However, as these models
* **What it likely covers:** This entry likely discusses contemporary concerns regarding the safety and restriction of freedom of speech or civil liberties, drawing a parallel between past discus...
arXiv:2608.11483v1 Announce Type: new Abstract: Hit-to-lead optimization requires iterative design of hit analogs across competing potency, selectivity, physicochemical, pharmacokinetic, safety, and s
arXiv:2608.11244v1 Announce Type: new Abstract: Construction standards are critical for building safety and sustainability. Existing standard application workflows rely on keyword-based document retri
arXiv:2608.12166v1 Announce Type: cross Abstract: Algorithm registers have been championed as a means of providing transparency on the use of algorithms in public services. Yet potential publics diffe
arXiv:2608.11641v1 Announce Type: cross Abstract: Energy feasibility under wind uncertainty is a critical safety issue for low-altitude air-ground delivery. In truck-UAV systems, UAVs complete assigne
arXiv:2603.07499v3 Announce Type: replace-cross Abstract: Accurate estimation of the vehicle's sideslip angle and tire forces is essential for enhancing safety and handling performances in unknown dri
arXiv:2608.11592v1 Announce Type: new Abstract: Closed-track testing plays a fundamental role in the verification and validation of automated driving systems (ADS), particularly for safety-critical sc
arXiv:2608.10256v1 Announce Type: new Abstract: Accurate vessel trajectory prediction is critical for maritime safety and anomaly detection, yet existing models often struggle with geographic bias and
arXiv:2607.14945v2 Announce Type: replace Abstract: State-of-the-art flow based text-to-image (T2I) models exhibit remarkable generative abilities but remain vulnerable to producing unsafe content. Pr
arXiv:2608.10289v1 Announce Type: new Abstract: Deep neural networks are increasingly deployed in safety-critical domains as perception modules, where failures are often caused due to rare and under-r
arXiv:2608.08887v1 Announce Type: new Abstract: Rapid urbanization has increased the need for surveillance systems that can monitor multiple public safety risks at the same time. Traditional systems o
arXiv:2608.07620v1 Announce Type: new Abstract: Recent advances in flow matching models have substantially improved the quality of text-to-image generation, but have also raised increasing safety conc
arXiv:2608.08301v1 Announce Type: new Abstract: Reliable identification of railway wheel defects is important for safety and maintenance. This study develops a machine-learning-based diagnostic framew
arXiv:2505.19766v4 Announce Type: replace Abstract: Large language models (LLMs) remain vulnerable to misalignment and jailbreaks, making external safeguards like moderation filters essential, yet exi
arXiv:2508.16406v3 Announce Type: replace-cross Abstract: Large Language Models (LLMs) remain vulnerable to jailbreak attacks, which attempt to elicit harmful responses from LLMs. The evolving nature
arXiv:2608.07057v1 Announce Type: new Abstract: Knife-enabled violence presents a major public safety challenge, and law enforcement agencies require scalable tools for catalogue-level knife identific
arXiv:2608.07139v1 Announce Type: new Abstract: Uncertainty quantification is essential when deploying machine learning models in safety-critical applications. Online conformal prediction (OCP) provid
arXiv:2512.08991v3 Announce Type: replace Abstract: End-to-end image controllers that map raw camera frames directly to control actions are increasingly deployed in safety-critical systems. However, f
arXiv:2608.05313v1 Announce Type: cross Abstract: Service robots operate in household environments shared with humans, pets, and everyday objects, where they are highly susceptible to failures such as
arXiv:2510.14538v3 Announce Type: replace Abstract: Neuro-symbolic (NeSy) AI aims to develop deep neural networks whose predictions comply with prior knowledge encoding, e.g. safety or structural cons
arXiv:2510.22021v3 Announce Type: replace Abstract: Safety-critical applications of machine learning require uncertainty estimates that support reliable worst-case analysis. Neural networks (NNs) prov
arXiv:2608.04065v1 Announce Type: cross Abstract: Vehicle-to-everything (V2X) systems increasingly incorporate large language models (LLMs) for semantic tasks such as message summarization, operator a
arXiv:2608.04398v1 Announce Type: cross Abstract: Robotic planning often involves multiple objectives with complex priority relationships, such as safety, efficiency, and regulatory compliance. Rulebo
arXiv:2607.16868v2 Announce Type: replace Abstract: Large Language Models often produce confidently stated yet unreliable outputs, posing critical challenges for deployment in safety-sensitive applica
arXiv:2608.04035v1 Announce Type: cross Abstract: The wide adoption of Vision Transformers (ViTs) in safety-critical applications raises reliability concerns related to hardware faults. Algorithm-Base
arXiv:2608.04412v1 Announce Type: new Abstract: High-quality driving data are essential for autonomous-driving systems and generative world models. However, rare and safety-critical scenarios involvin
arXiv:2608.03101v1 Announce Type: new Abstract: Near-duplicate image matching is crucial for trust and safety, provenance verification, copyright enforcement, and large-scale visual search. Modern pla
arXiv:2608.02820v1 Announce Type: cross Abstract: Chain-of-thought (CoT) monitoring is an increasingly important component of AI safety stacks but relies on the assumption that a model's reasoning tra
Just had to create an 'accidental-cyberattacks' tag on my blog We're up to four now: the original OpenAI+Hugging Face one, Anthropic's me-too attacks, then two new ones from the UK AI Safety Institute
arXiv:2608.00151v2 Announce Type: replace-cross Abstract: Current evaluation frameworks for artificial intelligence focus mainly on capability, safety, and proxies such as adoption, engagement, effici
arXiv:2603.18792v2 Announce Type: replace Abstract: Uncertainty quantification (UQ) is crucial in safety-critical applications such as medical image segmentation. Total uncertainty is typically decomp
Some people are surprised that APIs (aka what Anthropic, OpenAI, and others provide) are treated differently than open weights in the new AI model framework. I'm not surprised at all, and it's actuall
arXiv:2608.02811v1 Announce Type: new Abstract: Robotic systems must operate under uncertainty while satisfying complex task and safety specifications. Monitoring such specifications under uncertainty
Cloudflare Wallets will provide AI agents with native payments and verifiable identity on the web. Using the x402 protocol, agents can autonomously purchase APIs and content within clear safety guardr
arXiv:2606.20389v2 Announce Type: replace Abstract: Continuum robots offer strong potential for manipulation tasks due to their high degrees of freedom, compliant structures, and operational safety. H
arXiv:2606.15240v2 Announce Type: replace Abstract: Accurate vessel trajectory forecasting is essential for maritime situational awareness, navigation safety, traffic management, and autonomous naviga
arXiv:2608.00973v1 Announce Type: new Abstract: Text-to-image (T2I) systems typically have prompt-level safety filters before the generator to block unsafe requests, yet such systems remain vulnerable
arXiv:2607.18295v2 Announce Type: replace Abstract: We study whether alignment schemes that reshape a base model's output distribution, combined with bounded safety filters, can drive the probability
arXiv:2608.00687v1 Announce Type: new Abstract: LiDAR point clouds provide explicit, deterministic physical boundaries critical for collaborative safety-critical perception. However, wireless channels
arXiv:2602.21429v3 Announce Type: replace Abstract: Flow-based generative models, such as diffusion models and flow matching models, have achieved remarkable success in learning complex data distribut
arXiv:2608.02188v1 Announce Type: new Abstract: Fine-grained understanding of surgical activity is essential for context-aware assistance in the operating room, including safety monitoring, adverse ev
Today, we’re launching Alpamayo 2 Super, our frontier open reasoning model for autonomous vehicles. Beyond seeing, Alpamayo understands and reasons through the complex world - thinks before it acts. I
arXiv:2607.28677v1 Announce Type: new Abstract: LLM now pass medical licensing examinations and, in curated cases, can rival physicians at diagnostic reasoning. These developments have accelerated the
arXiv:2607.29569v1 Announce Type: new Abstract: This article presents a modular inference framework that integrates Flow Matching generative models with formal Control Barrier Function (CBF) safety gu
arXiv:2607.28665v1 Announce Type: cross Abstract: Automated driving systems (ADSs) are becoming ubiquitous. Future Software Defined Vehicles (SDVs) may be able to run multiple ADSs, both native and af
arXiv:2607.29567v1 Announce Type: new Abstract: Manipulating transparent laboratory glassware that contains liquid is inherently safety-critical: even small geometric errors can cause unstable grasps
arXiv:2607.27418v1 Announce Type: new Abstract: Long-term ship trajectory prediction is a fundamental capability for maritime safety and autonomous navigation. While recent Transformer-based architect
arXiv:2607.28390v1 Announce Type: new Abstract: Constrained Markov Decision Processes (CMDPs) provide a natural framework for reinforcement learning in safety-critical applications, where agents maxim