ZeroHour
DashboardNewsCVEsExploitsIncidentsAIBriefAnalyticsEntitiesWatchlistIOCsAsk the feedSourcesFeeds & APIKiosk mode

Search: “evaluation infrastructure”

80 items

Searchkeywordsmart Sortlatesthotimportancerelevance
DomainAllCyberAIWindow24h3d7d30dAllIn the wildImportant onlyShow duplicates
CategoryAllVulnerability 520Ransomware 288Exploit / PoC 265Policy & legal 204Data breach 184Industry 169Malware 149Threat actor 96AI safety & security 92AI research 80Phishing & fraud 68AI industry 36AI tools & infra 28Research 27
SeverityAllcritical 1190high 316medium 344low 162info 271

OpenWAM: An Open, Modular Exploration Towards Systematic World-Action Model Pretraining

OpenWAM releases an open modular stack for world-action model pretraining, plus OpenWAM-alpha trained on about 6,400 hours of egocentric and robot data.

OpenWAM is an open research stack that factorizes World-Action Model pretraining into composable infrastructure, study, and model components with unified training, inference, and evaluation. Controlled experiments distill three principles on knowledge inheritance, world-action synergy, and out-of-domain generalization gains from embodied co-training. The resulting OpenWAM-alpha, pretrained on roughly 6,400 hours of egocentric human and robot data, achieves top-tier results across eight simulation benchmarks and real-robot tests spanning single-arm, bimanual, and dexterous embodiments. The full stack, including infrastructure, evaluation protocols, pretrained models, and data recipes, is released openly.

Hugging Face daily papers · 10d agoAI research

Scalability Analysis of Distributed Kolmogorov-Arnold Network Training on High-Performance Computing Systems

An empirical study shows distributed Kolmogorov-Arnold Network training reaches 74.7% parallel efficiency at 8 A100 GPUs, with overheads driven by All-Reduce choices.

The study evaluates data-parallel Kolmogorov-Arnold Network (KAN) training on the FinisTerrae III supercomputer using up to 8 NVIDIA A100 GPUs across 4 nodes with PyTorch Distributed Data Parallel. Strong scaling yields 5.97x speedup and 74.7% parallel efficiency at 8 GPUs, comparable to conventional deep learning workloads, while communication overhead ranges from 1.3% to 6.1%, driven mainly by All-Reduce algorithm selection and inter-node latency rather than KAN's edge-wise gradient structure. Weak scaling shows an initial single-to-multi-GPU throughput drop followed by stability, and the parameter-to-memory ratio improves with model size even as training time scales unfavorably. The authors provide GPU topology and model-size deployment guidelines for KAN training.

arXiv cs.AI / cs.LG / cs.CL · 9d agoAI research1

Lessons from the hacks

The recent run of cyberattacks by in-development frontier models has got me thinking a lot about how our current incentive systems are not well suited for such fast technological transitions. The two primary power structures here are the rapidly growing technology companies and the federal government. The companies are incentivized to grow, so they can keep growing and keep scaling – in what is…

Interconnects · Aug 9, 2026AI research

Φ-Bench: Can Large Language Models Engineer the Infrastructure That Powers Them?

Researchers release Phi-Bench, a benchmark evaluating frontier LLMs on open-ended, long-horizon engineering and optimization of the LLM infrastructure stack.

Phi-Bench evaluates LLMs on open-ended engineering of the LLM infrastructure stack, derived from optimization problems studied in frontier research and grounded in real-world code repositories. Tasks range from localized kernel-level function completion to long-horizon implementation and end-to-end system optimization. Experiments on frontier LLMs reveal current capabilities and limitations on the path toward autonomous optimization of future AI infrastructure.

Hugging Face daily papers · 8d agoAI research1

Nuha-Speech: Building General-Purpose Arabic Speech-LLMs

Nuha-Speech initiative builds general-purpose Arabic speech-LLMs using a 1.5M-sample speech QA corpus and fine-tuned Qwen-Omni variants.

The paper introduces Nuha-Speech, an initiative covering dataset construction, model training, and evaluation for Arabic speech large language models. The authors built an Arabic Speech Question-Answering corpus of over 1.5 million training samples and used it for supervised fine-tuning of Qwen-Omni model variants at multiple scales. A tailored evaluation framework with diverse tasks and metrics is designed to assess Arabic speech capabilities under limited resource constraints.

arXiv cs.AI / cs.LG / cs.CL · 6d agoAI research1

Prepared Or Unprepared? Evaluating Healthcare Workforce Readiness for Clinical Adoption of Artificial Intelligence in Nigeria

Survey of 761 Nigerian healthcare professionals finds high AI awareness (92.6%) but limited knowledge, preparedness, and major training and infrastructure barriers.

A cross-sectional study of 761 healthcare professionals across Nigeria, conducted from December 2025 to March 2026, found 92.6% awareness of AI in healthcare but 40.9% reporting low knowledge and only 63.0% feeling adequately prepared. Top barriers were lack of training (84.7%), poor infrastructure (71.1%), and high tool costs (61.0%). Willingness to adopt was strong, with 92.5% interested in training and 78.7% supporting AI in undergraduate curricula; preparedness differed significantly across geopolitical zones and professions.

arXiv cs.AI / cs.LG / cs.CL · 20h agoAI research

ScienceIDE: Turning World's Scientific Codebase into Agent Learnable Environments

ScienceIDE turns scientific code repositories into agent-trainable environments and trains PhAI-IDE models at 72B, 9B, and 4B scales.

ScienceIDE is infrastructure that transforms scientific repositories into executable environments supporting task generation, execution, and scientific verification, guided by expert-defined cases and acceptance criteria. Using verified interaction trajectories, the authors train PhAI-IDE-72B, PhAI-IDE-9B, and PhAI-IDE-4B. The model family improves held-out scientific-code repair and selected general benchmarks in code, reasoning, and knowledge, indicating positive transfer. Code is released on GitHub.

arXiv cs.AI / cs.LG / cs.CL · 19h agoAI research 2 sources

ScienceIDE: Turning World's Scientific Codebase into Agent Learnable Environments

ScienceIDE converts scientific code repositories into verifiable agent training environments, producing the PhAI-IDE 4B-72B model family.

ScienceIDE turns scientific code repositories into executable environments supporting task generation, execution, and scientific verification, guided by expert-defined scientific cases and acceptance criteria. Using verified interaction trajectories, the authors train PhAI-IDE-72B, PhAI-IDE-9B, and PhAI-IDE-4B. The model family shows gains in held-out scientific-code repair and selected general-purpose code, reasoning, and knowledge benchmarks, evidencing positive transfer from scientific experience.

Hugging Face daily papersupdated · 19h agofirst · 1d agoAI research 2 sources