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Meta describes sealed liquid-cooling systems for dense AI servers and reports a pilot in which reinforcement learning reduced air-cooling fan energy by 20% and water use by 4%.
City Desk ABQ reports that Albuquerque Public Schools will delete Amira AI assessment recordings within 48 hours after parents raised privacy and consent concerns about recording children’s voices.
Anthropic is previewing a model-agnostic standard that lets AI agents coordinate programmable laboratory and manufacturing devices through a shared interface. Early partner demonstrations included automated protein assays, qPCR monitoring, instrument handoffs and dose-response experiments, but the system remains a…
GitHub says Copilot code review can now fully review pull requests opened by Copilot cloud agent, automatically review eligible bot-authored pull requests, and handle pull requests beyond its former 300-file or 20,000-line limit.
A security researcher reports that a crafted website and archive caused Claude Code Opus 5 in Auto Mode to execute attacker-controlled code in lab tests. The reported success rates reached 60% to 80% in small five-run samples, while Anthropic’s separate fixed evaluation reported zero successful attacks.
Fortune reports that Hong Kong startup Votee AI is retraining open-weight models on Cantonese data for banks, universities and government departments, aiming to address gaps in mainstream AI systems’ local and cultural knowledge.
A new arXiv preprint introduces GRAPE, a two-stage Bayesian-optimization framework that its authors say reduces the number of queries needed for high-dimensional black-box tasks, including adversarial attacks and large language model prompt optimization.
A new arXiv preprint proposes MoPLEx, an algorithm for learning from multi-way rankings when annotators have different underlying preferences. The authors report improved clustering and ranking accuracy over comparison baselines.
An arXiv preprint by Fan Yang and Matt Thomson proposes translating spatiotemporal field data into physical quantities before giving it to a large language model. On simulated-field benchmarks, the authors say this interpretation stage nearly tripled recovered-equation accuracy versus showing the model raw data…
An arXiv preprint surveys transform methods used before quantizing large language models and identifies a conflict between classical transform coding and grouped, shared-scale quantization. It argues that the best transform depends jointly on the deployment format and how scale information is allocated.
A new empirical study reports that attention architecture strongly affects how large language model inference energy grows with context length. It finds steeper growth for multi-head attention, flatter growth for grouped-query attention, and nearly constant energy for grouped-query attention combined with…
A new arXiv paper describes an AI system that combines an autoregressive transformer with a Finite Scalar Quantization tokenizer to suppress structured radio-frequency interference. The authors report lower latency and stronger interference rejection than traditional and earlier AI-based approaches, but provide no…
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