NVIDIA's ENPIRE Enables Self-Improving Real-World Robots; Tencent's ARGUS Traces 10,000-GPU Training Clusters; UC Berkeley Releases LOCUS Ordinance Corpus
NVIDIA researchers introduce ENPIRE, a framework that lets coding agents autonomously improve physical robot policies through automatic trial, evaluation, and reset. On dexterous tasks like push-T, pin insertion, and GPU installation, the best AI agents (GPT-5.5, Opus 4.7) reach 99% success, while multi-agent setups yield higher final scores. Tencent describes ARGUS, a low-overhead tracing system deployed on a 10,000+ GPU cluster for over six months to debug large-scale training jobs, including Hunyuan MoE and video models. UC Berkeley releases LOCUS, a corpus of 2.2 million rows of U.S. local ordinances harmonized for legal AI research. The issue also includes a cautionary essay on the historical failure of technology predictions and a long piece on AI-driven human disempowerment.