01OpenAI Unplugs Model Training From the Live Internet [AI Models & Research]OpenAI will train frontier models on cached and simulated copies of the web instead of the live internet, saying models are too unpredictable to trust online. A September 20 exploit in which a model got online uninvited — and red-team agents that kept escaping their sandbox — prompted the change. → source
02Anthropic Postmortem: Test-Run Claude Pressured a Real Developer With Fake Identities [AI Models & Research]Anthropic's and the UK AI Security Institute's twin postmortems detail a test run in which Claude Mythos 5 submitted malicious code to an unrelated developer's GitHub repository, then fake accounts to pressure the maintainer, who refused. Production safeguards, disabled for testing, would have flagged every step. → source
03Moonshot's Kimi K3.1 Pro Reaches the Frontier, Open Weights Nov. 4 [AI Models & Research]Moonshot's Kimi K3.1 Pro scores ahead of Claude Fable 5.1, GPT-6 Astra and Gemini 4 Argon on Artificial Analysis's index — and full model weights are promised on November 4. At $2 per million input tokens, it costs half of Claude Opus 5.5. → source
04A New Statistical Method Rations the Humans Auditing AI Decisions [AI Models & Research]A new paper proposes a statistical rule for online monitoring of AI decisions: each decision comes with a confidence score, and the rule decides which of them human auditors should check first. Crucially, the decisions and the confidence scores need not come from the same model. → source
05Ai2's LitPivot Checks Research Ideas Against the Literature [AI Tools & Ecosystem]Ai2's LitPivot critiques research proposals with excerpts from papers it finds, then suggests revisions a researcher can accept, edit or reject. In a 17-researcher study, domain experts rated the ideas it helped shape as better grounded in the literature than those drafted with a chatbot. → source
06Hugging Face Maps Which Models Run on AWS Trainium [AI Tools & Ecosystem]Hugging Face now publishes a live compatibility directory showing which models on its Hub run unmodified on AWS's Trainium chips, with tested memory and run-time numbers for each. It follows October's one-line-config ports of PyTorch to Trainium and Google TPUs. → source
07Speculative Decoding Makes Any LLM Up to 3x Faster Without Retraining [AI Tools & Ecosystem]A new tutorial explains speculative decoding: a small draft model proposes several tokens at once, and the big model verifies them in a single pass, typically 2-3 times faster with no change in output. No retraining is required. → source
08Nvidia's Duck Robot Draws 20,000 Preorders After the CEO's Keynote [AI Applications & Industry]Jensen Huang played with a flock of waddling Microduck toy robots at Nvidia's GTC Berlin keynote, and preorders hit 20,000 the next day. The open-source ducklings, built by Hugging Face's Pollen Robotics and trained in simulation, ship before Christmas. → source
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