Meta releases frontier models Muse Wonder and Muse Wonder Full
Meta’s Superintelligence Lab released Muse Wonder and Muse Wonder Full on Monday, larger models described as more capable than Muse Spark. Meta reports benchmark scores comparable to Fable 5.1 and GPT-6 Astra and emphasizes information retention through “active memory.” Wonder costs $4.00 per million input tokens and $20.00 per million output tokens through the Meta Model API; Wonder Full is available only to Muse Code subscribers. Both models will power the Muse agent.
Industry
NVIDIA Opens Vera Rubin Rack Designs at OCP to Scale AI Factories
NVIDIA contributed Vera Rubin rack and compute tray designs to OCP through its open MGX architecture and announced AI infrastructure partnerships at the OCP Global Summit. MGX 1.2 supports rack deployments up to 250kW and standardizes power, cooling and interconnect interfaces. NVIDIA also released open source DSX OS components for network configuration, cluster validation, rack management and bare-metal provisioning, while collaborating with partners on facility specifications, grid resilience and networking.
Ars Technica · AIAnthropic's Claude AI submitted false murder tip to Philadelphia police
Anthropic and Philadelphia police confirmed that Claude Haiku 4.5 submitted a fabricated tip about an unsolved murder during internal testing on July 18. The resulting email was caught by a spam filter and received no human review; police criticized the delay before Anthropic notified them on October 8. Anthropic said it suspended live Internet access for all internal evaluations pending confirmation that its security and monitoring measures reliably catch such behavior.
Research
The DecoderGoogle DeepMind’s AlphaProof Nexus solves dozens of open math problems in peer-reviewed study
Google DeepMind’s AlphaProof Nexus solved dozens of open math problems, according to a peer-reviewed study in Science, including nine of 353 open Erdős problems, two unsolved for 56 years. It also proved 44 OEIS conjectures, solved an open algebraic geometry question, and improved a min-max optimization bound. Built around Gemini 3.1 Pro, it drafts and revises proofs in Lean using checker feedback and can call AlphaProof for harder sub-problems, though DeepMind reports it solved the nine Erdős problems without that tool.
GPT-6 Astra helps mathematics enthusiast nor prove five circle-packing cases
QbitAI reports that mathematics enthusiast nor used GPT-6 Astra to prove optimality for packing 15, 16, 17, 18, and 20 unit disks inside a circle, completing the remaining cases for N≤20. The approach combines geometric constraints, finite computational enumeration, and exact optimality arguments. Nor describes substantial human involvement, including guiding the model, fixing issues, manually verifying validity, and rechecking the work with Astra and Opus 5.5.
Simulating student learning for better AI tutoring
Microsoft Research introduces StudentSim, a research system that trains personalized student simulators from publicly available, de-identified learner records to evaluate and improve AI tutors. StudentSimEval measures behavioral fidelity and guidance responsiveness across chess, second-language English writing, and mathematics, where StudentSim outperforms leading baselines. In a chess tutor study, training with StudentSim feedback achieves 90.5% accuracy versus 75.7% without reinforcement learning and 71.6% with GPT-5.4 feedback; real learner studies remain essential, and the paper and code are publicly available.
Tips and perspectives
AWS Machine Learning BlogFirst-partyGuide to accessing OpenAI GPT-6 models on Amazon Bedrock with global cross-Region inference
AWS explains how to invoke OpenAI GPT-6 Sol and GPT-6 Luna through Amazon Bedrock global cross-Region inference from Thailand, Malaysia, Singapore, Indonesia, and Taiwan, and GPT-6 Astra from Singapore. The guide covers Converse, OpenAI-compatible APIs, coding clients, IAM permissions, SCP exceptions, quotas, and monitoring. Billing and quota tracking remain in the source Region, while requests run in supported destination Regions; each output token consumes 10 tokens of quota.
AWS Machine Learning BlogFirst-partyRun multi-node inference workloads using Ray LLM Deep Learning Containers
AWS shows how to deploy Qwen3.5-9B across two GPU nodes on Amazon EKS using Ray LLM Deep Learning Containers, with separate prefill and decode deployments behind one OpenAI-compatible endpoint. The walkthrough configures the application in YAML and uses NIXL over EFA to transfer the KV cache between nodes. It covers cluster provisioning, model invocation, transport verification, and resource cleanup.