openai reports navier-stokes singularity find, a contender for second ever millenium prize awarded, overshadowing cognition's $48b series e, mistral's $24b series d, meta's muse agent, and gpt image 2.5
**openai** announced a proposed navier–stokes proof by an internal model "**significantly more capable than gpt-6 astra**" using **10,000 agents** over **88 hours** plus **17 hours** of formal verification. the effort highlights the emergence of **massive test-time compute scaling** as a new axis beyond pretraining, with estimated costs of **$10m–$40m** and **130b output tokens**. controversy arose over priority, data contamination, and scientific norms, with key figures like **sam altman**, **sébastien bubeck**, and **terence tao** weighing in on governance and open science risks. meanwhile, **meta** launched **muse**, a consumer personal ai agent featuring persistent isolated linux vms, browser integration, and connectors to various apps including meta-native services like instagram and messenger, emphasizing security and broad service integration.
not much happened today
**deepseek** launched **v4.1-flash**, a new open-weight flagship model focused on extreme inference efficiency and low cost, featuring a **763b total-parameter** causal encoder-decoder architecture with **8b active input** and **16b active output** parameters and **1m-token context**. it scored **40 on the artificial analysis intelligence index**, outperforming its predecessor and ranking just below **glm-5.3-flash**. the model supports text and image input, is available under an **mit license**, and is accessible via us/api. the architecture introduces a novel causal encoder-decoder design aimed at reducing active compute and kv/cache costs, with a hybrid sparse/local approach and a unique vision encoder differing from recent chinese models. early layers use a **swa-only** pattern, and the model has an effective depth of about **40 layers** with **20 decoder layers**. baseten and ollama have begun supporting and rolling out the model to users.