drama summary for those confused: - Aug 15th: Tristan Buckmaster & Levent Alpöge make progress on a few important math problems - they do NOT have a proof for the $1,000,000 Millenium Prize problem. BUT, they do claim to have a proof for a similar (non-Millenium) Navier Stokes problem that could help lead the way there - Levent works at Anthropic, but this research was independent of his work there, with a mix of GPT and Claude models. Tristan is not related to Anthropic. - Early Sep: Rumor spreads to OpenAI that Anthropic has solved a major problem. Tristan emails OpenAI to clarify. without revealing the problem they solved or how they did it. - After hearing of the rumor, OpenAI started researching Navier Stokes with a new internal model. - Sep 6th: OpenAI's Sebastien Bubeck tells Tristan that they solved the $1,000,000 Millenium Prize Navier Stokes problem. The approach is very similar to Tristan & Levent's approach to the non-Millenium problem. - Tristan is suspicious of the timing, as only few others were trying this approach. OpenAI says the model didn't access his user data directly, but leaves unanswered whether Tristan's chat conversations were part of the training. - OpenAI says they would partially credit Tristan for the $1,000,000 discovery (even though Tristan did not solve the $1,000,000 problem) — but only if they remove Levent as an author, as he works for Anthropic. - Sep 8th: Tristan refuses to remove Levent, and rushes to publish their results independently. Currently unclear is whether Anthropic had a separate solution for the $1,000,000 problem, or whether the rumor was about Tristan & Levent's independent research.

there are people who have load-bearing beliefs about human intelligence generally and their intelligence specifically that increasing AI capabilities render increasingly obviously fake, and they are not going to have a good time. the truth is that the vast majority of what we call thinking is also just regurgitating or recombining thoughts we've seen already, what frontier models can already do is simply not that different from what we do nearly all the time. your highly original thoughts that demonstrate the depth and breadth of your soul are mostly warmed-over reheated leftovers of leftovers of thoughts that were first articulated centuries if not millennia ago. this is completely fine and ordinary and not a problem, unless you were basing your ego defenses on this very popular but largely fake misunderstanding of what thoughts are and where they come from. you cannot own a thought. they were never yours to accrete your sense of self around. they are farts that gently pass through the butthole of your mind, full of sound and fury, signifying nothing

Can a model learn to break its own reasoning plateau? In our new paper, we show that LLMs can be taught with meta-RL to generate their own "stepping stones" that kickstart learning on hard math problems (0/128 success rate) where direct RL fails. Paper 📝: arxiv.org/abs/2601.18778 Blog post 🌐: ssundaram21.github.io/soar/ (1/n)

What if the best visual reasoning steps are ones humans can’t specify? 🤔 Existing VLM reasoning is often constrained by language, pixels, and human-designed intermediates. We introduce Latent Implicit Visual Reasoning, where we show that VLMs can discover the best visual reasoning steps by themselves — no bboxes, no intermediate images, no extra supervision. Presenting this week at CVPR! (1/n)🧵

A SINGLE encoder + decoder for all the 4D tasks! We release 🎯 D4RT (Dynamic 4D Reconstruction and Tracking). 📍 A simple, unified interface for 3D tracking, depth, and pose 🌟 SOTA results on 4D reconstruction & tracking 🚀 Up to 100x faster pose estimation than prior works

Text-to-image (T2I) models can generate rich supervision for visual learning but generating subtle distinctions still remains challenging. Fine-tuning helps, but too much tuning → overfitting and loss of diversity. How do we preserve fidelity without sacrificing diversity (1/8)

Today seems to be a fitting day for @GoogleDeepMind news, so I'm excited to announce our new preprint! Prior work suggests that text & img repr's are converging, albeit weakly. We found these same models actually have strong alignment; the inputs were too impoverished to see it!