🚨 New paper: Introducing MIND (Monge Inception Distance) Everyone agrees that FID is broken, requires too many samples, slowing down evals. MIND requires 10x fewer samples, is more robust, faster to compute. Our new drop-in replacement for evaluating generative models. 🧵👇

New on the Science Blog: Yes, Claude can do Nine Loops. Theoretical physicists predict how particles behave using formulas called scattering amplitudes. These are notoriously hard to compute, so researchers work with layers of increasingly fine corrections called “loops”—each added loop makes the answer more precise but takes exponentially more computation. Most calculations stop at two or three loops. Eight loops was the previous record in a simplified model physicists use as a testing ground (planar N=4 super-Yang-Mills), set by SLAC's Lance Dixon and collaborators. Last month, physicist and science writer @4gravitons issued a challenge: could an AI push past eight loops in this model, using only the compute budget an academic could reasonably access? Given a single prompt describing the nine-loop problem, Claude ran largely unsupervised for days in Claude Science and solved it using methods developed by Dixon and his colleagues, at a total cost of a few thousand dollars. Dixon independently verified the result, and von Hippel wrote about the experience for our blog. Read more: anthropic.com/research/yes-c…

A while ago we presented an early version of our work on spherical flows for categorical data at @diffusion_llms. The paper has grown a lot since, so it felt like time for a thread...

A very very bad decision.

holy shit. claude just pushed a theoretical physics calculation beyond the previous record after working on it largely by itself for days. >previous record: 8 loops >claude reached 9 >largely unsupervised >wrote its own code >found two different ways to calculate it >both gave the same result >107,053 nonzero coefficients matched physicists then spent weeks checking the work this is the shit i’ve been waiting for. science is about to get fucking weird.

📢Excited to share our new paper: Continuous Diffusion Scales Competitively with Discrete Diffusion for Language We introduce RePlaid 🌊, a continuous diffusion language model (DLM) with 🏅Discrete likelihood bound 🏅Scaling laws competitive with SOTA discrete DLMs How? Dive in👇[🧵1/12] Paper: arxiv.org/abs/2605.18530 Work done with my amazing collaborators: @WeiGuo01 @ShuibaiZ69721 @ssahoo_ @YongxinChen1 @ArashVahdat @MardaniMorteza @jwthickstun

Sakana AI welcomes Jürgen Schmidhuber as Chief Scientific Advisor. sakana.ai/schmidhuber/ Sakana AI is incredibly proud to announce that Jürgen Schmidhuber, universally recognized as the father of modern AI, is officially joining Sakana AI as Chief Scientific Advisor. For nearly four decades, Jürgen has explored how machines can learn to learn. His foundational work in the 1990s drove core advancements in deep learning and established early frameworks for world models. Crucially, his pioneering innovations in meta-learning opened the very path toward recursive self-improvement. These ideas have already shaped our own research, from the Darwin Gödel Machine to The AI Scientist. Now Jürgen will help guide our newly formed RSI Lab, whose objective is to trigger a compounding cycle of scientific discovery aimed at improving machine intelligence. We are assembling a critical mass of world-class experts in Tokyo to make this a reality. Welcome, @SchmidhuberAI !

Artificial intelligence systems do not think, feel, want or understand. Avoid language that gives them human characteristics. This is called anthropomorphizing, when we ascribe human traits, emotions or behaviors to non-human things, such as animals or inanimate objects. Instead, explain what a system does, how well it performs, who built it and who could be affected by it. apnews.com/article/openai-sa…

People wonder about the unreasonable effectiveness of math, but the real question is about the unreasonable ineffectiveness of any math that is more than just a little more advanced than Calculus

We instead show that the real reason is that critical windows for masked diffusion are quadratically narrower than critical windows for uniform/Gaussian! 3/