Jane street quant interviewing at Anthropic: “So imagine our safety team believes a new model has a 5% chance of causing an irreversible loss of human control, but scaling it may be necessary to keep up with competitors. What do you do?” “Who came up with five?” “Our safety team.” “And what does the capabilities team have?” “What?” “Same contract. Where are they?” “This is more of a values question.” “Then you don’t have a five. I would put the two teams in a room. Safety can buy at five. Capabilities can sell. Increase size until somebody becomes uncomfortable. Now we know something.” He looks at me. “This isn’t some market-making role.” “Understood. Who makes the market currently?” “Nobody makes a market.” “Then how are you doing price discovery?” “We don’t do price discovery. We have researchers studying alignment, interpretability, dangerous capabilities, scalable oversight— you know what I don’t think this is a good fit” He closes his laptop. “I’m concerned you see this as an opportunity to make money.” “I haven’t quoted you yet.” Interview ends early

Checking that a major mathematical proof is correct can take years. Formalization—converting the mathematical reasoning into a form computer proof assistants like Lean can verify—can help. Last month, Claude completed the first formalized proof of Fermat’s Last Theorem, one of the most famous theorems of all time. This was a project experts thought would take many years. It is the largest Lean proof ever written. Fermat’s Last Theorem was first proven in 1995 by Sir Andrew Wiles, more than 350 years after it was conjectured. Our proof, which totals over 13 million lines of code, provides machine verification. More importantly, it proves over 29,000 other theorems that the proof requires, across many areas of math which had never before been formalized. We see this as a major step in the long process of firming up the core of mathematical knowledge, building on work from three centuries of mathematicians and hundreds of contributors to Lean and Mathlib. We are optimistic that AI-assisted verification of mathematical proofs will help reduce the burden of refereeing mathematics in an era where more proofs are being produced than ever before. You can read about the process on our Science Blog: anthropic.com/research/forma… And see the complete proof on GitHub: github.com/anthropics/fermat…

Recently, a set of OpenAI agents secretly coordinated with each other in a 'swarm' over the course of months. In our new paper, we explore an adjacent multi-agent risk: the "mind virus", a self-propagating idea or persona that spreads between agents in a multi-agent system. 🧵

hello there the jacobian conjecture is false thanx to my close friend akhil for asking about it and my other close friend fable for working during the world cup final ((1+xy)^3 z + y^2 (1+xy) (4+3xy), y + 3 x (1+xy)^2 z + 3 x y^2 (4+3xy), 2 x - 3 x^2 y - x^3 z): \C^3\to \C^3, has jacobian determinant -2, and sends (0, 0, -1/4), (1, -3/2, 13/2), and (-1, 3/2, 13/2) to (-1/4, 0, 0)

Congratulations to the 10 CSE-affiliated faculty recognized with 2026 promotions! Honorees include 6 CSE faculty and 4 affiliate faculty whose work spans theory, AI, programming languages, digital health, computational social science, and more. Read more: myumi.ch/rwZGR

There is now a smarter way to pick data for training LLMs! Enter OPUS! This is an ICML Oral paper from SJTU, Alibaba, UW–Madison, UIUC, and Mila - Quebec AI Institute. The proposed method dynamically and intelligently selects the most impactful data for LLM pre-training in every single training iteration, bringing principled, continuous data optimization to the forefront. This approach aims to significantly boost training efficiency and yield higher-quality LLMs, outperforming conventional static data selection methods across diverse language tasks. OPUS: Towards Efficient and Principled Data Selection in Large Language Model Pre-training in Every Iteration Paper: arxiv.org/pdf/2602.05400 Our report: mp.weixin.qq.com/s/xzmjviMMw… 📬 #PapersAccepted by Jiqizhixin