Claude Opus 5.5 will be the first Opus meant to fall back to a less capable model for "a small set of capabilities related to the development of frontier LLMs, such as kernel development.."

As expected, unfortunately the only bad thing about the new Opus is it's a token-gobbling monster. Uses the most tokens per Intelligence Index task of any model benchmarked. The price cut makes more sense here!

New podcast with @datagenproc of @EpochAIResearch digging into the open questions determining the future of frontier AI! We cover: 00:00 Predictions for RSI 18:15 The role of robotics in an AI acceleration 24:20 How far behind are Chinese models? 27:39 Does distillation explain the gap? 40:58 What Chinese job postings reveal about their labs 48:13 Are open or closed models safer? 58:10 How Epoch AI ticks 1:00:55 What a frontier post-training recipe looks like He's one of the people who gives the best feedback on my writing, so I was stoked to have him on.

releasing many high quality open-source RL environments is the most impactful thing anyone can do to push the open-source frontier right now the equivalent of sharing high quality pretraining data but in the new RLVR paradigm

Today, more than 100 leading AI experts endorsed a set of minimum requirements to take seriously AI companies' recent call to embed external evaluators. These evaluators need to be genuinely independent, transparent, and represent a range of expertise areas. They also need to be guaranteed employee-level access and to be protected from retaliation for findings that make companies look bad. We welcome model developers’ recent calls for independent oversight, but it’s what they do next that matters. The labs must be accountable for ensuring these requirements are met, so that the public can have faith in the process and the outcomes. Over the past week, the AI community has debated the appropriate role of external evaluation, including who should do it and on what terms. We may not agree on everything, but there is a lot of common ground. To make embedded evaluations credible, more than 100 experts with varying backgrounds and ideas about AI risk agree in today’s letter that frontier AI developers should: 1. Guarantee embedded evaluators full editorial independence and mitigate conflicts of interest 2. Rely on multiple evaluators with differing viewpoints and areas of expertise 3. Publicly document the terms under which evaluators operate, as well as facilitating permissive publication of methods and findings 4. Shield evaluators from retaliation 5. Grant access equivalent to that of highly privileged employees There is a thriving and growing ecosystem of independent AI evaluators who are advancing this science every day – but we need aligned standards, guaranteed protections, and independent funding. That’s why we created the AI Evaluator Forum. Today we are entering our next phase. We’re launching an open call for new members, collaborators, and independent funding sources to help evaluators meet this moment and demand accountability from developers. Join us in building the evaluator ecosystem. See the public letter here: Learn more at

Nearly half a year of silence. We spent it studying one problem: how far RL can scale. MiMo-V2.6 is in the middle of its RL run right now. Three things we scaled: compute (~2B tokens per step, 1568 prompts × 16 rollouts, fully async), environments and harnesses (multi-task agentic RL, mixed across multiple harnesses in one run), and grader compute (agentic in-group credit assignment, with test-case and rubric-based rewards). We'll open-source the details piece by piece over the coming weeks. Streaming the run: