Sakana AI welcomes Jürgen Schmidhuber as Chief Scientific Advisor. https://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 !

Claude has discovered a previously unknown enzyme system hidden in the DNA of bacteriophages. Beside the enzyme’s gene sits a long array of repeating DNA—a structure that looks somewhat similar to CRISPR. We don’t yet understand what this system does, but only a handful of known systems share its features, and all of them are able to cut, copy, and paste DNA. Historically, the discovery of such programmable systems has helped revolutionize medicine. CRISPR, for instance, is now the foundation of genetic medicines. But it will take much more work to learn what this system does, and whether it can be put to similar use. Read more: https://www.anthropic.com/news/claude-discovers-novel-enzyme-system

Please welcome GPT-6 Sol and GPT-6 Luna to the GPT-6 universe. GPT-6 Sol and Luna build on the advances behind GPT-6 Astra, bringing much of its strengths into faster and more affordable models to support work at scale. We’ve also made caching and inference more efficient, and we’re passing the savings directly to you: 50% lower API prices for Sol and Luna compared with GPT‑5.6 promotional pricing.

StepFun's Step 5 Preview scores 44 on the Artificial Analysis Intelligence Index, matching Kimi K3 (max) at ~2.8x lower cost per task, but trails peers on agentic evaluations Step 5 Preview is @StepFun_ai's new flagship model, with 600B total and 27B active parameters, succeeding Step 3.7 Flash (released May 2026). It scores 44 on the Intelligence Index, level with Kimi K3 (max) and just behind GLM-5.3 (max, 45) and Qwen3.8 Max (45) Key takeaways: ➤ Step 5 Preview costs ~2.8x less per Intelligence Index task than models at the same score. It costs ~$0.72 per task, against ~$2.00 for Kimi K3 (max) at the same score of 44 and ~$2.01 for GLM-5.3 (max) at 45. This is driven by pricing: at $1/$2.70 per 1M input/output tokens, it is priced below both on input and output. MiMo-V2.6-Pro is the one model that scores higher (46) at a lower cost per task ($0.13) ➤ Frontier reasoning is the standout strength, and where the jump from Step 3.7 Flash is largest. Step 5 Preview scores 46% on Humanity's Last Exam, in line with Kimi K3 (max, 47%), and 21% on CritPt, between Kimi K3 (23%) and GLM-5.3 (max, 19%). Both are up sharply from Step 3.7 Flash: +25 points on HLE and +19 points on CritPt ➤ Higher AA-Omniscience accuracy than GLM-5.3 at fewer parameters, but with more hallucination. At 600B total parameters, Step 5 Preview reaches 42% accuracy on AA-Omniscience, our benchmark measuring factual recall and hallucination, ahead of GLM-5.3 (max, 34%, 753B) and behind Kimi K3 (max, 48%, 2.8T). It attempts more questions than GLM-5.3 (68% vs 55%) and hallucinates more often when it does (43% vs 30%), landing at 16 on the AA-Omniscience Index, between GLM-5.3 (14) and Kimi K3 (20) ➤ Agentic evaluations are where Step 5 Preview lags peers at a similar Intelligence Index score. It scores 1,566 Elo on GDPval-AA, our primary evaluation for agentic performance, behind Qwen3.8 Max (1,668) and GLM-5.3 (max, 1,646). The gap holds on Terminal-Bench 4.0 (33% vs 39% and 42%), AA-Briefcase (1,432 Elo vs 1,640 and 1,525) and AutomationBench-AA (51% vs 56% and 62%) Key model details: ➤ Model Size: 600B total parameters, 27B active MoE model ➤ Context window: 1M tokens ➤ Multimodality: Text, image and video input, text output ➤ Pricing: $1/$2.70 per 1M input/output tokens, with cached input at $0.05/M ➤ Availability: StepFun first-party API, with open weights release planned for October 15th ➤ Licensing: Closed weights currently, with weights release planned for October 15th

I had the great honor and pleasure of sitting down with @JeffDean for his first public talk since leaving Google, where he spent an extraordinary 27 years. Few people have shaped modern computing and AI as profoundly - from MapReduce and Bigtable to TensorFlow, Mixture-of-Experts, TPUs, and Gemini. Our conversation covered some of the biggest questions shaping the future of AI: • How do you recognize a foundational idea before everyone else does? • How do you choose a research problem worth spending 5 years on? • What can coding teach us about building better reasoning models? • What might recursive self-improvement (RSI) actually look like? • What happens when the scientific discovery loop itself becomes increasingly automated? (and how is Jeff’s new startup going to contribute in this space?) • As AI becomes increasingly autonomous, how do we keep it safe and secure? • What should the next generation of researchers be working on? Here are some key insights and highlights for anyone building the future of AI. 🧵1/8

After co-inventing ChatGPT, I kept asking myself: why have superhuman chat models not led to AGI? I’ve spent the last 2 years in stealth building a new way to train models (RLCD), and a new type of frontier AI model that we are releasing today: Jev • 20-200x faster • 40-400x cheaper (w/ output tokens free) • Frontier composable intelligence optimized for decisions AFAICT the shortest path to AI-based economic revolution

SGLang-Diffusion with VDN-H3 now generates 14.4s of 768p video in just 9.0s 🚀 On 8× B200, 8 step denoising takes just 6.9s, reaching over 2× real time. The 9.0s figure covers the full generation request after warmup. No measured quality regression versus dense 50-step H3 across 103 test prompts. 🧵

Our own @johnschulman2 talks with Dwarkesh about where human judgment still matters as models improve and self-improve: teaching them to handle messy real-world tasks, applying taste to what works in the long run, and, above all, specifying what we actually want.