Recursive Superintelligence

Headlines · 4

1. 14h
   Ex-Anthropic, DeepMind researcher Yao Shunyu reportedly starts RSI firm at $2B valuation

   Market reports dated Oct 13 say Yao Shunyu — a 29-year-old Tsinghua physics graduate and Stanford theoretical-physics PhD who worked on Claude and Gemini — has started a company in the RSI (recursive self-improvement) space to build customized models for enterprises, at a rumored valuation of about $2 billion with mostly overseas backers; the report is unconfirmed and no funding amount has been disclosed. Yao joined Anthropic in October 2024, contributing to agentic coding and tool-use research for Claude 3.7 and reinforcement-learning foundations for the Claude 4 family, and left partly over the company's China-related stance before joining Google DeepMind on Sept 29, 2025. The piece also surveys the crowded RSI field, including Tian Yuandong's Recursive Superintelligence, Jeff Dean's Discovery Loop, Sakana AI and China's Chaoyan Intelligence, noting there is no consensus on technical approach and that automated research may raise compute bills before it lowers them.

   虎嗅 AI · 🔥 7
2. 5d
   LeCun's H-JEPA hierarchical world model takes aim at agents and RSI

   Yann LeCun's new world model H-JEPA is presented as the first reproducible implementation of the hierarchical JEPA idea from his 2022 paper: multiple JEPAs are stacked, each with its own observation encoder, action encoder and latent predictor, forecasting states at different time horizons, with high levels setting coarse goals that lower levels refine into actions. The article reports that a three-layer H-JEPA raised success on Visual AntMaze to 73% from 18% for single-layer LeWM, but on Push-T two layers worked best and three or four layers degraded, using roughly 58% and 14% of LeWM's transferable training data respectively; training uses neither reward nor reconstruction, and DROID teleoperation experiments needed an inverse-dynamics constraint to avoid slow-feature collapse. It frames H-JEPA as a counterpoint to today's agents, which split tasks into more steps and more tokens, and to recursive self-improvement, which LeCun calls only an amplifier of existing abilities.

   虎嗅 AI · 🔥 0
3. 7d
   Yuandong Tian: co-writing a paper with GPT-5 made me fear job loss within 5 years

   Yuandong Tian, a former Meta FAIR researcher and co-founder of Recursive Superintelligence, said on The Information Bottleneck podcast that he treated his final Meta paper — on the neural-network grokking phenomenon — as a collaboration with GPT-5, using the model to prove and check theorems and brainstorm, and estimates the workflow was roughly 6–10x faster. That experience, he said, led him to conclude his own job could be replaced within about five years, and to start a company instead: "rather than passively lose my job, I'd rather start a company heading in that direction." He was also skeptical of full autonomy, saying models asked to generate ideas on their own produce mediocre ones and that humans still frame the questions and validate results. He argued that restricting recursive self-improvement is hard because the boundary is blurry, and said Recursive will open-source its research. Claims such as the company's reported $4.65 billion valuation and over $650 million raise, and OpenAI's unreleased internal model producing 722 math manuscripts, come from the article and interview accounts.

   36氪 人工智能 · 🔥 0
4. Oct 9
   Recursive self-improvement in 2026: how far has AI building AI actually gone?

   A lengthy Chinese feature surveys the state of recursive self-improvement (RSI) in 2026: Anthropic says Claude "leads" 26% of its R&D work and wrote over 80% of merged code, OpenAI says its "intern-level" AI research assistant arrived on schedule with roughly 3.1 "agent workdays" per human researcher day, and Zhipu and MiniMax describe self-improvement loops around inference infrastructure and scaffolds. It also offers counter-evidence: a Princeton "shadow evaluation" in which Claude failed to produce a publishable paper, and Weco AI's AIDE², which claims level-1 "net positive" RSI but did not pass its own level-2 "ignition" test. The piece cites Mikhail Burtsev's R_AI "recursive reproduction number" to argue the signal to watch is whether each research-capability gain makes the next gain easier.

   36氪 人工智能 · 🔥 0

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