After leaving Meta, Tian Yuandong has also started his entrepreneurial journey.
Just now, the startup company Recursive_SI officially debuted and disclosed its list of founders, which includes Tian Yuandong.
In addition to Tian Yuandong, the founding team includes Richard Socher (CEO), Tim Rocktäschel, Jeff Clune, Tim Shi, Caiming Xiong, and Alexey Dosovitskiy, among others.
These founding members have previously been involved in establishing AI research labs at Salesforce and Uber, and have held leadership positions at teams such as OpenAI, DeepMind, Google Brain, and Meta, possessing extensive research and entrepreneurial experience.
Recursive_SI is committed to building an artificial intelligence that can autonomously conduct experiments and safely self-improve—continuously evolving within an open-ended, automated scientific discovery process, which is considered the most likely path towards superintelligence.
Currently, Recursive has raised $6.5 billion with a valuation of $46.5 billion, led by GV (Google Ventures) and Greycroft, with significant investments from AMD Ventures and NVIDIA.
The team has grown to over 25 members and continues to expand, having attracted many outstanding talents, including Zhuge Mingchen, who is set to join soon.
Zhuge Mingchen is currently a Founding Member at Recursive. He holds a Ph.D. in Computer Science from King Abdullah University of Science and Technology (KAUST), where he studied under Professor Jürgen Schmidhuber, known as the "Father of LSTM." His research focuses primarily on Coding Agents, Recursive Self-Improvement (RSI), and Next-generation Machine Paradigms.
Since 2023, Zhuge Mingchen has been systematically exploring the direction of Recursive Self-Improvement (RSI).
During the MetaGPT period, he proposed that agents should possess mechanisms for continuous self-optimization and capability evolution, advancing this research direction in subsequent work. Among these, GPTSwarm is considered one of the earliest RSI system paradigms in the LLM era. It was the first to systematically propose and validate a Graph-based Agents framework for self-organizing collaboration, enabling coordination, feedback, and capability evolution among agents through dynamic graph structures. Its core concepts have since been widely adopted by numerous subsequent multi-agent and Agentic AI projects. Agent-as-a-Judge further explored continuous feedback and self-evaluation mechanisms in long-term tasks, attempting to address issues of continuity and stable optimization for agents in complex missions. Research on NeuralComputer further advanced towards next-generation AI system architectures, exploring new machine paradigms that integrate memory, reasoning, and autonomous evolution capabilities.
It can be seen that the research team joining Recursive possesses profound academic experience in the field of recursive self-improvement.
Tian Yuandong and several other founders have promoted the venture on X: "We are building an artificial intelligence that can autonomously discover knowledge and recursively self-improve—this open-ended process will fundamentally change the way science and technology advance."
In several core areas of recursively self-improving artificial intelligence, the team is at the industry forefront.
Members have previously achieved major breakthroughs in fields such as open-ended algorithms, quality-diversity algorithms, AI-generated algorithms, self-improving programming agents, automated red teaming and capability discovery, prompt engineering and its automation, learning challenges and environment generation, foundational world models, deep learning for natural language processing, vision transformers, retrieval-augmented generation, and AI scientists.
Therefore, we are truly filled with anticipation for the upcoming research from Recursive_SI.
This article is from the WeChat public account "机器之心" (Machine Heart), author: 机器之心 (Machine Heart), editor: 机器之心编辑部 (Machine Heart Editorial Department)
