趋势解读：Sakana AI bets AI that improves itself can，解读最新研究结论

Japanese startup Sakana AI has founded the "Sakana AI RSI Lab," a research group focused on recursive self-improvement (RSI). The goal is to explore how AI can speed up and improve the development of new AI systems. Instead of training ever-larger models with massive compute, the company focuses on evolutionary optimization. Sakana sees this as a route to more efficient and more widely accessible AI. In a four-phase roadmap, Sakana describes a path toward AI agents that work on their own technical foundations and write code for their underlying architectures. Sakana AI has launched a research lab focused on recursive self-improvement (RSI), the idea that AI systems can iteratively redesign and improve themselves, creating a compounding cycle of progress. The startup sees RSI as a potential way beyond the compute arms race. Japanese AI startup Sakana AI has created a new research group exploring how AI can speed up and improve the development of new AI systems. The Sakana AI RSI Lab builds on the company's earlier work. Since its founding in 2023, Sakana has focused on evolutionary, adaptive AI systems and, more recently, on practical steps toward recursive self-improvement. In its announcement, Sakana points to several research milestones from the past two years. These include LLM-Squared, where language models design better training methods for other language models, and the Darwin Gödel Machine , which generates, tests, and iterates on variants of its own codebase. Ad With ShinkaEvolve and ALE-Agent , Sakana also highlights work on evolutionary program optimization and agents that derive new strategies from trial-and-error loops. Another key project is The AI Scientist , a system for automating parts of scientific research. A later version wrote a paper that passed peer review , according to Sakana. The underlying research was published in Nature in March 2026. Ad DEC_D_Incontent-1 Taken together, Sakana AI presents these projects as evidence that recursive self-improvement is no longer purely theoretical, but is already being tested in controlled research environments. In its blog post , Sakana outlines a four-phase transition from conventional, human-led AI optimization to self-improving systems. Ad The roadmap builds partly on Sakana's own research. It starts with models designed not as chatbots but for open-ended agent tasks from the ground up. With The AI Scientist, Sakana has already shipped a system that applies agent capabilities to automated research, from idea generation and experiments to writing scientific papers. The next step is recursive self-improvement itself: AI agents that actively work on their own technical foundations by writing, benchmarking, and verifying code for their underlying architectures. Ad DEC_D_Incontent-2 The fourth phase and long-term goal is broader access to frontier AI. Sakana positions RSI as a counter to the dominant scaling paradigm: instead of training ever-larger monolithic models with ever more compute, the company bets on adaptive systems and evolutionary optimization, where AI finds better solutions in as few attempts as possible. Ad