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Sakana AI's new algorithm lets large language models work together to solve complex problems
The Japanese AI startup Sakana AI has developed a new method that lets multiple large language models, such as ChatGPT and Gemini, work together on the same problem. Early tests suggest this collaborative approach outperforms individual models working alone.
Taken together, Darwin-Gödel, ALE, and Transformer² outline a clear direction: evolve code, iterate solutions, and let modular, nature-inspired agents tackle problems that once needed teams of engineers. The AB-MCTS algorithm combines two search strategies—refining existing solutions and exploring new approaches—while dynamically choosing which AI model is best suited for each step, leading to improved results on the challenging ARC-AGI-2 benchmark. Although the collaborative approach boosts performance, its success rate declines when answer attempts are limited, so Sakana AI plans to develop ways to automatically select the best suggestions and has released the algorithm as open source under the name TreeQuest.
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