A Tree Search Library with Flexible API for LLM Inference-Time Scaling - SakanaAI/treequest

Sakana AI's new inference-time scaling technique uses Monte-Carlo Tree Search to orchestrate multiple LLMs to collaborate on complex tasks.

A Tree Search Library with Flexible API for LLM Inference-Time Scaling - SakanaAI/treequest

Sakana AI's new inference-time scaling technique uses Monte-Carlo Tree Search to orchestrate multiple LLMs to collaborate on complex tasks.

On 23 June 2025, IIASA will launch the global citizen science “Tree-Quest” campaign at the Living Planet Symposium in Vienna, inviting people from all over the world to take part using the free Geo-Qu...

Sakana AI's new inference-time scaling technique uses Monte-Carlo Tree Search to orchestrate multiple LLMs to collaborate on complex tasks.Read More

A Tree Search Library with Flexible API for LLM Inference-Time Scaling - SakanaAI/treequest

Sakana AI's new inference-time scaling technique uses Monte-Carlo Tree Search to orchestrate multiple LLMs to collaborate on complex tasks.

Sakana AI's new inference-time scaling technique uses Monte-Carlo Tree Search to orchestrate multiple LLMs to collaborate on complex tasks.

著者：ソウマ(著)　個人出版　2026/7/29(水)配信

😱 What if you could make AI 30% smarter by turning it into a collaborative genius? Imagine your AI prompts failing because you’re stuck with a single model’s limitations—vague answers, hallucinations, or just missing the mark. 😅 Now picture a squad of AI models working together, each bringing its A-game to crush complex tasks. That’s the magic of TreeQuest Prompting…

Japanese researchers prove AI models work better as teams than alone, boosting performance 30%. TreeQuest system lets companies mix different AI providers instead of relying on one, potentially cutting costs while improving results.

Sakana AI's new inference-time scaling technique uses Monte-Carlo Tree Search to orchestrate multiple LLMs to collaborate on complex tasks.

Want smarter insights in your inbox? Sign up for our weekly newsletters to get only what matters to enterprise AI, data, and security leaders. Subscribe Now Japanese AI lab Sakana AI has introduced a new technique that allows multiple large language models (LLMs) to cooperate on a single task, effectively creating a “dream team” of AI agents. The method, called Multi-LLM AB-MCTS, enables models to perform trial-and-error and combine their unique strengths to solve problems that are too complex for any individual model.

Do you want smarter ideas in your entrance tray? Register in our weekly newsletters to obtain only what matters to the leaders of AI, data and business security. Subscribe now Japanese laboratory Samán It has introduced a new technique that allows multiple models of large language (LLM) to cooperate in a single task, effectively creating a "dream equipment" of AI agents.

Sakana AI's new inference-time scaling technique uses Monte-Carlo Tree Search to orchestrate multiple LLMs to collaborate on complex tasks.

Sakana AI's new inference-time scaling technique uses Monte-Carlo Tree Search to orchestrate multiple LLMs to collaborate on complex tasks.