Sakana AI’s TreeQuest: Deploy Multi-Model Teams that Outperform Individual LLMs by 30%

Sakana AI’s TreeQuest: Deploy multi-model teams that outperform individual LLMs by 30%

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 TreeQuest: Deploy multi-model teams that outperform individual LLMs by 30% – #CryptoUpdatesGNIT

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…

On-device LLMs: The Disruptive Shift in AI Deployment

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Sakana AI’s New Algorithm Can Let Gemini and ChatGPT Work Together

Sakana AI released an open-source algorithm on Tuesday, which allows multiple artificial intelligence (AI) models to collaborate on complex problems. Dubbed Adaptive Branching Monte Carlo Tree Search (AB-MCTS), it is an inference-time scaling or test-time scaling algorithm that adds a third dimension to the existing framework of large language models (LLMs).