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We dive into the latest paper from Google and a team of academic researchers: "TUMIX: Multi-Agent Test-Time Scaling with Tool-Use Mixture."
Hear from one of the paper's authors — Yongchao Chen, Research Scientist — walks through the research and its implications.
The paper proposes Tool-Use Mixture (TUMIX), an ensemble framework that runs multiple agents in parallel, each employing distinct tool-use strategies and answer paths. Agents in TUMIX iteratively share and refine responses based on the question and previous answers. In experiments, TUMIX achieves significant gains over state-of-the-art tool-augmented and test-time scaling methods.
Learn more about AI observability and evaluation, join the Arize AI Slack community or get the latest on LinkedIn and X.
By Arize AI5
1313 ratings
We dive into the latest paper from Google and a team of academic researchers: "TUMIX: Multi-Agent Test-Time Scaling with Tool-Use Mixture."
Hear from one of the paper's authors — Yongchao Chen, Research Scientist — walks through the research and its implications.
The paper proposes Tool-Use Mixture (TUMIX), an ensemble framework that runs multiple agents in parallel, each employing distinct tool-use strategies and answer paths. Agents in TUMIX iteratively share and refine responses based on the question and previous answers. In experiments, TUMIX achieves significant gains over state-of-the-art tool-augmented and test-time scaling methods.
Learn more about AI observability and evaluation, join the Arize AI Slack community or get the latest on LinkedIn and X.

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