TUMIX: Multi-Agent Test-Time Scaling with Tool-Use Mixture

• URL: http://arxiv.org/abs/2510.01279v1
• Date: Tue, 30 Sep 2025 19:19:56 GMT
• Title: TUMIX: Multi-Agent Test-Time Scaling with Tool-Use Mixture
• Authors: Yongchao Chen, Jiefeng Chen, Rui Meng, Ji Yin, Na Li, Chuchu Fan, Chi Wang, Tomas Pfister, Jinsung Yoon,
• Abstract summary: We propose an ensemble framework that runs multiple agents in parallel, each employing distinct tool-use strategies and answer paths.<n>TUmix achieves significant gains over state-of-the-art tool-augmented and test-time scaling methods.<n>We find that agent diversity and quality are crucial and can be enhanced by using LLMs to auto-optimize agent designs.
• Score: 60.945393748584316
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: While integrating tools like Code Interpreter and Search has significantly enhanced Large Language Model (LLM) reasoning in models like ChatGPT Agent and Gemini-Pro, practical guidance on optimal tool use is lacking. The core challenge is effectively combining textual reasoning, coding, and search for diverse questions. In this paper, we propose 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, delivering an average accuracy improvement of up to 3.55% over the best baseline on Gemini-2.5-Pro and Gemini-2.5-Flash across key reasoning benchmarks, with near-equal inference costs. We find that agent diversity and quality are crucial and can be enhanced by using LLMs to auto-optimize agent designs. Furthermore, TUMIX can halt refinement upon reaching sufficient confidence, preserving performance at only 49% of the inference cost. Further scaling can achieve higher performance, albeit at a greater cost.

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