Team of Thoughts: Efficient Test-time Scaling of Agentic Systems through Orchestrated Tool Calling

• URL: http://arxiv.org/abs/2602.16485v1
• Date: Wed, 18 Feb 2026 14:19:01 GMT
• Title: Team of Thoughts: Efficient Test-time Scaling of Agentic Systems through Orchestrated Tool Calling
• Abstract summary: We introduce Team-of-Thoughts, a novel Multi-Agent System (MAS) that leverages the complementary capabilities of heterogeneous agents via an orchestrator-tool paradigm.<n>Our framework introduces two key mechanisms to optimize performance: (1) an orchestrator calibration scheme that identifies models with superior coordination capabilities, and (2) a self-assessment protocol.<n>Experiments on five reasoning and code generation benchmarks show that Team-of-Thoughts delivers consistently superior task performance.
• Score: 11.13437934037871
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Existing Multi-Agent Systems (MAS) typically rely on static, homogeneous model configurations, limiting their ability to exploit the distinct strengths of differently post-trained models. To address this, we introduce Team-of-Thoughts, a novel MAS architecture that leverages the complementary capabilities of heterogeneous agents via an orchestrator-tool paradigm. Our framework introduces two key mechanisms to optimize performance: (1) an orchestrator calibration scheme that identifies models with superior coordination capabilities, and (2) a self-assessment protocol where tool agents profile their own domain expertise to account for variations in post-training skills. During inference, the orchestrator dynamically activates the most suitable tool agents based on these proficiency profiles. Experiments on five reasoning and code generation benchmarks show that Team-of-Thoughts delivers consistently superior task performance. Notably, on AIME24 and LiveCodeBench, our approach achieves accuracies of 96.67% and 72.53%, respectively, substantially outperforming homogeneous role-play baselines, which score 80% and 65.93%.

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