思想团队：通过协调工具调用实现代理系统高效测试时扩展
Team of Thoughts: Efficient Test-time Scaling of Agentic Systems through Orchestrated Tool Calling
AI总结：
本文提出Team-of-Thoughts框架，通过协调工具调用实现异构代理系统在测试时的高效扩展，通过动态激活最兼容的代理提升数学推理和代码生成任务的性能。
摘要：
现有的多代理系统（MAS）通常依赖同构模型配置，无法利用不同预训练架构中固有的多样化专业知识。我们提出Team-of-Thoughts，一种异构MAS框架，将不同模型视为在指挥官驱动范式下的专用工具。Team-of-Thoughts引入两个新组件：（1）指挥官校准，识别具有优越协调和综合能力的模型；（2）代理自我评估，一种协议，其中工具代理评估自身领域特定的优势以指导选择。在推理时，指挥官根据这些资料动态激活最兼容的代理以最大化能力覆盖。在五个数学推理和代码生成基准测试中，Team-of-Thoughts始终优于单个模型和现有MAS基线。值得注意的是，在AIME24和LiveCodeBench上，Team-of-Thoughts分别达到96.00%和77.91%的准确率，显著优于同构角色扮演基线（80.00%和65.93%）
英文摘要：
Existing Multi-Agent Systems (MAS) typically rely on homogeneous model configurations, failing to exploit the diverse expertise inherent in different post-trained architectures. We propose Team-of-Thoughts, a heterogeneous MAS framework that treats diverse models as specialized tools within an orchestrator-driven paradigm. Team-of-Thoughts introduces two novel components: (1) Orchestrator Calibration, which identifies models with superior coordination and synthesis capabilities, and (2) Agent Self-Assessment, a protocol where tool agents profile their own domain-specific strengths to guide selection. At inference, the orchestrator dynamically activates the most compatible agents based on these profiles to maximize capability coverage. Across five mathematical reasoning and code generation benchmarks, Team-of-Thoughts consistently outperforms individual models and existing MAS baselines. Notably, on AIME24 and LiveCodeBench, Team-of-Thoughts achieves 96.00% and 77.91% accuracy, respectively, significantly improving over homogeneous role-play baselines (80.00% and 65.93%).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
