智能多代理通信驱动的协作工作流建模：从沟通到任务完成

摘要

针对复杂工作场景中的团队协作需要多样化的沟通策略，但现有多智能体大语言模型系统缺乏面向任务的系统性沟通框架。我们提出"任务达成沟通框架"（C2C），这一可扩展框架通过两项关键创新填补空白：（1）创新性提出"对齐因子"指标，该量化智能体任务对齐度的新标准直接影响工作效率；（2）集成逐步执行与智能沟通决策的序列化行动框架。C2C使智能体能够做出成本感知的沟通选择，通过精准交互动态提升任务理解能力。我们在三个复杂度层级、5至17个智能体规模的现实编程工作流中评估C2C，并与无沟通基准和固定步骤基准进行对比。结果表明，C2C在可接受的沟通成本下将任务完成时间缩短约40%。该框架在标准配置下成功完成所有任务，并具备规模化应用的有效性。C2C既为衡量多智能体系统沟通效能建立了理论基础，也为复杂协作任务提供了实践框架。

English

Teamwork in workspace for complex tasks requires diverse communication strategies, but current multi-agent LLM systems lack systematic frameworks for task oriented communication. We introduce Communication to Completion (C2C), a scalable framework that addresses this gap through two key innovations: (1) the Alignment Factor (AF), a novel metric quantifying agent task alignment that directly impacts work efficiency, and (2) a Sequential Action Framework that integrates stepwise execution with intelligent communication decisions. C2C enables agents to make cost aware communication choices, dynamically improving task understanding through targeted interactions. We evaluated C2C on realistic coding workflows across three complexity tiers and team sizes from 5 to 17 agents, comparing against no communication and fixed steps baselines. The results show that C2C reduces the task completion time by about 40% with acceptable communication costs. The framework completes all tasks successfully in standard configurations and maintains effectiveness at scale. C2C establishes both a theoretical foundation for measuring communication effectiveness in multi-agent systems and a practical framework for complex collaborative tasks.

智能多代理通信驱动的协作工作流建模：从沟通到任务完成

Communication to Completion: Modeling Collaborative Workflows with Intelligent Multi-Agent Communication

摘要

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