用消息时序图规范大模型智能体协作，避免死锁且可验证。
Provable Coordination for LLM Agents via Message Sequence Charts
- 基于消息时序图构建协作语言，分离通信结构与不可预测的LLM行为。
- 自动生成无死锁的本地智能体程序，确保全局协调正确性。
- 适用于需高可靠协作的场景，如医疗诊断或自动化流程设计。
基于大语言模型（LLMs）的多智能体系统难以推理，协调错误如死锁或消息类型不匹配常难以通过测试发现。本文提出一种基于消息时序图（MSCs）的领域专用语言，用于指定智能体协作。该语言将消息传递结构与LLM调用、工具调用及人工控制点分离，后者结果仍具不确定性。我们定义了语言的语法与语义，并提出一种语法导向的投影方法，从全局协调规范生成无死锁的本地智能体程序。通过诊断共识协议实例展示，协调性质可在独立于LLM非确定性的前提下建立。此外，还描述了一种运行时规划扩展，其中LLM动态生成满足相同结构性保障的协作流程。开源的Python框架ZipperGen已发布。
原文摘要 · Abstract (English)
Multi-agent systems built on large language models (LLMs) are difficult to reason about. Coordination errors such as deadlocks or type-mismatched messages are often hard to detect through testing. We introduce a domain-specific language for specifying agent coordination based on message sequence charts (MSCs). The language separates message-passing structure from LLM calls, tool calls, and human control points, whose outcomes remain unpredictable. We define the syntax and semantics of the language and present a syntax-directed projection that generates deadlock-free local agent programs from global coordination specifications. We illustrate the approach with a diagnosis consensus protocol and show how coordination properties can be established independently of LLM nondeterminism. We also describe a runtime planning extension in which an LLM dynamically generates a coordination workflow for which the same structural guarantees apply. An open-source Python implementation of our framework is available as ZipperGen.
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