用结构化状态更新替代对话，提升大模型多智能体协作的可靠性与可审计性。
PatchBoard: Schema-Grounded State Mutation for Reliable and Auditable LLM Multi-Agent Collaboration
- 以JSON Patch方式更新共享结构化状态，避免语言歧义
- 在ALFWorld上成功率达84.6%，高于基线模型30%以上
- 适合需要高可靠性和审计追溯的自动化任务系统
大模型多智能体系统常通过自然语言对话或松散共享内存协作，导致中间状态难以验证、归属和审计。我们提出PatchBoard，一种基于模式的协作架构，用经验证的JSON Patch状态变更替代智能体间的对话。架构由建筑师智能体构建特定任务的模式与工作流规则，确定性内核在事务性提交前，依据模式约束、角色写权限和运行时不变式验证每个状态变更。在630个匹配的ALFWorld任务中，PatchBoard成功率高达84.6%，远超LangGraph的30.8%和Flock的61.6%；同时每成功任务消耗令牌降至45.5k，显著低于LangGraph的368.3k和Flock的64.2k。
原文摘要 · Abstract (English)
LLM multi-agent systems often coordinate through natural-language dialogue or loosely structured shared memory, making intermediate state difficult to validate, attribute, and audit. We introduce PatchBoard, a schema-grounded collaboration architecture that replaces inter-agent dialogue with validated JSON Patch mutations over a shared structured state. An Architect agent constructs a task-specific schema and workflow rules, while a deterministic kernel validates each proposed state mutation against schema constraints, role-specific write contracts, and runtime invariants before committing it transactionally. On 630 matched ALFWorld episodes, PatchBoard achieves an 84.6% success rate, compared with 30.8% for LangGraph and 61.6% for Flock, while reducing tokens per successful task to 45.5k, compared with 368.3k and 64.2k, respectively.
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