语义共识：面向企业多智能体LLM系统的过程感知冲突检测与解决
Semantic Consensus: Process-Aware Conflict Detection and Resolution for Enterprise Multi-Agent LLM Systems
AI总结：
本文提出语义共识框架，通过过程感知中间件解决多智能体系统中因共享目标解释不一致导致的冲突问题，实现100%工作流完成率和高精度冲突检测。
摘要：
多智能体大语言模型（LLM）系统正在迅速成为企业AI自动化的主要架构，但生产部署中故障率在41%至86.7%之间，近79%的故障源于规范和协调问题而非模型能力限制。本文识别出语义意图分歧——合作LLM代理因孤立上下文和缺失过程模型而发展出不一致的目标解释——作为企业环境中多智能体故障的主要但未正式解决的根本原因。我们提出语义共识框架（SCF），一个过程感知的中间件，包含六个组件：共享操作语义的过程上下文层、形式意图表示的语义意图图、实时识别矛盾、竞争性和因果无效意图组合的冲突检测引擎、使用策略-权威-时间层级的共识解决协议、检测渐进性语义分歧的漂移监控器，以及用于组织政策执行的过程感知治理集成层。在涵盖三个多智能体框架（AutoGen、CrewAI、LangGraph）和四个企业场景的600次运行评估中，SCF是唯一实现100%工作流完成率的方法——相比次优基线的25.1%——同时检测65.2%的语义冲突（27.9%精度）并提供完整的治理审计追踪。该框架协议无关，并兼容MCP和A2A通信标准。
英文摘要：
Multi-agent large language model (LLM) systems are rapidly emerging as the dominant architecture for enterprise AI automation, yet production deployments exhibit failure rates between 41% and 86.7%, with nearly 79% of failures originating from specification and coordination issues rather than model capability limitations. This paper identifies Semantic Intent Divergence--the phenomenon whereby cooperating LLM agents develop inconsistent interpretations of shared objectives due to siloed context and absent process models--as a primary yet formally unaddressed root cause of multi-agent failure in enterprise settings. We propose the Semantic Consensus Framework (SCF), a process-aware middleware comprising six components: a Process Context Layer for shared operational semantics, a Semantic Intent Graph for formal intent representation, a Conflict Detection Engine for real-time identification of contradictory, contention-based, and causally invalid intent combinations, a Consensus Resolution Protocol using a policy--authority--temporal hierarchy, a Drift Monitor for detecting gradual semantic divergence, and a Process-Aware Governance Integration layer for organizational policy enforcement. Evaluation across 600 runs spanning three multi-agent frameworks (AutoGen, CrewAI, LangGraph) and four enterprise scenarios demonstrates that SCF is the only approach to achieve 100% workflow completion--compared to 25.1% for the next-best baseline--while detecting 65.2% of semantic conflicts with 27.9% precision and providing complete governance audit trails. The framework is protocol-agnostic and compatible with MCP and A2A communication standards.
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。
