用超图结构解决企业系统多跳推理的幻觉问题，提升分析准确性与可审计性。
Hypergraph Enterprise Agentic Reasoner over Heterogeneous Business Systems
- 构建分层超图本体，虚拟化数据接口并编码多值业务规则
- 在供应链任务中实现最高94.7%的根因分析准确率
- 支持无需重训模型的动态推理，适合复杂企业系统部署
将大语言模型应用于异构企业系统时，常因幻觉和多跳、多值推理失败而受阻。现有范式（如GraphRAG、NL2SQL）缺乏语义锚定和可审计执行能力。我们提出HEAR，一种基于分层超图本体的企业智能体推理框架。其基础图层虚拟化具溯源能力的数据接口，超边层编码多值业务规则与流程协议。通过证据驱动的推理循环，HEAR动态编排本体工具，实现无需重训的结构化多跳分析。在供应链任务中的评估显示，其在订单履约阻塞根因分析中达到最高94.7%的准确率。关键优势在于自适应效率：利用流程超边降低令牌开销，借助拓扑探索保障复杂查询的严谨正确性。通过匹配专有模型性能与开源底座，并自动化人工诊断，HEAR为企事业智能构建了可扩展、可审计的基础。
原文摘要 · Abstract (English)
Applying Large Language Models (LLMs) to heterogeneous enterprise systems is hindered by hallucinations and failures in multi-hop, n-ary reasoning. Existing paradigms (e.g., GraphRAG, NL2SQL) lack the semantic grounding and auditable execution required for these complex environments. We introduce HEAR, an enterprise agentic reasoner built on a Stratified Hypergraph Ontology. Its base Graph Layer virtualizes provenance-aware data interfaces, while the Hyperedge Layer encodes n-ary business rules and procedural protocols. Operating an evidence-driven reasoning loop, HEAR dynamically orchestrates ontology tools for structured multi-hop analysis without requiring LLM retraining. Evaluations on supply-chain tasks, including order fulfillment blockage root cause analysis (RCA), show HEAR achieves up to 94.7% accuracy. Crucially, HEAR demonstrates adaptive efficiency: utilizing procedural hyperedges to minimize token costs, while leveraging topological exploration for rigorous correctness on complex queries. By matching proprietary model performance with open-weight backbones and automating manual diagnostics, HEAR establishes a scalable, auditable foundation for enterprise intelligence.
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