Hypergraph Enterprise Agentic Reasoner over Heterogeneous Business Systems
Summary
HEAR is an enterprise agentic reasoner using a Stratified Hypergraph Ontology to perform multi-hop reasoning over heterogeneous business systems, achieving up to 94.7% accuracy on supply-chain tasks.
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# Hypergraph Enterprise Agentic Reasoner over Heterogeneous Business Systems Source: [https://arxiv.org/abs/2605.14259](https://arxiv.org/abs/2605.14259) Authors:[Ling Wang](https://arxiv.org/search/cs?searchtype=author&query=Wang,+L),[Songnan Liu](https://arxiv.org/search/cs?searchtype=author&query=Liu,+S),[Jianan Wang](https://arxiv.org/search/cs?searchtype=author&query=Wang,+J),[Cheng Cheng](https://arxiv.org/search/cs?searchtype=author&query=Cheng,+C),[Xin Liu](https://arxiv.org/search/cs?searchtype=author&query=Liu,+X),[Yihan Zhu](https://arxiv.org/search/cs?searchtype=author&query=Zhu,+Y),[Enyu Li](https://arxiv.org/search/cs?searchtype=author&query=Li,+E),[Yu Xiao](https://arxiv.org/search/cs?searchtype=author&query=Xiao,+Y),[Jiangyong Xie](https://arxiv.org/search/cs?searchtype=author&query=Xie,+J),[Duogong Yan](https://arxiv.org/search/cs?searchtype=author&query=Yan,+D),[Jiangyi Chen](https://arxiv.org/search/cs?searchtype=author&query=Chen,+J) [View PDF](https://arxiv.org/pdf/2605.14259) > Abstract: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\. ## Submission history From: Xin Liu \[[view email](https://arxiv.org/show-email/37917084/2605.14259)\] **\[v1\]**Thu, 14 May 2026 01:57:59 UTC \(352 KB\)
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