arXiv:2605.14259v2 Announce Type: replace 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-
Large Language Models are rapidly maturing, necessitating more robust and auditable integration methods into complex enterprise environments beyond basic RAG or NL2SQL solutions.
This development addresses critical limitations of current LLM applications in enterprise, particularly regarding accuracy, explainability, and the handling of complex, multi-system interactions.
Enterprise LLM integration shifts from simplified data retrieval and query generation to agentic, auditable reasoning over heterogeneous systems using structured semantic grounding.
- · Enterprise AI platform providers
- · Companies with complex internal systems
- · LLM developers focused on reliability
- · Providers of simplistic RAG solutions
- · Systems integrators lacking deep semantic AI expertise
Increased adoption and trustworthiness of LLM-powered solutions in high-stakes enterprise functions.
New standards and architectures emerge for building enterprise-grade AI agents, emphasizing explainability and auditability.
White-collar workflows are significantly automated and optimized across complex, interconnected business processes, leading to substantial productivity gains.
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Read at arXiv cs.AI