From Business Events to Auditable Decisions: Ontology-Governed Graph Simulation for Enterprise AI
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arXiv
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| Autores principales: | , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866914462860050432 |
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| author | Zhu, Hongyin Liang, Jinming Hou, Mengjun Tang, Ruifan Zhu, Xianbin Yang, Jingyuan Mao, Yuanman Wu, Feng |
| author_facet | Zhu, Hongyin Liang, Jinming Hou, Mengjun Tang, Ruifan Zhu, Xianbin Yang, Jingyuan Mao, Yuanman Wu, Feng |
| contents | Existing LLM-based agent systems share a common architectural failure: they answer from the unrestricted knowledge space without first simulating how active business scenarios reshape that space for the event at hand -- producing decisions that are fluent but ungrounded and carrying no audit trail. We present LOM-action, which equips enterprise AI with \emph{event-driven ontology simulation}: business events trigger scenario conditions encoded in the enterprise ontology~(EO), which drive deterministic graph mutations in an isolated sandbox, evolving a working copy of the subgraph into the scenario-valid simulation graph $G_{\text{sim}}$; all decisions are derived exclusively from this evolved graph. The core pipeline is \emph{event $\to$ simulation $\to$ decision}, realized through a dual-mode architecture -- \emph{skill mode} and \emph{reasoning mode}. Every decision produces a fully traceable audit log. LOM-action achieves 93.82% accuracy and 98.74% tool-chain F1 against frontier baselines Doubao-1.8 and DeepSeek-V3.2, which reach only 24--36% F1 despite 80% accuracy -- exposing the \emph{illusive accuracy} phenomenon. The four-fold F1 advantage confirms that ontology-governed, event-driven simulation, not model scale, is the architectural prerequisite for trustworthy enterprise decision intelligence. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_08603 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | From Business Events to Auditable Decisions: Ontology-Governed Graph Simulation for Enterprise AI Zhu, Hongyin Liang, Jinming Hou, Mengjun Tang, Ruifan Zhu, Xianbin Yang, Jingyuan Mao, Yuanman Wu, Feng Artificial Intelligence Computation and Language Existing LLM-based agent systems share a common architectural failure: they answer from the unrestricted knowledge space without first simulating how active business scenarios reshape that space for the event at hand -- producing decisions that are fluent but ungrounded and carrying no audit trail. We present LOM-action, which equips enterprise AI with \emph{event-driven ontology simulation}: business events trigger scenario conditions encoded in the enterprise ontology~(EO), which drive deterministic graph mutations in an isolated sandbox, evolving a working copy of the subgraph into the scenario-valid simulation graph $G_{\text{sim}}$; all decisions are derived exclusively from this evolved graph. The core pipeline is \emph{event $\to$ simulation $\to$ decision}, realized through a dual-mode architecture -- \emph{skill mode} and \emph{reasoning mode}. Every decision produces a fully traceable audit log. LOM-action achieves 93.82% accuracy and 98.74% tool-chain F1 against frontier baselines Doubao-1.8 and DeepSeek-V3.2, which reach only 24--36% F1 despite 80% accuracy -- exposing the \emph{illusive accuracy} phenomenon. The four-fold F1 advantage confirms that ontology-governed, event-driven simulation, not model scale, is the architectural prerequisite for trustworthy enterprise decision intelligence. |
| title | From Business Events to Auditable Decisions: Ontology-Governed Graph Simulation for Enterprise AI |
| topic | Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2604.08603 |