用事件驱动的本体模拟，让企业AI决策有依据、可审计。
From Business Events to Auditable Decisions: Ontology-Governed Graph Simulation for Enterprise AI
- 基于业务事件触发本体模拟，生成可信的决策图谱。
- 准确率93.82%，工具链F1达98.74%，远超基线模型。
- 适合需要可追溯、高可信度决策的企业AI系统。
现有基于大模型的智能体系统存在共性缺陷：在未模拟业务场景如何重塑知识空间的情况下直接作答，导致决策流畅却无依据，且无审计痕迹。本文提出LOM-action，引入事件驱动的本体模拟机制：业务事件触发企业本体（EO）中的场景条件，在隔离沙箱中驱动确定性图结构变异，将子图演化为符合场景的仿真图 $G_{ ext{sim}}$，所有决策均仅来源于此图。核心流程为“事件→模拟→决策”，通过双模式架构（技能模式与推理模式）实现。每项决策生成完整可追溯的审计日志。实验显示，LOM-action在准确率上达93.82%，工具链F1为98.74%，显著优于前沿基线Doubao-1.8和DeepSeek-V3.2（准确率80%但工具链F1仅24–36%），揭示了‘虚假准确率’现象。四倍的F1优势证明，企业级可信决策智能的关键在于本体引导的事件驱动模拟，而非模型规模。
原文摘要 · Abstract (English)
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.
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