# From Business Events to Auditable Decisions: Ontology-Governed Graph Simulation for Enterprise AI

Source: [From Business Events to Auditable Decisions: Ontology-Governed Graph Simulation for Enterprise AI](<https://www.sota2.com/research/paper/69dc788a7d0172140ec78ab0>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-04-14T05:00:04.234Z

Authors: Hongyin Zhu, Jinming Liang, Mengjun Hou, Ruifan Tang, Xianbin Zhu, Jingyuan Yang, Yuanman Mao, Feng Wu

Published: 2026-04-08T06:07:48.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Graph Reasoning on Scenario-Simulation Graph Dataset](<https://www.sota2.com/research/sota/graph-reasoning-on-scenario-simulation-graph-dataset.md>): Graph Reasoning; Scenario-Simulation Graph Dataset; Connectivity: 100.

- [Tool-augmented Graph Reasoning on Scenario-Simulation and Graph Algorithms Suite](<https://www.sota2.com/research/sota/tool-augmented-graph-reasoning-on-scenario-simulation-and-graph-algorithms-suite.md>): Tool-augmented Graph Reasoning; Scenario-Simulation and Graph Algorithms Suite; Connectivity: 100.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2604.08603>)

[PDF](<https://arxiv.org/pdf/2604.08603.pdf>)