arXiv：cs.AI（全量分类）· Terry Dorsey, Kevin Huggins·· 5 小时前AI 评分24
企业表征简化（ERS）：降低企业 AI 的表征复杂度
Enterprise Representation Simplification (ERS): Reducing Representational Complexity for Enterprise AI
AI 导读
论文提出企业表征简化（ERS）与企业表征复杂度（ERC）模型，ERC 从表征对象、交互、行为、支撑来源四个维度刻画表征范围，可在表征层与任务层比较复杂度，并区分架构简化与检索优化。
正文
Abstract:Enterprise information is represented through artifacts shaped by applications, projects, technologies, organizational boundaries, and local requirements. These structures accumulate over time, creating representational complexity that must be maintained by the enterprise and interpreted by information consumers and AI systems. This paper introduces Enterprise Representation Simplification (ERS) as reducing unnecessary representational complexity while preserving required information within a defined scope, and Enterprise Representation Complexity (ERC), a representation-neutral model for comparing complexity across representation states.
ERC characterizes representational extent through four dimensions: Representation Objects, Interactions, Behaviors, and Supporting Sources. Objects, Interactions, and Behaviors form dependent categories, while Supporting Sources characterize representation exposure. ERC is defined at representation and task levels, enabling comparison and distinguishing architectural simplification from retrieval optimization.
The paper develops two consequences of ERS. First, representational structures create lifecycle obligations for maintenance, governance, dependencies, change, enhancement, and operation. An economic model distinguishes recurring global representation cost, recurring task-level cost, and one-time transformation cost, enabling evaluation over a defined time horizon. Second, reductions in task-level ERC reduce the representational extent an AI system must identify, relate, and interpret. Text-to-SQL research provides evidence that reduced schema and reasoning complexity can improve reasoning accuracy.
ERC is not a universal complexity, performance, or cost metric. It provides measurable architectural variables for comparing representational alternatives, transformation effects, economic outcomes, and AI reasoning performance.
| Subjects: | Artificial Intelligence (cs.AI); Information Retrieval (cs.IR) |
| Cite as: | arXiv:2610.00791 [cs.AI] |
| (or arXiv:2610.00791v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.00791 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Terry Dorsey [view email]
[v1]
Wed, 30 Sep 2026 22:31:40 UTC (477 KB)
来源：arXiv：cs.AI（全量分类） · arxiv.org