GUIDE：企业场景下从文档到制品生成的受控统一智能
GUIDE: Governed Unified Intelligence for Document-to-Artifact Generation in Enterprise Settings
- Centific Research（森蒂菲克研究院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对企业指南文档手动处理效率低的问题，提出基于共享版本化规则库的多智能体框架GUIDE，在120份真实文档上实现96%成功率，大幅提升文档到制品的生成效率。
AI中文摘要：
企业指南文档具有异构性和多模态特点，包含叙事文本、复杂表格和嵌入图像。现有大语言模型（LLM）和视觉语言模型（VLM）系统存在内容幻觉、表格结构退化问题，且缺乏从提取到验证及制品生成的受控工作流，导致企业需手动处理，每份文档耗时2-3天。为解决该问题，本文提出GUIDE，这是一个基于共享版本化规则库的受控多智能体框架，具备经模式验证的智能体间合约和端到端溯源跟踪能力。六个专用智能体分别负责解析、VLM驱动的提取、一致性检查、评估、人在回路（HITL）升级及角色定制化制品合成。在120份真实企业指南文档上评估显示，GUIDE实现96%的文档成功率，提取3896条规则，其中71.4%自动获批，生成812份可部署制品，将每份文档的处理周转时间缩短至40-125分钟。
英文摘要：
Enterprise guideline documents are heterogeneous and multimodal, combining narrative text, complex tables, and embedded images. Existing LLM and VLM systems face hallucinated content, table structure degradation, and lack governed workflows extending beyond extraction to validation and artifact generation. This leaves enterprises to perform this manually, consuming 2-3 days per document. To address this, we introduce GUIDE, a governed multi-agent framework built on a shared versioned rule store with schema-validated inter-agent contracts and end-to-end provenance tracking. Six specialized agents handle parsing, VLM-driven extraction, consistency checking, evaluation, human-in-the-loop (HITL) escalation, and persona-tailored artifact synthesis. Evaluated on 120 real-world enterprise guideline documents, GUIDE achieves 96% document success, extracts 3,896 rules with 71.4% auto-approved, produces 812 deployment-ready artifacts, and reduces turnaround to 40-125 minutes per document.
