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Source note3 min read

GUIDE: Governed Unified Intelligence for Document-to-Artifact Generation in Enterprise Settings

Source

arXiv

arxiv.org/abs/2608.12133v1

Read the source

Source

  • Source name: arXiv
  • Source kind: paper
  • Authors / organization: Shivali Dalmia, Sumukha Thoppanahalli, Mohammadreza Sediqin, Abhishek Mukherji
  • Venue: arXiv: cs.AI
  • Published: 2026-08-12
  • Collected: 2026-08-13
  • URL: http://arxiv.org/abs/2608.12133v1
  • PDF: https://arxiv.org/pdf/2608.12133v1
  • DOI: UNKNOWN
  • Registry group: academic_apis_and_preprints
  • Evidence role: canonical_academic_index
  • Language: en
  • Country / region: UNKNOWN

Plain-Language Brief

The paper introduces GUIDE, a framework for generating enterprise guideline documents that are often complex and multimodal (combining text, tables, images). Existing systems struggle with maintaining the structure of these documents and often produce inaccurate content. GUIDE aims to address this issue by using a multi-agent system with six specialized agents that handle different aspects of document processing, such as parsing, extraction, validation, and artifact synthesis. The framework was tested on 120 real-world documents and showed promising results, achieving high success rates and significantly reducing the time required for document generation.

What the Document Says

  • Enterprise guideline documents are multimodal and combine narrative text, complex tables, and images.
  • Existing LLM and VLM systems struggle with maintaining the structure of these documents and often produce inaccurate content.
  • The paper introduces GUIDE, a framework that uses six specialized agents to handle parsing, extraction, validation, human-in-the-loop escalation, and artifact synthesis for enterprise guideline documents.
  • GUIDE was tested on 120 real-world enterprise guideline documents.
  • The evaluation shows that GUIDE achieves a high success rate, extracts rules with a high auto-approval percentage, produces deployment-ready artifacts, and significantly reduces the turnaround time for document generation.

Key Concepts Mentioned

  • Enterprise guideline documents
  • LLM
  • VLM
  • multi-agent framework
  • shared versioned rule store
  • schema-validated inter-agent contracts
  • end-to-end provenance tracking
  • parsing
  • extraction
  • validation
  • human-in-the-loop
  • artifact synthesis

Claims Made by the Document

  • Existing systems struggle with maintaining the structure of enterprise guideline documents.
  • GUIDE addresses this issue by using a multi-agent system.
  • The evaluation shows that GUIDE achieves a high success rate.

Evidence Used

  • The text states that existing LLM and VLM systems face hallucinated content, table structure degradation, and lack governed workflows extending beyond extraction to validation and artifact generation.
  • The text mentions that GUIDE was evaluated on 120 real-world enterprise guideline documents and showed promising results.

Methods / Approach

  • The use of a multi-agent system with six specialized agents for handling different aspects of document processing.
  • The use of a shared versioned rule store, schema-validated inter-agent contracts, and end-to-end provenance tracking.

Limitations Stated by the Authors

The paper does not provide information on potential limitations or challenges that were encountered during the development or testing of GUIDE.

Limitations Noted by the Agent

Agent interpretation — not from the source.

It is unknown if there are any limitations or challenges with the framework that were not addressed by the authors. The agent should investigate further to determine potential issues and constraints.

Open Questions

  • What specific challenges did the authors face during the development and testing of GUIDE?
  • How does GUIDE handle situations where the input document is incomplete or inconsistent?

Notes for Future Q&A

May include interpretation. Labeled where it does.

The paper provides promising results for GUIDE but does not discuss potential limitations or challenges. It may be useful to investigate further to gain a more complete understanding of the framework.

Raw Abstract / Excerpt

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.