mlsanityData to 14 Oct 2026

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

arXiv 2608.12133 ↗First posted 12 Aug 2026cs.AIcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 10 of its 18 references that are in the map. First posted on arXiv 12 Aug 2026.

Builds on

10 of 18 references.

References · 10

  1. 2024Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolutioncited by 4,234
  2. 2025Qwen3-VL Technical Reportcited by 2,793
  3. 2023AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversationcited by 1,988
  4. 2022Unified Structure Generation for Universal Information Extractioncited by 221
  5. 2023GoLLIE: Annotation Guidelines improve Zero-Shot Information-Extractioncited by 119
  6. 2025Docling: An Efficient Open-Source Toolkit for AI-driven Document Conversioncited by 63
  7. 2023LMDX: Language Model-based Document Information Extraction and Localizationcited by 37
  8. 2024The Design of an LLM-powered Unstructured Analytics Systemcited by 20
  9. 2024MMDocBench: Benchmarking Large Vision-Language Models for Fine-Grained Visual Document Understandingcited by 19
  10. 2026Position: Early-Stage Quality Assurance in Annotation Pipelines Is More Cost-Effective Than Late-Stage Validationcited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

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  1. 2026SAGE: Governed Artifact Generation from Enterprise Guidelines
  2. 2026ExtractBench: A Benchmark for Schema-Guided Enterprise Document Extraction
  3. 2026Frustratingly Easy Agentic RAG via Portable Skills: A Case Study in Customer Support
  4. 2026Fidelity Is Not Enough: Dispatch-Level Instrumentation for Agentic Datasheet Extraction
  5. 2024Spider2-V: How Far Are Multimodal Agents From Automating Data Science and Engineering Workflows?
  6. 2026Agent-Based Software Artifact Evaluation
  7. 2025AI Agents-as-Judge: Automated Assessment of Accuracy, Consistency, Completeness and Clarity for Enterprise Documents
  8. 2026IDP AutoOpt: Agent-Driven Optimization of Document Processing Pipeline Configurations