GVD: Governed Versioning and Deduplication for Document Repositories
cs.AI
Submitted: 2026-09-15
Updated: 2026-09-15
License: http://creativecommons.org/licenses/by/4.0/
The gist: Document repositories evolve continuously.
Terminology
Abstract
Document repositories evolve continuously. Guidelines and policies are revised, superseded, and re-uploaded, so the same content recurs in different wording and newer versions refine or contradict earlier ones. These inconsistencies belong to the growing collection rather than to any single document, yet existing work treats versioning, duplicate detection, and contradiction detection as isolated pairwise tasks and stops once a pair is labeled. We present GVD (Governed Versioning and Deduplication), a framework that unifies cross-document version linking with rule-level conflict resolution under an auditable update policy. Incoming documents are assigned to version families through bidirectional rule alignment, and their rules are compared against the family memory to identify duplicates, contradictions, asymmetric refinements, and new knowledge, with Counterfactual Span Probing (CSP) resolving related pairs that inference misclassifies as neutral. Relation-specific policies suppress duplicates and escalate only consequential changes for review, retaining version lineage as an audit trail. The pipeline runs fully locally, with no large language model. On 120 enterprise documents processed as 140 ingestions across 59 version families, GVD reaches an F1 of 0.97 for version-family construction and 0.94 for rule-level consistency, with CSP raising rule consistency from 0.90 to 0.94.
Sources
- Automatically detecting the conflicts between software requirements based on finer semantic analysis
- Transfer learning for conflict and duplicate detection in software requirement pairs
- LegalWiz: A Multi-Agent Generation Framework for Contradiction Detection in Legal Documents
- GUIDE: Governed Unified Intelligence for Document-to-Artifact Generation in Enterprise Settings
- NewsEdits 2.0: Learning the Intentions Behind Updating News
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