Managing corporate document repositories without large language models: GVD framework settles policy conflicts and version lineages locally

Document repositories in corporate environments face a persistent dilemma that goes beyond mere file storage, as policies, regulations and procedural instructions undergo successive amendments and periodic replacement, leading to repeated content in varying wording and the emergence of new versions that explicitly revise or contradict earlier ones. These contradictions do not pertain to a single document but arise across the growing collection as a whole, while traditional approaches treat version tracking, duplicate detection and inconsistency discovery as isolated binary tasks that stop once a pair is labeled. Confronted with this structural shortcoming, researchers have proposed the GVD framework for version management and governed deduplication, offering an architecture that unifies version linking across documents and resolves conflicts at the level of individual rules according to a fully auditable update policy.

The new framework relies on assigning incoming documents to defined version families through a bidirectional rule-alignment mechanism, where extracted rules are compared against the cumulative family memory to classify relationships precisely, whether they are explicit duplicates, direct contradictions, asymmetric revisions, or additions of emerging knowledge. To overcome the shortcomings of traditional inference that sometimes misclassifies linked pairs as neutral cases, the team employed a reality-aligned extension probing technique to audit and resolve fine-grained relationships.This design eliminates the need to invoke large language models entirely, as the processing pipeline runs fully locally within a closed infrastructure, relying on directed governance that suppresses identical copies and escalates only substantive changes to human review while preserving a historical audit trail.

Experimental results demonstrated the efficiency of this pipeline on 120 corporate documents processed through 140 ingestion operations distributed across 59 version families. The system achieved a F1 score of 0.97 for building version families and 0.94 for maintaining regulatory rule consistency, with the extension probing technique raising rule-consistency accuracy from 0.90 to 0.94, confirming that specialized local algorithms can enforce knowledge coherence without draining computational resources or relying on external generation systems.

As a follower of the technical trajectory, I see this shift as highly significant for financial institutions, government agencies and tech teams in the Gulf, Egypt and the Arab Levant, as it reshapes infrastructure decisions and actual operating costs. Banks and regulated firms in the region manage thousands of regulations and circulars that are continuously updated, and integrating these accumulated documents into common retrieval systems often produces misleading answers because older policies clash with newer ones.Relying on a local audit architecture gives institutions strict sovereign protection for their sensitive data, reduces the cost of generative inference, and ensures the delivery of consistent answers that accurately follow the modification lineage.Therefore, review your knowledge-management system before resorting to costly cloud models, and focus on locally resolving rule-level conflicts to isolate regulatory inconsistencies and provide auditors and compliance officers with a reliable audit log.