VersionRAG: Version-Aware Retrieval-Augmented Generation for Evolving Documents

• URL: http://arxiv.org/abs/2510.08109v1
• Date: Thu, 09 Oct 2025 11:48:58 GMT
• Title: VersionRAG: Version-Aware Retrieval-Augmented Generation for Evolving Documents
• Abstract summary: Existing approaches achieve only 58-64% accuracy on version-sensitive questions.<n>We present VersionRAG, a version-aware RAG framework that explicitly models document evolution.<n>During retrieval, VersionRAG routes queries through specialized paths based on intent classification.
• Score: 2.657536539253924
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Retrieval-Augmented Generation (RAG) systems fail when documents evolve through versioning-a ubiquitous characteristic of technical documentation. Existing approaches achieve only 58-64% accuracy on version-sensitive questions, retrieving semantically similar content without temporal validity checks. We present VersionRAG, a version-aware RAG framework that explicitly models document evolution through a hierarchical graph structure capturing version sequences, content boundaries, and changes between document states. During retrieval, VersionRAG routes queries through specialized paths based on intent classification, enabling precise version-aware filtering and change tracking. On our VersionQA benchmark-100 manually curated questions across 34 versioned technical documents-VersionRAG achieves 90% accuracy, outperforming naive RAG (58%) and GraphRAG (64%). VersionRAG reaches 60% accuracy on implicit change detection where baselines fail (0-10%), demonstrating its ability to track undocumented modifications. Additionally, VersionRAG requires 97% fewer tokens during indexing than GraphRAG, making it practical for large-scale deployment. Our work establishes versioned document QA as a distinct task and provides both a solution and benchmark for future research.

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