CorpusMap provides a scalable, explainable way to enhance multi-document retrieval by grounding navigation in a structured entity graph rather than relying on flat or loosely connected documents.

Common workarounds like folder-based aggregation and unconstrained LLM wikis perform poorly in experiments, increasing token usage and reducing correctness due to misaligned structure and hallucinations.

Initial indexing costs can be offset by using cheaper builder models to construct the map, while smaller models enable cost-effective maintenance and incremental updates save a large majority of tokens compared to full rebuilds without sacrificing quality.

In practical tests, CorpusMap dramatically cuts token usage and boosts accuracy: EnterpriseRAG-Bench with GPT-5.5 saw tokens drop from 206,500 to 88,100 (57% reduction) and correctness rise from 62.1% to 73.8%; WixQA saw tokens fall from 337,200 to 74,500 (78% reduction) with accuracy improving from 67.5% to 70.7%.

For local workflows, avoid relying on frontier models to fix unstructured storage, steer clear of folder-centric indexes, and explicitly extract and maintain backlinks for entities to prevent wandering searches.

The approach shows robust improvements across seven model families, consistently outperforming blind document traversal when explicit entity anchors guide navigation.

A common inefficiency in multi-file search is token waste on blind navigation before locating relevant evidence, a problem CorpusMap addresses.

CorpusMap restructures the corpus around recurring named entities—people, projects, systems, vendors, and code modules—creating Entity Pages with aggregated facts and backlinks, forming a bipartite graph between entities and documents.