MIDR: Enrichment-Augmented Indexing for Multimodal Document Retrieval
Retrieval over visually rich documents has a representation problem: important content often lives in tables, charts, figures, and layout relations that plain OCR linearizes, corrupts, or omits. ColPali-family visual retrievers address this with patch-level multi-vector indexes and late-interaction scoring, keeping image-derived retrieval on the query-time serving path. We introduce MIDR (Multimodal Indexing for Document Retrieval), a training-free framework for enrichment-augmented indexing that shifts multimodal reasoning to index time. During ingestion, a multimodal LLM converts rendered pages into verified textual fields that are indexed with BM25F and optionally fused with dense retr...
| Authors | Debanjan Mahata, Atharva Tendle, Daniel Preotiuc-Pietro et al. |
|---|---|
| Primary category | cs.IR |
| Published | 1 September 2026 |
| Last updated at source | 1 September 2026 |
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