LITTA: Late-Interaction and Test-Time Alignment for Visually-Grounded Multimodal Retrieval
Retrieving relevant evidence from visually rich documents such as textbooks, technical reports, and manuals is challenging due to long context, complex layouts, and weak lexical overlap between user questions and supporting pages. We propose LITTA, a query-expansion-centric retrieval framework for evidence page retrieval that improves multimodal document retrieval without retriever retraining. Given a user query, LITTA generates complementary query variants using a large language model and retrieves candidate pages for each variant using a frozen vision retriever with late-interaction scoring. Candidates from expanded queries are then aggregated through reciprocal rank fusion to improve e...
| Authors | Seonok Kim |
|---|---|
| Primary category | cs.IR |
| Published | 10 March 2026 |
| Last updated at source | 10 March 2026 |
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