# Retrieval-Augmented Search for Large-Scale Map Collections with ColPali

arXiv: https://arxiv.org/abs/2510.25718 · HTML: https://mlsanity.com/paper/2510.25718/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Multimodal RAG, one of 15 directions in Retrieval-augmented generation.
- Builds on 8 of its 21 references that are in the map.
- First posted on arXiv 29 Oct 2025.

## Placed in

- Retrieval-augmented generation / [Multimodal RAG](https://mlsanity.com/rag/multimodal-rag/)

## Builds on (most cited first, 8 of 8)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [PaliGemma: A versatile 3B VLM for transfer](https://mlsanity.com/paper/2407.07726/) (2024)
- [Long-CLIP: Unlocking the Long-Text Capability of CLIP](https://mlsanity.com/paper/2403.15378/) (2024)
- [ArtSeek: Deep artwork understanding via multimodal in-context reasoning and late interaction retrieval](https://mlsanity.com/paper/2507.21917/) (2025)
- [Digital Collections Explorer: An Open-Source, Multimodal Viewer for Searching Digital Collections](https://mlsanity.com/paper/2507.00961/) (2025)
- [Integrating Visual and Textual Inputs for Searching Large-Scale Map Collections with CLIP](https://mlsanity.com/paper/2410.01190/) (2024)
- [RiverEcho: Real-Time Interactive Digital System for Ancient Yellow River Culture](https://mlsanity.com/paper/2506.21865/) (2025)

## Abstract (arXiv)

Multimodal approaches have shown great promise for searching and navigating digital collections held by libraries, archives, and museums. In this paper, we introduce map-RAS: a retrieval-augmented search system for historic maps. In addition to introducing our framework, we detail our publicly-hosted demo for searching 101,233 map images held by the Library of Congress. With our system, users can multimodally query the map collection via ColPali, summarize search results using Llama 3.2, and upload their own collections to perform inter-collection search. We articulate potential use cases for archivists, curators, and end-users, as well as future work with our system in both machine learning and the digital humanities. Our demo can be viewed at: http://www.mapras.com.