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    <abstract>In this paper, we introduce CollEx, an innovative multimodal agentic Retrieval-Augmented Generation (RAG) system designed to enhance interactive exploration of extensive scientific collections. Given the overwhelming volume and inherent complexity of scientific collections, conventional search systems often lack necessary intuitiveness and interactivity, presenting substantial barriers for learners, educators, and researchers. CollEx addresses these limitations by employing state-of-the-art Large Vision-Language Models (LVLMs) as multimodal agents accessible through an intuitive chat interface. By abstracting complex interactions via specialized agents equipped with advanced tools, CollEx facilitates curiosity-driven exploration, significantly simplifying access to diverse scientific collections and records therein. Our system integrates textual and visual modalities, supporting educational scenarios that are helpful for teachers, pupils, students, and researchers by fostering independent exploration as well as scientific excitement and curiosity. Furthermore, CollEx serves the research community by discovering interdisciplinary connections and complementing visual data. We illustrate the effectiveness of our system through a proof-of-concept application containing over 64,000 unique records across 32 collections from a local scientific collection from a public university.</abstract>
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%0 Conference Proceedings
%T CollEX – A Multimodal Agentic RAG System Enabling Interactive Exploration of Scientific Collections
%A Schneider, Florian
%A Baba Ahmadi, Narges
%A Baba Ahmadi, Niloufar
%A Vogel, Iris
%A Semmann, Martin
%A Biemann, Chris
%Y Kriz, Reno
%Y Murray, Kenton
%S Proceedings of the 1st Workshop on Multimodal Augmented Generation via Multimodal Retrieval (MAGMaR 2025)
%D 2025
%8 August
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-280-0
%F schneider-etal-2025-collex
%X In this paper, we introduce CollEx, an innovative multimodal agentic Retrieval-Augmented Generation (RAG) system designed to enhance interactive exploration of extensive scientific collections. Given the overwhelming volume and inherent complexity of scientific collections, conventional search systems often lack necessary intuitiveness and interactivity, presenting substantial barriers for learners, educators, and researchers. CollEx addresses these limitations by employing state-of-the-art Large Vision-Language Models (LVLMs) as multimodal agents accessible through an intuitive chat interface. By abstracting complex interactions via specialized agents equipped with advanced tools, CollEx facilitates curiosity-driven exploration, significantly simplifying access to diverse scientific collections and records therein. Our system integrates textual and visual modalities, supporting educational scenarios that are helpful for teachers, pupils, students, and researchers by fostering independent exploration as well as scientific excitement and curiosity. Furthermore, CollEx serves the research community by discovering interdisciplinary connections and complementing visual data. We illustrate the effectiveness of our system through a proof-of-concept application containing over 64,000 unique records across 32 collections from a local scientific collection from a public university.
%R 10.18653/v1/2025.magmar-1.2
%U https://aclanthology.org/2025.magmar-1.2/
%U https://doi.org/10.18653/v1/2025.magmar-1.2
%P 18-39

Markdown (Informal)

[CollEX – A Multimodal Agentic RAG System Enabling Interactive Exploration of Scientific Collections](https://aclanthology.org/2025.magmar-1.2/) (Schneider et al., MAGMaR 2025)

• CollEX – A Multimodal Agentic RAG System Enabling Interactive Exploration of Scientific Collections (Schneider et al., MAGMaR 2025)

ACL

• Florian Schneider, Narges Baba Ahmadi, Niloufar Baba Ahmadi, Iris Vogel, Martin Semmann, and Chris Biemann. 2025. CollEX – A Multimodal Agentic RAG System Enabling Interactive Exploration of Scientific Collections. In Proceedings of the 1st Workshop on Multimodal Augmented Generation via Multimodal Retrieval (MAGMaR 2025), pages 18–39, Vienna, Austria. Association for Computational Linguistics.