The second edition of the TREC Retrieval Augmented Generation (RAG) Track advances research on systems that integrate retrieval and generation to address complex, real-world information needs. Building on the foundation of the inaugural 2024 track, this year's challenge introduces long, multi-sentence narrative queries to better reflect the deep search task with the growing demand for reasoning-driven responses. Participants are tasked with designing pipelines that combine retrieval and generation while ensuring transparency and factual grounding. The track leverages the MS MARCO V2.1 corpus and employs a multi-layered evaluation framework encompassing relevance assessment, response completeness, attribution verification, and agreement analysis. By emphasizing multi-faceted narratives and attribution-rich answers from over 150 submissions this year, the TREC 2025 RAG Track aims to foster innovation in creating trustworthy, context-aware systems for retrieval augmented generation.
@article{arxiv.2603.09891,
title = {Overview of the TREC 2025 Retrieval Augmented Generation (RAG) Track},
author = {Shivani Upadhyay and Nandan Thakur and Ronak Pradeep and Nick Craswell and Daniel Campos and Jimmy Lin},
journal= {arXiv preprint arXiv:2603.09891},
year = {2026}
}