# Overview of the TREC 2025 Retrieval Augmented Generation (RAG) Track

arXiv: https://arxiv.org/abs/2603.09891 · HTML: https://mlsanity.com/paper/2603.09891/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in RAG pipeline design and evaluation, one of 15 directions in Retrieval-augmented generation.
- Builds on 7 of its 11 references that are in the map.
- First posted on arXiv 10 Mar 2026.

## Placed in

- Retrieval-augmented generation / [RAG pipeline design and evaluation](https://mlsanity.com/rag/rag-pipeline-design-and-evaluation/)

## Builds on (most cited first, 7 of 7)

- [Evaluating Verifiability in Generative Search Engines](https://mlsanity.com/paper/2304.09848/) (2023)
- [SPLADE-v3: New baselines for SPLADE](https://mlsanity.com/paper/2403.06789/) (2024)
- [UMBRELA: UMbrela is the (Open-Source Reproduction of the) Bing RELevance Assessor](https://mlsanity.com/paper/2406.06519/) (2024)
- [Ragnar"ok: A Reusable RAG Framework and Baselines for TREC 2024 Retrieval-Augmented Generation Track](https://mlsanity.com/paper/2406.16828/) (2024)
- [Embedding And Clustering Your Data Can Improve Contrastive Pretraining](https://mlsanity.com/paper/2407.18887/) (2024)
- [RankLLM: A Python Package for Reranking with LLMs](https://mlsanity.com/paper/2505.19284/) (2025)
- [FreshStack: Building Realistic Benchmarks for Evaluating Retrieval on Technical Documents](https://mlsanity.com/paper/2504.13128/) (2025)

## Built on by (most cited first, 3 of 3)

- [Predicting Partial Answer Quality and Utility in Agentic Retrieval-Augmented Generation](https://mlsanity.com/paper/2609.16453/) (2026)
- [Sovereign Context Protocol: An Open Attribution Layer for Human-Generated Content in the Age of Large Language Models](https://mlsanity.com/paper/2603.27094/) (2026)
- [Assessing the Downstream Utility of Evidence-Aware Retrieval in RAG](https://mlsanity.com/paper/2608.26379/) (2026)

## Abstract (arXiv)

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.