mlsanityData to 7 Oct 2026

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

arXiv 2603.09891 ↗First posted 10 Mar 2026cs.IRcited by 3

On the map: Retrieval-augmented generation → RAG pipeline design and evaluation

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.

Key facts. Cited by 3 papers in the map (data through 7 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.

Builds on

7 of 11 references.

Closest ancestors · 1

Same direction, newest first

  1. 2024Ragnar"ok: A Reusable RAG Framework and Baselines for TREC 2024 Retrieval-Augmented Generation Track24 Jun

Other references · 6

  1. 2023Evaluating Verifiability in Generative Search Enginescited by 297
  2. 2024SPLADE-v3: New baselines for SPLADEcited by 69
  3. 2024UMBRELA: UMbrela is the (Open-Source Reproduction of the) Bing RELevance Assessorcited by 41
  4. 2024Embedding And Clustering Your Data Can Improve Contrastive Pretrainingcited by 17
  5. 2025FreshStack: Building Realistic Benchmarks for Evaluating Retrieval on Technical Documentscited by 15
  6. 2025RankLLM: A Python Package for Reranking with LLMscited by 15

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 3.

3 papers.

  1. 2026Predicting Partial Answer Quality and Utility in Agentic Retrieval-Augmented Generationcited by 1
  2. 2026Sovereign Context Protocol: An Open Attribution Layer for Human-Generated Content in the Age of Large Language Modelscited by 0
  3. 2026Assessing the Downstream Utility of Evidence-Aware Retrieval in RAGcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Support Evaluation for the TREC 2024 RAG Track: Comparing Human versus LLM Judges
  2. 2024Initial Nugget Evaluation Results for the TREC 2024 RAG Track with the AutoNuggetizer Framework
  3. 2025A Systematic Review of Key Retrieval-Augmented Generation (RAG) Systems: Progress, Gaps, and Future Directions
  4. 2026Overview of the TREC 2025 RAGTIME Track
  5. 2024A Comprehensive Survey of Retrieval-Augmented Generation (RAG): Evolution, Current Landscape and Future Directions
  6. 2025A Systematic Literature Review of Retrieval-Augmented Generation: Techniques, Metrics, and Challenges
  7. 2025Retrieval Augmented Generation Evaluation in the Era of Large Language Models: A Comprehensive Survey
  8. 2025Towards Trustworthy Retrieval Augmented Generation for Large Language Models: A Survey