# Overview of the TREC 2025 RAGTIME Track

arXiv: https://arxiv.org/abs/2602.10024 · HTML: https://mlsanity.com/paper/2602.10024/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 6 references that are in the map.
- First posted on arXiv 10 Feb 2026.

## Builds on (most cited first, 3 of 3)

- [On the Evaluation of Machine-Generated Reports](https://mlsanity.com/paper/2405.00982/) (2024)
- [Translate-Distill: Learning Cross-Language Dense Retrieval by Translation and Distillation](https://mlsanity.com/paper/2401.04810/) (2024)
- [Auto-ARGUE: LLM-Based Report Generation Evaluation](https://mlsanity.com/paper/2509.26184/) (2025)

## Built on by (most cited first, 5 of 5)

- [CoverageBench: Evaluating Information Coverage across Tasks and Domains](https://mlsanity.com/paper/2603.20034/) (2026)
- [DoGMaTiQ: Automated Generation of Question-and-Answer Nuggets for Report Evaluation](https://mlsanity.com/paper/2605.04458/) (2026)
- [LLM Olympiad: Why Model Evaluation Needs a Sealed Exam](https://mlsanity.com/paper/2603.23292/) (2026)
- [Assessing the Downstream Utility of Evidence-Aware Retrieval in RAG](https://mlsanity.com/paper/2608.26379/) (2026)
- [Knowledge Pull Requests for Continual Document Authoring](https://mlsanity.com/paper/2609.26634/) (2026)

## Abstract (arXiv)

The principal goal of the RAG TREC Instrument for Multilingual Evaluation (RAGTIME) track at TREC is to study report generation from multilingual source documents. The track has created a document collection containing Arabic, Chinese, English, and Russian news stories. RAGTIME includes three task types: Multilingual Report Generation, English Report Generation, and Multilingual Information Retrieval (MLIR). A total of 125 runs were submitted by 13 participating teams (and as baselines by the track coordinators) for three tasks. This overview describes these three tasks and presents the available results.