# Overview of the TREC 2024 NeuCLIR Track

arXiv: https://arxiv.org/abs/2509.14355 · HTML: https://mlsanity.com/paper/2509.14355/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Sep 2025.

## Built on by (most cited first, 13 of 13)

- [Auto-ARGUE: LLM-Based Report Generation Evaluation](https://mlsanity.com/paper/2509.26184/) (2025)
- [Principled Context Engineering for RAG: Statistical Guarantees via Conformal Prediction](https://mlsanity.com/paper/2511.17908/) (2025)
- [FACTUM: Mechanistic Detection of Citation Hallucination in Long-Form RAG](https://mlsanity.com/paper/2601.05866/) (2026)
- [Query Decomposition for RAG: Balancing Exploration-Exploitation](https://mlsanity.com/paper/2510.18633/) (2025)
- [When Deep Research Agents Stagnate: Enhancing Reasoning with Retrieval-Aware Agent Control](https://mlsanity.com/paper/2608.15191/) (2026)
- [Incorporating Q&A Nuggets into Retrieval-Augmented Generation](https://mlsanity.com/paper/2601.13222/) (2026)
- [LANCER: LLM Reranking for Nugget Coverage](https://mlsanity.com/paper/2601.22008/) (2026)
- [RoutIR: Fast Serving of Retrieval Pipelines for Retrieval-Augmented Generation](https://mlsanity.com/paper/2601.10644/) (2026)
- [Insider Knowledge: How Much Can RAG Systems Gain from Evaluation Secrets?](https://mlsanity.com/paper/2601.13227/) (2026)
- [DoGMaTiQ: Automated Generation of Question-and-Answer Nuggets for Report Evaluation](https://mlsanity.com/paper/2605.04458/) (2026)
- [Findings of the MAGMaR 2026 Shared Task](https://mlsanity.com/paper/2606.12295/) (2026)
- [SHIFT: Semantic Harmonization via Index-side Feature Transformation for Multilingual Information Retrieval](https://mlsanity.com/paper/2606.18801/) (2026)
- [ChemCLIR-Bench: Benchmarking Cross-Lingual Information Retrieval in Multilingual Chemical Patents](https://mlsanity.com/paper/2609.23231/) (2026)

## Abstract (arXiv)

The principal goal of the TREC Neural Cross-Language Information Retrieval (NeuCLIR) track is to study the effect of neural approaches on cross-language information access. The track has created test collections containing Chinese, Persian, and Russian news stories and Chinese academic abstracts. NeuCLIR includes four task types: Cross-Language Information Retrieval (CLIR) from news, Multilingual Information Retrieval (MLIR) from news, Report Generation from news, and CLIR from technical documents. A total of 274 runs were submitted by five participating teams (and as baselines by the track coordinators) for eight tasks across these four task types. Task descriptions and the available results are presented.