---
title: "Let Retrievers Think Before Action — paper by Yan et al. (2026)"
description: "No dataset contains query–thought pairs, so the authors build training data in two steps. 2.3 points. 82 references."
authors: ["Ruiran Yan", "Wen Xiong", "Ze Liu", "Chaozhuo Li", "Hao Liao", "Defu Lian", "Zheng Liu"]
year: 2026
venue: "Findings of the Association for Computational Linguistics: ACL 2026"
source: "https://aclanthology.org/2026.findings-acl.1603/"
doi: "10.18653/v1/2026.findings-acl.1603"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/yan-2026-let-retrievers-think-before-action/"
---
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# Let Retrievers Think Before Action: Thought-Augmented Embedding for Dense Retrieval

**Authors:** Ruiran Yan, Wen Xiong, [Ze Liu](https://md.research.elamur.ai/free/en/authors/ze-liu/), [Chaozhuo Li](https://md.research.elamur.ai/free/en/authors/chaozhuo-li/), [Hao Liao](https://md.research.elamur.ai/free/en/authors/hao-liao/), [Defu Lian](https://md.research.elamur.ai/free/en/authors/defu-lian/) and [Zheng Liu](https://md.research.elamur.ai/free/en/authors/zheng-liu/) · **Year:** 2026 · **Venue:** [Findings of the Association for Computational Linguistics: ACL 2026](https://md.research.elamur.ai/free/en/venues/findings/) ([Findings of the Association for Computational Linguistics 2026](https://md.research.elamur.ai/free/en/venues/findings/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.findings-acl.1603/) · [PDF](https://aclanthology.org/2026.findings-acl.1603.pdf) · [DOI 10.18653/v1/2026.findings-acl.1603](https://doi.org/10.18653/v1/2026.findings-acl.1603) · [OpenAlex](https://openalex.org/W7166810378)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary
Dense retrievers often perform poorly on queries from unfamiliar domains or on queries whose relevance depends on reasoning, such as multi-hop evidence or code search. Reasoning LLMs like OpenAI O1 and DeepSeek R1 improve by thinking before they answer, and this paper applies that idea to retrieval with O1 Embedder. A single model first writes several thoughts about what the query needs. It then encodes the query together with those thoughts into one thought-augmented vector, using mean pooling.

No dataset contains query–thought pairs, so the authors build training data in two steps. An LLM proposes several candidate thoughts for each query. A committee of retrievers then votes for the candidate that best matches the ground-truth document. Training combines two objectives: supervised fine-tuning teaches the model to generate thoughts, and contrastive learning teaches it to produce embeddings.

The model improves on in-domain MS MARCO, on zero-shot BEIR-style datasets and on reasoning-heavy tasks. This suggests that test-time thinking can make embedding models more robust.

## Key points
- On MS MARCO, adding thoughts improves MRR@10 by 1.4 points. nDCG@10 rises by 1.6 on DL'19 and 2.1 on DL'20.
- In zero-shot evaluation, average nDCG@10 is 61.4, which is 2.3 points above the same model without thoughts (O1-E w/o T). Even without thoughts, the model performs about as well as RepLLaMA.
- The authors also tested a pipeline of two separate models, with RepLLaMA as the retriever and GPT-4o-mini generating thoughts. Its average changed by −0.1, and it lost ground on Trec-Covid, FiQA and SciFact.
- Under the same comparison, the jointly trained single model gained +2.2 on average, improving on every dataset including CosQA and HotPotQA.
- Stated limitations include:
  - thoughts can be noisy in specialized domains;
  - inference costs more;
  - the model has no adaptive decision about when to think;
  - thoughts are single-round with fixed mean pooling.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [University of Science and Technology of China](https://md.research.elamur.ai/free/en/universities/university-of-science-and-technology-of-china/) — University, Hefei, China · 145 works · 114,409 citations
- [Beijing University of Posts and Telecommunications](https://md.research.elamur.ai/free/en/universities/beijing-university-of-posts-and-telecommunications/) — University, Beijing, China · 78 works · 33,365 citations
- [Hong Kong Polytechnic University](https://md.research.elamur.ai/free/en/universities/hong-kong-polytechnic-university/) — University, Hong Kong, China · 106 works · 5,190 citations
- [Beijing Academy of Artificial Intelligence](https://md.research.elamur.ai/free/en/labs/beijing-academy-of-artificial-intelligence/) — Research institute, Beijing, China · 46 works · 8,682 citations
- [Beijing University of Technology](https://md.research.elamur.ai/free/en/universities/beijing-university-of-technology/) — University, Beijing, China · 16 works · 1,910 citations
- [Shenzhen University](https://md.research.elamur.ai/free/en/universities/shenzhen-university/) — University, Shenzhen, China · 22 works · 95 citations

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## Cite

Cite as: Ruiran Yan, Wen Xiong, Ze Liu, Chaozhuo Li, Hao Liao, Defu Lian and Zheng Liu. 2026. Let Retrievers Think Before Action: Thought-Augmented Embedding for Dense Retrieval. Findings of the Association for Computational Linguistics: ACL 2026.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{yan2026let,
  title = {{Let Retrievers Think Before Action: Thought-Augmented Embedding for Dense Retrieval}},
  author = {Ruiran Yan, Wen Xiong, Ze Liu, Chaozhuo Li, Hao Liao, Defu Lian and Zheng Liu},
  booktitle = {Findings of the Association for Computational Linguistics: ACL 2026},
  year = {2026},
  doi = {10.18653/v1/2026.findings-acl.1603},
  url = {https://doi.org/10.18653/v1/2026.findings-acl.1603}
}
```

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- [next → Let the Comments Speak: A Multi-Agent Framework based on Large Language Model for Comment-Guided Code Refactoring](https://md.research.elamur.ai/free/en/papers/wang-2026-let-comments-speak-multi-agent/)
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*Updated 2 November 2026*