---
格式版本: 2
标题: "ThinkRetrieve: Retrieval-Augmented Reasoning Traces for Test-Time Scaling"
原文链接: "https://arxiv.org/abs/2608.10928"
发布日期: "2026-08-11"
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发现时间: "2026-08-12T10:46:47+08:00"
入库时间: "2026-08-12T02:48:09.045Z"
来源平台: "arXiv 学术论文搜索"
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AI优质: "否"
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去重键: "https://arxiv.org/abs/2608.10928"
---

## Computer Science > Artificial Intelligence

## Title:ThinkRetrieve: Retrieval-Augmented Reasoning Traces for Test-Time Scaling

[View PDF](https://arxiv.org/pdf/2608.10928) [HTML (experimental)](https://arxiv.org/html/2608.10928v1)

> Abstract:Large Reasoning Models (LRMs) improve performance by allocating additional inference-time compute to generate extended chain-of-thought reasoning. However, recent studies reveal that sequential test-time scaling often yields diminishing or even negative returns, as longer traces exhibit increased uncertainty, error compounding, and drift from the original problem. We propose ThinkRetrieve, a test-time scaling framework that augments the reasoning traces of LRMs with dynamically retrieved solved examples at each reasoning step. Given an external corpus of problems paired with step-by-step solutions, ThinkRetrieve retrieves relevant exemplars at each intermediate step and injects them directly into the thinking trace, providing the model with guidance on how to reason rather than merely what facts are relevant. Experiments across five reasoning models (1.5B--8B parameters) on GSM-8K, MATH-500, AIME 2025, and SciQ demonstrate that ThinkRetrieve consistently improves accuracy over standard test-time scaling, with relative gains of up to $60\\%$ on AIME 2025.

| Subjects: | Artificial Intelligence (cs.AI) |
| --- | --- |
| Cite as: | [arXiv:2608.10928](https://arxiv.org/abs/2608.10928) \[cs.AI\] |
|  | (or [arXiv:2608.10928v1](https://arxiv.org/abs/2608.10928v1) \[cs.AI\] for this version) |
|  | [https://doi.org/10.48550/arXiv.2608.10928](https://doi.org/10.48550/arXiv.2608.10928) |

## Submission history

From: Vaibhav Singh \[[view email](https://arxiv.org/show-email/0907af33/2608.10928)\]  
**\[v1\]** Tue, 11 Aug 2026 13:58:07 UTC (3,023 KB)

[Which authors of this paper are endorsers?](https://arxiv.org/auth/show-endorsers/2608.10928) | Disable MathJax ([What is MathJax?](https://info.arxiv.org/help/mathjax.html))
