---
title: "Who cites RaDeR: Reasoning-aware Dense Retrieval Models"
description: "9 papers of the Elamur Research library cite «RaDeR: Reasoning-aware Dense Retrieval Models», found in their bibliographies; the most cited first."
canonical: "https://research.elamur.ai/free/en/papers/das-2025-rader-reasoning-aware-dense-retrieval/cited-by/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/das-2025-rader-reasoning-aware-dense-retrieval/cited-by/)

[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [RaDeR: Reasoning-aware Dense Retrieval Models](https://md.research.elamur.ai/free/en/papers/das-2025-rader-reasoning-aware-dense-retrieval/) / cited by

# Who cites «RaDeR: Reasoning-aware Dense Retrieval Models»

9 papers of our library cite it (of the 149,439 whose bibliographies we read) · 2 with a page here, the most cited first · [← back to «RaDeR: Reasoning-aware Dense Retrieval Models»](https://md.research.elamur.ai/free/en/papers/das-2025-rader-reasoning-aware-dense-retrieval/)

1. **[ReasonRank: Empowering Passage Ranking with Strong Reasoning Ability](https://md.research.elamur.ai/free/en/papers/liu-2026-reasonrank-empowering-passage-ranking-strong/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Wenhan Liu, Xinyu Ma, Weiwei Sun, et al.*2026 · 18 citations*
2. **[A Survey of Reasoning-Intensive Retrieval: Progress and Challenges](https://md.research.elamur.ai/free/en/papers/wei-2026-survey-reasoning-intensive-retrieval-progress/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yiyang Wei, Tingyu Song, Siyue Zhang, et al.*2026*

7 more papers of our library cite it; they have no page here yet.

- [← back to](https://md.research.elamur.ai/free/en/papers/das-2025-rader-reasoning-aware-dense-retrieval/) — RaDeR: Reasoning-aware Dense Retrieval Models