---
title: "Who cites R-Search: Empowering LLM — 7 citing papers"
description: "7 papers of the Elamur Research library cite «R-Search: Empowering LLM Reasoning with Search via Multi-Reward Reinforcement Learning»…"
canonical: "https://research.elamur.ai/free/en/papers/zhao-2026-r-search-empowering-llm-reasoning/cited-by/"
---
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# Who cites «R-Search: Empowering LLM Reasoning with Search via Multi-Reward Reinforcement Learning»

7 papers of our library cite it (of the 149,439 whose bibliographies we read) · 3 with a page here, the most cited first · [← back to «R-Search: Empowering LLM Reasoning with Search via Multi-Reward Reinforcement Learning»](https://md.research.elamur.ai/free/en/papers/zhao-2026-r-search-empowering-llm-reasoning/)

1. **[AgenticRAGTracer: A Hop-Aware Benchmark for Diagnosing Multi-Step Retrieval Reasoning in Agentic RAG](https://md.research.elamur.ai/free/en/papers/you-2026-agenticragtracer-hop-aware-benchmark-diagnosing/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Qijie You, Wenkai Yu, Hao Liang, et al.*2026 · 1 citation*
2. **[A Survey of Large Language Model-Based Search Agents](https://md.research.elamur.ai/free/en/papers/xi-2026-survey-large-language-model-based/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yunjia Xi, Jianghao Lin, Yongzhao Xiao, et al.*2026*
3. **[CheckRLM: Effective Knowledge–Thought Coherence Checking in Retrieval-Augmented Reasoning](https://md.research.elamur.ai/free/en/papers/xu-2026-checkrlm-effective-knowledge-thought-coherence/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Dingling Xu, Ruobing Wang, Qingfei Zhao, et al.*2026*

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

- [← back to](https://md.research.elamur.ai/free/en/papers/zhao-2026-r-search-empowering-llm-reasoning/) — R-Search: Empowering LLM Reasoning with Search via Multi-Reward Reinforcement Learning