---
title: "Who cites Back Attention: Understanding — 13 citing papers"
description: "13 papers of the Elamur Research library cite «Back Attention: Understanding and Enhancing Multi-Hop Reasoning in Large Language…"
canonical: "https://research.elamur.ai/free/en/papers/yu-2025-back-attention-understanding-enhancing-multi/cited-by/"
---
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# Who cites «Back Attention: Understanding and Enhancing Multi-Hop Reasoning in Large Language Models»

13 papers of our library cite it (of the 149,439 whose bibliographies we read) · 7 with a page here, the most cited first · [← back to «Back Attention: Understanding and Enhancing Multi-Hop Reasoning in Large Language Models»](https://md.research.elamur.ai/free/en/papers/yu-2025-back-attention-understanding-enhancing-multi/)

1. **[Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Models](https://md.research.elamur.ai/free/en/papers/chen-2025-towards-reasoning-era-survey-long/) [Science China Information Sciences](https://md.research.elamur.ai/free/en/venues/science-china-information-sciences/)**Qiguang Chen, Libo Qin, Jinhao Liu, et al.*2025 · 176 citations*
2. [**Stop Overthinking: A Survey on Efficient Reasoning for Large Language Models arXiv:2503.16419**Yang Sui, Yu-Neng Chuang, Guanchu Wang, et al.*2025 · 157 citations*](https://md.research.elamur.ai/free/en/papers/sui-2025-stop-overthinking-survey-efficient-reasoning/)
3. [**A Survey on Latent Reasoning arXiv:2507.06203**Rui-Jie Zhu, Tianhao Peng, Tianhao Cheng, et al.*2025 · 15 citations*](https://md.research.elamur.ai/free/en/papers/zhu-2025-survey-latent-reasoning/)
4. **[Locate, Steer, and Improve: A Practical Survey of Actionable Mechanistic Interpretability in Large Language Models](https://md.research.elamur.ai/free/en/papers/zhang-2026-locate-steer-improve-practical-survey/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Hengyuan Zhang, Zhihao Zhang, Ercong Nie, et al.*2026 · 9 citations*
5. **[Internal Chain-of-Thought: Empirical Evidence for Layer-wise Subtask Scheduling in LLMs](https://md.research.elamur.ai/free/en/papers/yang-2025-internal-chain-thought-empirical-evidence/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Zhipeng Yang, Junzhuo Li, Siyu Xia, et al.*2025 · 7 citations*
6. **[NEAT: Neuron-Based Early Exit for Large Reasoning Models](https://md.research.elamur.ai/free/en/papers/liu-2026-neat-neuron-based-early-exit/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Kang Liu, YongKang Liu, Xiaocui Yang, et al.*2026 · 1 citation*
7. **[ReasonAny: Incorporating Reasoning Capability to Any Model via Simple and Effective Model Merging](https://md.research.elamur.ai/free/en/papers/yang-2026-reasonany-incorporating-reasoning-capability-any/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Junyao Yang, Chen Qian, Wen Shen, et al.*2026*

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

- [← back to](https://md.research.elamur.ai/free/en/papers/yu-2025-back-attention-understanding-enhancing-multi/) — Back Attention: Understanding and Enhancing Multi-Hop Reasoning in Large Language Models