---
title: "Who cites Deconstructing Long — 14 citing papers"
description: "14 papers of the Elamur Research library cite «Deconstructing Long Chain-of-Thought: A Structured Reasoning Optimization Framework for…"
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---
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# Who cites «Deconstructing Long Chain-of-Thought: A Structured Reasoning Optimization Framework for Long CoT Distillation»

14 papers of our library cite it (of the 149,439 whose bibliographies we read) · 10 with a page here, the most cited first · [← back to «Deconstructing Long Chain-of-Thought: A Structured Reasoning Optimization Framework for Long CoT Distillation»](https://md.research.elamur.ai/free/en/papers/luo-2025-deconstructing-long-chain-thought-structured/)

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. [**Dynamic Early Exit in Reasoning Models arXiv:2504.15895**Chenxu Yang, Qingyi Si, Yongjie Duan, et al.*2025 · 74 citations*](https://md.research.elamur.ai/free/en/papers/yang-2025-dynamic-early-exit-reasoning-models/)
3. [**A Survey of Efficient Reasoning for Large Reasoning Models: Language, Multimodality, and Beyond arXiv:2503.21614**Xiaoye Qu, Yafu Li, Zhao-Chen Su, et al.*2025 · 55 citations*](https://md.research.elamur.ai/free/en/papers/qu-2025-survey-efficient-reasoning-large-reasoning/)
4. [**Efficient Reasoning Models: A Survey arXiv:2504.10903**Sicheng Feng, Gongfan Fang, Xinyin Ma, et al.*2025 · 37 citations*](https://md.research.elamur.ai/free/en/papers/feng-2025-efficient-reasoning-models-survey/)
5. **[Learning from Diverse Reasoning Paths with Routing and Collaboration](https://md.research.elamur.ai/free/en/papers/lei-2025-learning-diverse-reasoning-paths-routing/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Zhenyu Lei, Zhen Tan, Song Wang, et al.*2025 · 11 citations*
6. [**Are Large Language Models Reliable AI Scientists? Assessing Reverse-Engineering of Black-Box Systems arXiv:2505.17968**Jiayi Geng, Howard Chen, Dilip Arumugam, et al.*2025 · 4 citations*](https://md.research.elamur.ai/free/en/papers/geng-2025-large-language-models-reliable/)
7. **[Beyond Templates: Dynamic Adaptation of Reasoning Demonstrations via Feasibility-Aware Exploration](https://md.research.elamur.ai/free/en/papers/wu-2026-beyond-templates-dynamic-adaptation-reasoning/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yong Wu, Weihang Pan, Ke Li, et al.*2026 · 3 citations*
8. **[Incentivizing Strong Reasoning from Weak Supervision](https://md.research.elamur.ai/free/en/papers/yuan-2026-incentivizing-strong-reasoning-weak-supervision/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Yige Yuan, Teng Xiao, Shuchang Tao, et al.*2026 · 1 citation*
9. **[On the Step Length Confounding in LLM Reasoning Data Selection](https://md.research.elamur.ai/free/en/papers/wang-2026-step-length-confounding-llm-reasoning/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Bing Wang, Rui Miao, Chen Shen, et al.*2026 · 1 citation*
10. **[Concise Math Reasoning via Difficulty-Aware Distillation](https://md.research.elamur.ai/free/en/papers/wu-2026-concise-math-reasoning-difficulty-aware/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yifan Wu, Jingze Shi, Bingheng Wu, 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/luo-2025-deconstructing-long-chain-thought-structured/) — Deconstructing Long Chain-of-Thought: A Structured Reasoning Optimization Framework for Long CoT Distillation