--- title: "Who cites Reasoning with Latent Thoughts: — 42 citing papers" description: "42 papers of the Elamur Research library cite «Reasoning with Latent Thoughts: On the Power of Looped Transformers», found in their…" canonical: "https://research.elamur.ai/free/en/papers/saunshi-2025-reasoning-latent-thoughts-power-looped/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/saunshi-2025-reasoning-latent-thoughts-power-looped/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Reasoning with Latent Thoughts: On the Power of…](https://md.research.elamur.ai/free/en/papers/saunshi-2025-reasoning-latent-thoughts-power-looped/) / cited by # Who cites «Reasoning with Latent Thoughts: On the Power of Looped Transformers» 42 papers of our library cite it (of the 149,439 whose bibliographies we read) · 17 with a page here, the most cited first · [← back to «Reasoning with Latent Thoughts: On the Power of Looped Transformers»](https://md.research.elamur.ai/free/en/papers/saunshi-2025-reasoning-latent-thoughts-power-looped/) 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 Test-Time Scaling in Large Language Models: What, How, Where, and How Well? arXiv:2503.24235**Qiyuan Zhang, Fuyuan Lyu, Zexu Sun, et al.*2025 · 79 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2025-survey-test-time-scaling-large/) 4. **[CODI: Compressing Chain-of-Thought into Continuous Space via Self-Distillation](https://md.research.elamur.ai/free/en/papers/shen-2025-codi-compressing-chain-thought-into/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Zhenyi Shen, Hanqi Yan, Linhai Zhang, et al.*2025 · 78 citations* 5. [**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/) 6. [**Reasoning Models Can Be Effective Without Thinking arXiv:2504.09858**Wenjie Ma, Jingxuan He, Charlie Snell, et al.*2025 · 58 citations*](https://md.research.elamur.ai/free/en/papers/ma-2025-reasoning-models-effective-thinking/) 7. [**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/) 8. [**Scaling Latent Reasoning via Looped Language Models arXiv:2510.25741**Rui-Jie Zhu, Zixuan Wang, Kai Hua, et al.*2025 · 22 citations*](https://md.research.elamur.ai/free/en/papers/zhu-2025-scaling-latent-reasoning-looped-language/) 9. **[Think Silently, Think Fast: Dynamic Latent Compression of LLM Reasoning Chains](https://md.research.elamur.ai/free/en/papers/tan-2025-think-silently-think-fast-dynamic/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Wenhui Tan, Jiaze Li, Jianzhong Ju, et al.*2025 · 19 citations* 10. [**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/) 11. [**Implicit Reasoning in Large Language Models: A Comprehensive Survey arXiv:2509.02350**Jindong Li, Yali Fu, Li Fan, et al.*2025 · 10 citations*](https://md.research.elamur.ai/free/en/papers/li-2025-implicit-reasoning-large-language-models/) 12. **[Efficient Reasoning for LLMs through Speculative Chain-of-Thought](https://md.research.elamur.ai/free/en/papers/wang-2026-efficient-reasoning-llms-through-speculative/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Jikai Wang, Juntao Li, Jianye Hou, et al.*2026 · 6 citations* 13. **[Enhancing Auto-regressive Chain-of-Thought through Loop-Aligned Reasoning](https://md.research.elamur.ai/free/en/papers/yu-2026-enhancing-auto-regressive-chain-thought/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Qifan Yu, Zhenyu He, Sijie Li, et al.*2026 · 5 citations* 14. **[Think How to Think: Mitigating Overthinking with Autonomous Difficulty Cognition in Large Reasoning Models](https://md.research.elamur.ai/free/en/papers/liu-2026-think-how-think-mitigating-overthinking/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yongjiang Liu, Haoxi Li, Xiaosong Ma, et al.*2026 · 2 citations* 15. **[SpiralThinker: Latent Reasoning through an Iterative Process with Text–Latent Interleaving](https://md.research.elamur.ai/free/en/papers/piao-2026-spiralthinker-latent-reasoning-through-iterative/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Shengmin Piao, Sanghyun Park*2026 · 1 citation* 16. **[Learning Shortcut Models for Efficient Recursive Reasoning](https://md.research.elamur.ai/free/en/papers/shankar-2026-learning-shortcut-models-efficient-recursive/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Shiv Shankar*2026* 17. **[LoopCoder: Scaling Code Intelligence via Looped Language Models](https://md.research.elamur.ai/free/en/papers/yang-2026-loopcoder-scaling-code-intelligence-looped/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Jian Yang, Wei Zhang, Shuyue Guo, et al.*2026* 25 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/saunshi-2025-reasoning-latent-thoughts-power-looped/) — Reasoning with Latent Thoughts: On the Power of Looped Transformers