---
title: "Who cites Mehul Damani — 138 papers of our library"
description: "138 papers of the Elamur Research library cite the works of Mehul Damani, found in their bibliographies; the most cited first."
canonical: "https://research.elamur.ai/free/en/authors/mehul-damani/cited-by/"
---
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# Who cites Mehul Damani

140 citations of their works from 138 papers of our library (of the 149,439 whose bibliographies we read) · 41 with a page here, the most cited first · [← Mehul Damani](https://md.research.elamur.ai/free/en/authors/mehul-damani/)

1. **[DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning](https://md.research.elamur.ai/free/en/papers/deepseek-ai-2025-deepseek-r1-incentivizing-reasoning-capability/) [Nature](https://md.research.elamur.ai/free/en/venues/nature/)**DeepSeek-AI, Daya Guo, Dejian Yang, et al.*2025 · 4,728 citations*
2. **[Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models](https://md.research.elamur.ai/free/en/papers/zhang-2023-siren-s-song-ai-ocean/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**Yue Zhang, Yafu Li, Leyang Cui, et al.*2023 · 977 citations*
3. [**Do NOT Think That Much for 2+3=? On the Overthinking of o1-Like LLMs arXiv:2412.21187**Xingyu Chen, Jiahao Xu, Tian Liang, et al.*2024 · 192 citations*](https://md.research.elamur.ai/free/en/papers/chen-2024-think-that-much-2-3/)
4. [**Self-Distilled Reasoner: On-Policy Self-Distillation for Large Language Models arXiv:2601.18734**Siyan Zhao, Zhihui Xie, Mengchen Liu, et al.*2026 · 97 citations*](https://md.research.elamur.ai/free/en/papers/zhao-2026-self-distilled-reasoner-policy-self/)
5. **[TTRL: Test-Time Reinforcement Learning](https://md.research.elamur.ai/free/en/papers/zuo-2025-ttrl-test-time-reinforcement-learning/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Yuxin Zuo, Kaiyan Zhang, Li Sheng, et al.*2025 · 76 citations*
6. [**Reinforcement Learning via Self-Distillation arXiv:2601.20802**Jonas Hubotter, Frederike Lubeck, Lejs Behric, et al.*2026 · 74 citations*](https://md.research.elamur.ai/free/en/papers/hubotter-2026-reinforcement-learning-self-distillation/)
7. [**Rethinking On-Policy Distillation of Large Language Models: Phenomenology, Mechanism, and Recipe arXiv:2604.13016**Yaxuan Li, Yuxin Zuo, Bingxiang He, et al.*2026 · 58 citations*](https://md.research.elamur.ai/free/en/papers/li-2026-rethinking-policy-distillation-large-language/)
8. [**Reasoning Models Know When They're Right: Probing Hidden States for Self-Verification arXiv:2504.05419**Anqi Zhang, Yulin Chen, Jane Pan, et al.*2025 · 52 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2025-reasoning-models-know-they-re/)
9. [**A Survey of On-Policy Distillation for Large Language Models arXiv:2604.00626**Mingyang Song, Mao Zheng*2026 · 41 citations*](https://md.research.elamur.ai/free/en/papers/song-2026-survey-policy-distillation-large-language/)
10. [**A Survey of Frontiers in LLM Reasoning: Inference Scaling, Learning to Reason, and Agentic Systems arXiv:2504.09037**Zixuan Ke, Fangkai Jiao, Yifei Ming, et al.*2025 · 39 citations*](https://md.research.elamur.ai/free/en/papers/ke-2025-survey-frontiers-llm-reasoning-inference/)
11. [**Self-Distilled RLVR arXiv:2604.03128**Chenxu Yang, Chuanyu Qin, Qingyi Si, et al.*2026 · 37 citations*](https://md.research.elamur.ai/free/en/papers/yang-2026-self-distilled-rlvr/)
12. [**On-Policy Context Distillation for Language Models arXiv:2602.12275**Tianzhu Ye, Li Dong, Xun Wu, et al.*2026 · 36 citations*](https://md.research.elamur.ai/free/en/papers/ye-2026-policy-context-distillation-language-models/)
13. [**The Surprising Effectiveness of Test-Time Training for Few-Shot Learning arXiv:2411.07279**Ekin Akyürek, Mehul Damani, Adam Zweiger, et al.*2024 · 36 citations*](https://md.research.elamur.ai/free/en/papers/akyurek-2024-surprising-effectiveness-test-time-training/)
14. **[Aristotle: IMO-level Automated Theorem Proving](https://md.research.elamur.ai/free/en/papers/achim-2025-aristotle-imo-level-automated-theorem/) [SuperIntelligence - Robotics - Safety & Alignment](https://md.research.elamur.ai/free/en/venues/superintelligence-robotics-safety-alignment/)**Tudor Achim, Alex Best, Alberto Bietti, et al.*2025 · 29 citations*
15. [**Training a Scientific Reasoning Model for Chemistry arXiv:2506.17238**Siddharth M. Narayanan, James D. Braza, Ryan-Rhys Griffiths, et al.*2025 · 15 citations*](https://md.research.elamur.ai/free/en/papers/narayanan-2025-training-scientific-reasoning-model/)
16. **[Is That Your Final Answer? Test-Time Scaling Improves Selective Question Answering](https://md.research.elamur.ai/free/en/papers/jurayj-2025-is-that-your-final-answer/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**William Jurayj, Jeffrey Cheng, Benjamin Van Durme*2025 · 11 citations*
17. [**Lightning OPD: Efficient Post-Training for Large Reasoning Models with Offline On-Policy Distillation arXiv:2604.13010**Yecheng Wu, Song Han, Han Cai*2026 · 10 citations*](https://md.research.elamur.ai/free/en/papers/wu-2026-lightning-opd-efficient-post-training/)
18. [**KL for a KL: On-Policy Distillation with Control Variate Baseline arXiv:2605.07865**Minjae Oh, Sangjun Song, Gyubin Choi, et al.*2026 · 8 citations*](https://md.research.elamur.ai/free/en/papers/oh-2026-kl-kl-policy-distillation-control/)
19. [**Unmasking On-Policy Distillation: Where It Helps, Where It Hurts, and Why arXiv:2605.10889**Mohammadreza Armandpour, Fatih Ilhan, David Harrison, et al.*2026 · 7 citations*](https://md.research.elamur.ai/free/en/papers/armandpour-2026-unmasking-policy-distillation-where-helps/)
20. [**Asymmetric On-Policy Distillation: Bridging Exploitation and Imitation at the Token Level arXiv:2605.06387**Nan Jia, Haojin Yang, Xing Ma, et al.*2026 · 6 citations*](https://md.research.elamur.ai/free/en/papers/jia-2026-asymmetric-policy-distillation-bridging-exploitation/)
21. [**Uni-OPD: Unifying On-Policy Distillation with a Dual-Perspective Recipe arXiv:2605.03677**Wenjin Hou, Shangpin Peng, Weinong Wang, et al.*2026 · 6 citations*](https://md.research.elamur.ai/free/en/papers/hou-2026-uni-opd-unifying-policy-distillation/)
22. [**Making Small Language Models Efficient Reasoners: Intervention, Supervision, Reinforcement arXiv:2505.07961**Xuechen Zhang, Zijian Huang, Chenshun Ni, et al.*2025 · 6 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2025-making-small-language-models-efficient/)
23. [**Self-Distilled Policy Gradient arXiv:2606.04036**Yifeng Liu, Shiyuan Zhang, Yifan Zhang, et al.*2026 · 5 citations*](https://md.research.elamur.ai/free/en/papers/liu-2026-self-distilled-policy-gradient/)
24. **[Gated Differentiable Working Memory for Long-Context Language Modeling](https://md.research.elamur.ai/free/en/papers/mei-2026-gated-differentiable-working-memory-long/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Lingrui Mei, Shenghua Liu, Yiwei Wang, et al.*2026 · 4 citations*
25. **[Which Reasoning Trajectories Teach Students to Reason Better? A Simple Metric of Informative Alignment](https://md.research.elamur.ai/free/en/papers/yang-2026-which-reasoning-trajectories-teach-students/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yuming Yang, Mingyoung Lai, Wanxu Zhao, et al.*2026 · 4 citations*
26. **[Beyond “I Don't Know”: Evaluating LLM Self-Awareness in Discriminating Data and Model Uncertainty](https://md.research.elamur.ai/free/en/papers/ren-2026-beyond-i-don-t-know/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jingyi Ren, Ante Wang, Yunghwei Lai, et al.*2026 · 3 citations*
27. **[The Confidence Dichotomy: Analyzing and Mitigating Miscalibration in Tool-Use Agents](https://md.research.elamur.ai/free/en/papers/xuan-2026-confidence-dichotomy-analyzing-mitigating-miscalibration/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Weihao Xuan, Qingcheng Zeng, Heli Qi, et al.*2026 · 2 citations*
28. **[Confidence-Calibrated Small-Large Language Model Collaboration for Cost-Efficient Reasoning](https://md.research.elamur.ai/free/en/papers/zhang-2026-confidence-calibrated-small-large-language/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Chuang Zhang, Zizhen Zhu, Yihao Wei, et al.*2026 · 1 citation*
29. **[GlimpRouter: Efficient Collaborative Inference by Glimpsing One Token of Thoughts](https://md.research.elamur.ai/free/en/papers/zeng-2026-glimprouter-efficient-collaborative-inference-glimpsing/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Wenhao Zeng, Xuteng Zhang, Yuling Shi, et al.*2026 · 1 citation*
30. **[LightReasoner: Can Small Language Models Teach Large Language Models Reasoning?](https://md.research.elamur.ai/free/en/papers/wang-2026-lightreasoner-can-small-language-models/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jingyuan Wang, Yankai Chen, Zhonghang Li, et al.*2026 · 1 citation*
31. **[Adaptive Test-Time Compute Allocation with Evolving In-Context Demonstrations](https://md.research.elamur.ai/free/en/papers/zuo-2026-adaptive-test-time-compute-allocation/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Bowen Zuo, Dongruo Zhou, Yinglun Zhu*2026*
32. **[Balancing Classification and Calibration Performance in Decision-Making LLMs via Calibration Aware Reinforcement Learning](https://md.research.elamur.ai/free/en/papers/yaldiz-2026-balancing-classification-calibration-performance-decision/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Duygu Nur Yaldiz, Evangelia Spiliopoulou, Zheng Qi, et al.*2026*
33. **[Calibrating LLM Judges: Linear Probes for Fast and Reliable Uncertainty Estimation](https://md.research.elamur.ai/free/en/papers/radharapu-2026-calibrating-llm-judges-linear-probes/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Bhaktipriya Radharapu, Eshika Saxena, Kenneth Li, et al.*2026*
34. **[Calibration-Aware Policy Optimization for Reasoning LLMs](https://md.research.elamur.ai/free/en/papers/wang-2026-calibration-aware-policy-optimization-reasoning/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Ziqi Wang, Xingzhou Lou, Meiqi Wu, et al.*2026*
35. **[Distill and Align Decomposition for Enhanced Claim Verification](https://md.research.elamur.ai/free/en/papers/magomere-2026-distill-align-decomposition-enhanced-claim/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Jabez Magomere, Elena Kochkina, Samuel Mensah, et al.*2026*
36. **[EpiCaR: Knowing What You Don't Know Matters for Better Reasoning in LLMs](https://md.research.elamur.ai/free/en/papers/yeom-2026-epicar-knowing-what-you-don/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jewon Yeom, Jaewon Sok, Seonghyeon Park, et al.*2026*
37. **[Illusions of Confidence? Diagnosing LLM Truthfulness via Neighborhood Consistency](https://md.research.elamur.ai/free/en/papers/xu-2026-illusions-confidence-diagnosing-llm-truthfulness/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Haoming Xu, Ningyuan Zhao, Yunzhi Yao, et al.*2026*
38. **[Test-Time Training for Zero-Resource Dense Retrieval Reranking](https://md.research.elamur.ai/free/en/papers/liu-2026-test-time-training-zero-resource/) [Workshop on Towards Knowledgeable Foundation Models](https://md.research.elamur.ai/free/en/venues/knowfm/)**Shiyan Liu, Yichen Li*2026*
39. **[Think Hard Only When Needed: A Hybrid Best-of-N and Beam Search for Efficient Test-Time Compute](https://md.research.elamur.ai/free/en/papers/suh-2026-think-hard-only-when-needed/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Hyewon Suh, Chaojian Li, Cheng-Jhih Shih, et al.*2026*
40. **[VL-Calibration: Decoupled Confidence Calibration for Large Vision-Language Models Reasoning](https://md.research.elamur.ai/free/en/papers/xiao-2026-vl-calibration-decoupled-confidence-calibration/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Wenyi Xiao, Xinchi XU, Leilei Gan*2026*
41. **[Your Reasoning Benchmark May Not Test Reasoning: Revealing Perception Bottleneck in Abstract Reasoning Benchmarks](https://md.research.elamur.ai/free/en/papers/wang-2026-your-reasoning-benchmark-may-not/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xinhe Wang, Jin Huang, Xingjian Zhang, et al.*2026*

97 more citing papers of our library have no page here yet.

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