---
title: "Who cites Idan Shenfeld — 134 papers of our library"
description: "134 papers of the Elamur Research library cite the works of Idan Shenfeld, found in their bibliographies; the most cited first."
canonical: "https://research.elamur.ai/free/en/authors/idan-shenfeld/cited-by/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/idan-shenfeld/cited-by/)

[home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / [Idan Shenfeld](https://md.research.elamur.ai/free/en/authors/idan-shenfeld/) / cited by

# Who cites Idan Shenfeld

182 citations of their works from 134 papers of our library (of the 149,439 whose bibliographies we read) · 38 with a page here, the most cited first · [← Idan Shenfeld](https://md.research.elamur.ai/free/en/authors/idan-shenfeld/)

1. **[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*
2. [**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/)
3. [**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/)
4. [**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/)
5. [**Self-Distillation Enables Continual Learning arXiv:2601.19897**Idan Shenfeld, Mehul Damani, Jonas Hubotter, et al.*2026 · 62 citations*](https://md.research.elamur.ai/free/en/papers/shenfeld-2026-self-distillation-enables-continual-learning/)
6. [**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/)
7. [**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/)
8. [**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/)
9. [**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/)
10. [**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/)
11. [**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/)
12. [**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/)
13. [**Revisiting On-Policy Distillation: Empirical Failure Modes and Simple Fixes arXiv:2603.25562**Yuqian Fu, Haohuan Huang, Kaiwen Jiang, et al.*2026 · 31 citations*](https://md.research.elamur.ai/free/en/papers/fu-2026-revisiting-policy-distillation-empirical-failure/)
14. [**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/)
15. [**HDPO: Hybrid Distillation Policy Optimization via Privileged Self-Distillation arXiv:2603.23871**Ken Ding*2026 · 8 citations*](https://md.research.elamur.ai/free/en/papers/ding-2026-hdpo-hybrid-distillation-policy-optimization/)
16. [**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/)
17. [**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/)
18. [**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/)
19. [**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/)
20. [**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/)
21. **[Inference-Time Scaling of Verification: Self-Evolving Deep Research Agents via Test-Time Rubric-Guided Verification](https://md.research.elamur.ai/free/en/papers/wan-2026-inference-time-scaling-verification-self/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yuxuan Wan, Tianqing Fang, Zaitang LI, et al.*2026 · 4 citations*
22. **[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*
23. **[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*
24. **[On-Policy Self-Distillation for Efficient Diffusion Language Models with Early-Stage Calibration](https://md.research.elamur.ai/free/en/papers/zhu-2026-policy-self-distillation-efficient-diffusion/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Huaisheng Zhu, MingYu Liu, Junze Liu, et al.*2026 · 2 citations*
25. **[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*
26. **[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*
27. **[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*
28. **[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*
29. **[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*
30. **[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*
31. **[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*
32. **[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*
33. **[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*
34. **[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*
35. **[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*
36. [**ProBel: Propaganda Detection with Techniques, Spans, and Explanations arXiv:2608.22388**Mohamed Bayan Kmainasi, Ali Ezzat Shahroor, Elisa Sartori, et al.*2026*](https://md.research.elamur.ai/free/en/papers/kmainasi-2026-probel-propaganda-detection-techniques-spans/)
37. **[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*
38. **[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*

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

- [← back to](https://md.research.elamur.ai/free/en/authors/idan-shenfeld/) — Idan Shenfeld
