🔗 Who cites «Revisiting the Test-Time Scaling of o1-like Models: Do they Truly Possess Test-Time Scaling Capabilities?»

32 papers of our library cite it (of the 149,439 whose bibliographies we read) · 11 with a page here, the most cited first · ← back to «Revisiting the Test-Time Scaling of o1-like Models: Do they Truly Possess Test-Time Scaling Capabilities?»

1. Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language ModelsScience China Information SciencesQiguang Chen, Libo Qin, Jinhao Liu, et al.2025 · 176 citations
2. ThinkPrune: Pruning Long Chain-of-Thought of LLMs via Reinforcement LearningarXiv:2504.01296Bairu Hou, Yang Zhang, Jiabao Ji, et al.2025 · 67 citations
3. Efficient Reasoning Models: A SurveyarXiv:2504.10903Sicheng Feng, Gongfan Fang, Xinyin Ma, et al.2025 · 37 citations
4. DRP: Distilled Reasoning Pruning with Skill-aware Step Decomposition for Efficient Large Reasoning ModelsarXiv:2505.13975Yuxuan Jiang, Dawei Li, Francis Ferraro2025 · 21 citations
5. AlphaOne: Reasoning Models Thinking Slow and Fast at Test TimeConference on Empirical Methods in Natural Language Processing (EMNLP)Junyu Zhang, Runpei Dong, Han Wang, et al.2025 · 10 citations
6. RoT: Enhancing Table Reasoning with Iterative Row-Wise TraversalsConference on Empirical Methods in Natural Language Processing (EMNLP)Xuanliang Zhang, Dingzirui Wang, Keyan Xu, et al.2025 · 10 citations
7. Language of Thought Shapes Output Diversity in Large Language ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Shaoyang Xu, Wenxuan Zhang2026 · 2 citations
8. ARISE: An Adaptive Resolution-Aware Metric for Test-Time Scaling Evaluation in Large Reasoning ModelsFindings of the Association for Computational LinguisticsZhangyue Yin, Qiushi Sun, Zhiyuan Zeng, et al.2026 · 1 citation
9. Thinking Long, but Short: Stable Sequential Test-Time Scaling for Large Reasoning ModelsFindings of the Association for Computational LinguisticsMichael R. Metel, Yufei Cui, Boxing Chen, et al.2026 · 1 citation
10. Format-Adapter: Improving Reasoning Capability of LLMs by Adapting Suitable FormatFindings of the Association for Computational LinguisticsDingzirui Wang, Xuanliang Zhang, Rongyu Cao, et al.2026
11. RFS-Guard: Detecting Reasoning Hallucinations via Cross-Phase Routing Focus in Large Reasoning ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Zihang Liu, Fang Zhouhua, Hui Liu, et al.2026

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

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