🔗 Who cites «DAST: Difficulty-Adaptive Slow-Thinking for Large Reasoning Models»

57 papers of our library cite it (of the 149,439 whose bibliographies we read) · 28 with a page here, the most cited first · ← back to «DAST: Difficulty-Adaptive Slow-Thinking for Large Reasoning Models»

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. Stop Overthinking: A Survey on Efficient Reasoning for Large Language ModelsarXiv:2503.16419Yang Sui, Yu-Neng Chuang, Guanchu Wang, et al.2025 · 157 citations
3. Dynamic Early Exit in Reasoning ModelsarXiv:2504.15895Chenxu Yang, Qingyi Si, Yongjie Duan, et al.2025 · 74 citations
4. Reasoning Models Can Be Effective Without ThinkingarXiv:2504.09858Wenjie Ma, Jingxuan He, Charlie Snell, et al.2025 · 58 citations
5. A Survey of Efficient Reasoning for Large Reasoning Models: Language, Multimodality, and BeyondarXiv:2503.21614Xiaoye Qu, Yafu Li, Zhao-Chen Su, et al.2025 · 55 citations
6. AdaptThink: Reasoning Models Can Learn When to ThinkConference on Empirical Methods in Natural Language Processing (EMNLP)Jiajie Zhang, Nianyi Lin, Lei Hou, et al.2025 · 44 citations
7. Efficient Reasoning Models: A SurveyarXiv:2504.10903Sicheng Feng, Gongfan Fang, Xinyin Ma, et al.2025 · 37 citations
8. Answer Convergence as a Signal for Early Stopping in ReasoningConference on Empirical Methods in Natural Language Processing (EMNLP)Xin Liu, Lu Wang2025 · 22 citations
9. ConCISE: Confidence-guided Compression in Step-by-step Efficient ReasoningConference on Empirical Methods in Natural Language Processing (EMNLP)Ziqing Qiao, Yongheng Deng, Jiali Zeng, et al.2025 · 19 citations
10. FoE: Forest of Errors Makes the First Solution the Best in Large Reasoning ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Kehan Jiang, Haonan Dong, Zhaolu Kang, et al.2026 · 15 citations
11. NeuReasoner: Towards Explainable, Controllable, and Unified Reasoning via Mixture-of-NeuronsAnnual Meeting of the Association for Computational Linguistics (ACL)Haonan Dong, Kehan Jiang, Haoran Ye, et al.2026 · 13 citations
12. Long-Short Chain-of-Thought Mixture Supervised Fine-Tuning Eliciting Efficient Reasoning in Large Language ModelsarXiv:2505.03469Bin Yu, Hang Yuan, Haotian Li, et al.2025 · 13 citations
13. Optimizing Length Compression in Large Reasoning ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Zhengxiang Cheng, Dongping Chen, Mingyang Fu, et al.2026 · 11 citations
14. Step Potential Advantage Estimation: Harnessing Intermediate Confidence and Correctness for Efficient Mathematical ReasoningFindings of the Association for Computational LinguisticsFei Wu, Zhenrong Zhang, Qikai Chang, et al.2026 · 11 citations
15. AutoL2S: Auto Long-Short Reasoning for Efficient Large Language ModelsFindings of the Association for Computational LinguisticsFeng Luo, Yu-Neng Chuang, Guanchu Wang, et al.2026 · 7 citations
16. SAT: Balancing Reasoning Accuracy and Efficiency with Stepwise Adaptive ThinkingAnnual Meeting of the Association for Computational Linguistics (ACL)Weiyang Huang, Xuefeng Bai, Kehai Chen, et al.2026 · 3 citations
17. Think How to Think: Mitigating Overthinking with Autonomous Difficulty Cognition in Large Reasoning ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Yongjiang Liu, Haoxi Li, Xiaosong Ma, et al.2026 · 2 citations
18. Anti-Length Shift: Dynamic Outlier Truncation for Training Efficient Reasoning ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Wei Wu, Liyi Chen, Congxi Xiao, et al.2026 · 1 citation
19. Not All Tokens Matter: Towards Efficient LLM Reasoning via Token Significance in Reinforcement LearningAnnual Meeting of the Association for Computational Linguistics (ACL)Hanbing Liu, Lang Cao, Yuanyi Ren, et al.2026 · 1 citation
20. A Survey of Reinforcement Learning for Large Language Models under Data Scarcity: Challenges and SolutionsAnnual Meeting of the Association for Computational Linguistics (ACL)Zhiyin Yu, Yuchen Mou, Juncheng Yan, et al.2026
21. AdapThink: Adaptive Thinking Preferences for Reasoning Language ModelsFindings of the Association for Computational LinguisticsWenyue Xu, Xu Wan, Wei Wang, et al.2026
22. Exploration-Exploitation Reshaping towards Efficient Reasoning for Large Language ModelsFindings of the Association for Computational LinguisticsYufeng Shi, Weilin Luo, Yuxiang Zhang, et al.2026
23. Generalizable LLM Learning of Graph Synthetic Data with Post-training AlignmentFindings of the Association for Computational LinguisticsYizhuo Zhang, Heng Wang, Shangbin Feng, et al.2026
24. MirageBackdoor: A Stealthy Attack that Induces Think-Well-Answer-Wrong ReasoningAnnual Meeting of the Association for Computational Linguistics (ACL)Yizhe Zeng, Wei Zhang, Yunpeng Li, et al.2026
25. Shorten After You're Right: Lazy Length Penalties for Reasoning RLFindings of the Association for Computational LinguisticsDanlong Yuan, Tian Xie, Shaohan Huang, et al.2026
26. Think Less, Know More: State-Aware Reasoning Compression with Knowledge Guidance for Efficient ReasoningFindings of the Association for Computational LinguisticsYi Sui, Chaozhuo Li, Dawei Song2026
27. Think Smart, Not Hard: Difficulty Adaptive Reasoning for Large Audio Language ModelsFindings of the Association for Computational LinguisticsZhichao Sheng, Shilin Zhou, Chen Gong, et al.2026
28. Vertical Routing: A Cost-Efficient Collaboration Routing FrameworkTransactions of the Association for Computational Linguistics (TACL)Si Shen, Peijun Shen, Danhao Zhu2026

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

↑ back to top

This page in Markdown → https://md.research.elamur.ai/free/en/papers/shen-2025-dast-difficulty-adaptive-slow-thinking/cited-by/