🔗 Who cites «LLMs Can't Plan, But Can Help Planning in LLM-Modulo Frameworks»

77 papers of our library cite it (of the 149,439 whose bibliographies we read) · 18 with a page here, the most cited first · ← back to «LLMs Can't Plan, But Can Help Planning in LLM-Modulo Frameworks»

1. To CoT or not to CoT? Chain-of-thought helps mainly on math and symbolic reasoningarXiv:2409.12183Zayne Sprague, Fangcong Yin, Juan Diego Rodriguez, et al.2024 · 138 citations
2. Best Practices and Lessons Learned on Synthetic DataarXiv:2404.07503Ruibo Liu, Jerry Wei, Fangyu Liu, et al.2024 · 119 citations
3. Multi-Step Reasoning with Large Language Models, a SurveyACM Computing SurveysAske Plaat, Annie Wong, Suzan Verberne, et al.2024 · 106 citations
4. TheAgentCompany: Benchmarking LLM Agents on Consequential Real World TasksConference on Neural Information Processing Systems (NeurIPS)Frank F. Xu, Yufan Song, Boxuan Li, et al.2024 · 68 citations
5. PlanGenLLMs: A Modern Survey of LLM Planning CapabilitiesAnnual Meeting of the Association for Computational Linguistics (ACL)Hui Wei, Zihao Zhang, Shenghua He, et al.2025 · 55 citations
6. AI Agents That MatterarXiv:2407.01502Sayash Kapoor, Benedikt Stroebl, Zachary S. Siegel, et al.2024 · 55 citations
7. SemEval-2026 Task 11: Disentangling Content and Formal Reasoning in Large Language ModelsInternational Workshop on Semantic Evaluation (SemEval)Marco Valentino, Leonardo Ranaldi, Giulia Pucci, et al.2026 · 22 citations
8. AI4Research: A Survey of Artificial Intelligence for Scientific ResearchScience China Information SciencesQiguang Chen, Mingda Yang, Libo Qin, et al.2025 · 21 citations
9. Large Language Models Can Solve Real-World Planning Rigorously with Formal Verification ToolsConference of the North American Chapter of the Association for Computational Linguistics (NAACL)Yilun Hao, Yongchao Chen, Yang Zhang, et al.2025 · 18 citations
10. PlanGEN: A Multi-Agent Framework for Generating Planning and Reasoning Trajectories for Complex Problem SolvingConference on Empirical Methods in Natural Language Processing (EMNLP)Mihir Parmar, Xin Liu, Palash Goyal, et al.2025 · 16 citations
11. Data-driven Discovery with Large Generative ModelsarXiv:2402.13610Bodhisattwa Prasad Majumder, Harshit Surana, Dhruv Agarwal, et al.2024 · 15 citations
12. Problem Solving Through Human–AI Preference-based CooperationComputational LinguisticsSubhabrata Dutta, Timo Kaufmann, Goran Glavaš, et al.2025 · 9 citations
13. Analysing Chain of Thought Dynamics: Active Guidance or Unfaithful Post-hoc Rationalisation?Conference on Empirical Methods in Natural Language Processing (EMNLP)Samuel Lewis-Lim, Xingwei Tan, Zhixue Zhao, et al.2025 · 5 citations
14. AOT*: Efficient Synthesis Planning via LLM-Empowered AND-OR Tree SearchFindings of the Association for Computational LinguisticsXiaozhuang Song, Xuanhao Pan, Xinjian Zhao, et al.2026 · 2 citations
15. Integrating Neural and Symbolic Components in a Model of Pragmatic Question-AnsweringPolina Tsvilodub, Robert D. Hawkins, Michael Franke2025 · 1 citation
16. Beyond Length Scaling: Synergizing Breadth and Depth for Generative Reward ModelsFindings of the Association for Computational LinguisticsQiyuan Zhang, Yufei Wang, Tianhe Wu, et al.2026
17. CARL: Constraint-Aware Reinforcement Learning for Planning with LLMsFindings of the Association for Computational LinguisticsQiuyi Qi, Jinjian Zhang, Mutian Bao, et al.2026
18. Computational models of pragmatic reasoning with flexible generation of meaning and expression alternativesarXiv:2607.18443Polina Tsvilodub, Fausto Carcassi, Michael Franke2026

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