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Revealing the Barriers of Language Agents in Planning
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Jian Xie, Kexun Zhang, Jiangjie Chen, Siyu Yuan, Kai Zhang, Yikai Zhang, Lei Li, Yanghua Xiao. Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2025.
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It remains difficult for LLMs to produce effective and coherent plans.
“However, as it remains difficult for LLMs to produce effective and coherent plans (Valmeekam et al., 2023; 2024; Xie et al., 2025), recent methods”
From iCLP: Large Language Model Reasoning with Implicit Cognition Latent Planning (2025) · cites this paper as Xie et al., 2025
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Research works printed but not linked: 42
- Yanan Chen, Ali Pesaranghader, Tanmana Sadhu, and Dong Hoon Yi. 2024. Can we rely on llm agents to draft long-horizon plans? let's take travelplanner as an example. arXiv preprint arXiv:2408.06318. · arXiv
- Gautier Dagan, Frank Keller, and Alex Lascarides. 2023. Dynamic planning with a llm. arXiv preprint arXiv:2308.06391. · arXiv
- Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Amy Yang, Angela Fan, et al. 2024. The llama 3 herd of models. arXiv preprint arXiv:2407.21783. · arXiv
- Aaron Fisher, Cynthia Rudin, and Francesca Dominici. 2019. All models are wrong, but many are useful: Learning a variable's importance by studying an en- tire class of prediction models simultaneously. Jour- nal of Machine Learning Research, 20(177):1-81.
- Yao Fu, Dong-Ki Kim, Jaekyeom Kim, Sungryull Sohn, Lajanugen Logeswaran, Kyunghoon Bae, and Honglak Lee. 2024. Autoguide: Automated generation and selection of state-aware guidelines for large language model agents. arXiv preprint arXiv:2403.08978. · arXiv
- Gemini G Team, Rohan Anil, Sebastian Borgeaud, Yonghui Wu, Jean-Baptiste Alayrac, Jiahui Yu, Radu Soricut, Johan Schalkwyk, Andrew M Dai, Anja Hauth, et al. 2023. Gemini: a family of highly capable multimodal models. arXiv preprint arXiv:2312.11805. · arXiv
- Carlos Gómez-Rodríguez and Paul Williams. 2023. A confederacy of models: a comprehensive evaluation of llms on creative writing. In Findings of the Associ- ation for Computational Linguistics: EMNLP 2023, pages 14504-14528.
- Yu Gu, Yiheng Shu, Hao Yu, Xiao Liu, Yuxiao Dong, Jie Tang, Jayanth Srinivasa, Hugo Latapie, and Yu Su. 2024. Middleware for llms: Tools are instrumental for language agents in complex environments. arXiv preprint arXiv:2402.14672. · arXiv
- Atharva Gundawar, Mudit Verma, Lin Guan, Karthik Valmeekam, Siddhant Bhambri, and Subbarao Kamb- hampati. 2024. Robust planning with llm-modulo framework: Case study in travel planning. arXiv preprint arXiv:2405.20625. · arXiv
- Subbarao Kambhampati, Karthik Valmeekam, Lin Guan, Mudit Verma, Kaya Stechly, Siddhant Bham- bri, Lucas Paul Saldyt, and Anil B Murthy. 2024. Position: Llms can't plan, but can help planning in llm-modulo frameworks. In Forty-first International Conference on Machine Learning.
- Shahar Katz and Yonatan Belinkov. 2023. Visit: Vi- sualizing and interpreting the semantic information flow of transformers. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 14094-14113.
- Bo Liu, Yuqian Jiang, Xiaohan Zhang, Qiang Liu, Shiqi Zhang, Joydeep Biswas, and Peter Stone. 2023. Llm+p: Empowering large language mod- els with optimal planning proficiency. arXiv preprint arXiv:2304.11477. · arXiv
- Nelson F. Liu, Kevin Lin, John Hewitt, Ashwin Paran- jape, Michele Bevilacqua, Fabio Petroni, and Percy Liang. 2024. Lost in the middle: How language mod- els use long contexts. Transactions of the Association for Computational Linguistics, 12:157-173. · DOI · Paper page
- Renze Lou, Kai Zhang, Jian Xie, Yuxuan Sun, Jan- ice Ahn, Hanzi Xu, Yu su, and Wenpeng Yin. 2024. MUFFIN: Curating multi-faceted instructions for im- proving instruction following. In The Twelfth Inter- national Conference on Learning Representations.
- Pan Lu, Baolin Peng, Hao Cheng, Michel Galley, Kai- Wei Chang, Ying Nian Wu, Song-Chun Zhu, and Jianfeng Gao. 2023. Chameleon: Plug-and-play compositional reasoning with large language models. In Thirty-seventh Conference on Neural Information Processing Systems.
- Haoyan Luo and Lucia Specia. 2024. From understand- ing to utilization: A survey on explainability for large language models. arXiv preprint arXiv:2401.12874. · arXiv
- Bodhisattwa Prasad Majumder, Bhavana Dalvi Mishra, Peter Jansen, Oyvind Tafjord, Niket Tandon, Li Zhang, Chris Callison-Burch, and Peter Clark. 2023. Clin: A continually learning language agent for rapid task adaptation and generalization. arXiv preprint arXiv:2310.10134. · arXiv
- Pramod Kaushik Mudrakarta, Ankur Taly, Mukund Sun- dararajan, and Kedar Dhamdhere. 2018. Did the model understand the question? In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1896-1906.
- OpenAI. 2022. Chatgpt. OpenAI. 2023a. Gpt-4 technical report. arXiv preprint arXiv:2303.08774. · arXiv
- Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. 2016. " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135- 1144.
- Noah Shinn, Federico Cassano, Ashwin Gopinath, Karthik Narasimhan, and Shunyu Yao. 2024. Re- flexion: Language agents with verbal reinforcement learning. Advances in Neural Information Process- ing Systems, 36.
- David Silver, Julian Schrittwieser, Karen Simonyan, Ioannis Antonoglou, Aja Huang, Arthur Guez, Thomas Hubert, Lucas Baker, Matthew Lai, Adrian Bolton, et al. 2017. Mastering the game of go without human knowledge. nature, 550(7676):354-359.
- Yifan Song, Da Yin, Xiang Yue, Jie Huang, Sujian Li, and Bill Yuchen Lin. 2024. Trial and error: Exploration-based trajectory optimization of LLM agents. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Vol- ume 1: Long Papers), pages 7584-7600, Bangkok, Thailand. Association for Computational Linguistics. · DOI
- Kaya Stechly, Karthik Valmeekam, and Subbarao Kamb- hampati. 2024. On the self-verification limitations of large language models on reasoning and planning tasks. arXiv preprint arXiv:2402.08115. · arXiv
- Theodore Sumers, Shunyu Yao, Karthik Narasimhan, and Thomas Griffiths. 2024. Cognitive architectures for language agents. Transactions on Machine Learn- ing Research.
- Mukund Sundararajan, Ankur Taly, and Qiqi Yan. 2017. Axiomatic attribution for deep networks. In Interna- tional conference on machine learning, pages 3319- 3328. PMLR.
- Karthik Valmeekam, Matthew Marquez, Alberto Olmo, Sarath Sreedharan, and Subbarao Kambhampati. 2024a. Planbench: An extensible benchmark for evaluating large language models on planning and reasoning about change. Advances in Neural Infor- mation Processing Systems, 36.
- Karthik Valmeekam, Kaya Stechly, and Subbarao Kamb- hampati. 2024b. Llms still can't plan; can lrms? a preliminary evaluation of openai's o1 on planbench. arXiv preprint arXiv:2409.13373. · arXiv
- Peter West, Chandra Bhagavatula, Jack Hessel, Jena Hwang, Liwei Jiang, Ronan Le Bras, Ximing Lu, Sean Welleck, and Yejin Choi. 2022. Symbolic knowledge distillation: from general language mod- els to commonsense models. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Hu- man Language Technologies, pages 4602-4625, Seat- tle, United States. Association for Computational Linguistics. · DOI
- Siye Wu, Jian Xie, Jiangjie Chen, Tinghui Zhu, Kai Zhang, and Yanghua Xiao. 2024. How easily do irrelevant inputs skew the responses of large language models? In First Conference on Language Modeling.
- Jian Xie, Kai Zhang, Jiangjie Chen, Renze Lou, and Yu Su. 2024a. Adaptive chameleon or stubborn sloth: Revealing the behavior of large language models in knowledge conflicts. In The Twelfth International Conference on Learning Representations.
- Jian Xie, Kai Zhang, Jiangjie Chen, Tinghui Zhu, Renze Lou, Yuandong Tian, Yanghua Xiao, and Yu Su. 2024b. Travelplanner: A benchmark for real-world planning with language agents. In Forty-first Interna- tional Conference on Machine Learning.
- An Yang, Baosong Yang, Binyuan Hui, Bo Zheng, Bowen Yu, Chang Zhou, Chengpeng Li, Chengyuan Li, Dayiheng Liu, Fei Huang, et al. 2024. Qwen2 technical report. arXiv preprint arXiv:2407.10671. · arXiv
- Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Thomas L. Griffiths, Yuan Cao, and Karthik R Narasimhan. 2023. Tree of thoughts: Deliberate problem solving with large language models. In Pro- ceedings of NeurIPS.
- Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik R Narasimhan, and Yuan Cao. 2022. React: Synergizing reasoning and acting in language models. In Proceedings of ICLR.
- Da Yin, Faeze Brahman, Abhilasha Ravichander, Khy- athi Chandu, Kai-Wei Chang, Yejin Choi, and Bill Yuchen Lin. 2024. Agent lumos: Unified and modular training for open-source language agents. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 12380-12403, Bangkok, Thai- land. Association for Computational Linguistics. · DOI · Paper page
- Aohan Zeng, Mingdao Liu, Rui Lu, Bowen Wang, Xiao Liu, Yuxiao Dong, and Jie Tang. 2023a. Agenttun- ing: Enabling generalized agent abilities for llms. Preprint, arXiv:2310.12823. · arXiv
- Fanlong Zeng, Wensheng Gan, Yongheng Wang, Ning Liu, and Philip S Yu. 2023b. Large language models for robotics: A survey. arXiv preprint arXiv:2311.07226. · arXiv
- Kai Zhang, Bernal Jiménez Gutiérrez, and Yu Su. 2023. Aligning instruction tasks unlocks large language models as zero-shot relation extractors. In Findings of ACL.
- Yikai Zhang, Siyu Yuan, Caiyu Hu, Kyle Richardson, Yanghua Xiao, and Jiangjie Chen. 2024. TimeArena: Shaping efficient multitasking language agents in a time-aware simulation. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 3894- 3916, Bangkok, Thailand. Association for Computa- tional Linguistics. · DOI
- Andrew Zhao, Daniel Huang, Quentin Xu, Matthieu Lin, Yong-Jin Liu, and Gao Huang. 2024. Expel: Llm agents are experiential learners. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 38, pages 19632-19642.
- Zifan Zheng, Yezhaohui Wang, Yuxin Huang, Shichao Song, Bo Tang, Feiyu Xiong, and Zhiyu Li. 2024b. Attention heads of large language models: A survey. arXiv preprint arXiv:2409.03752. · arXiv
Cited by
Sorted by citations
iCLP: Large Language Model Reasoning with Implicit Cognition Latent Planning
Chen, S., & Niu, D. (2025) · arXiv · doi:10.48550/arxiv.2512.24014
Metadata
- Authors
- Xie, J., Zhang, K., Chen, J., Yuan, S., Zhang, K., Zhang, Y., Li, L., & Xiao, Y.
- Affiliations
- Fudan University (CN)
- ByteDance (China) (CN)
- Venue
- Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
- Published
- 2025
- Pages
- 1872-1888
- OpenAlex
- W4411119640
- Cited by
- 11
- References
- 0
- Citations by year
- 2026: 5 · 2025: 6
- FWCI
- 7.73
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
- Physical Sciences › Computer Science › Artificial Intelligence › AI-based Problem Solving and Planning
- Keywords
- language agents, planning, barriers, large language models, human-AI collaboration
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