Can large language models reason and plan?
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While humans sometimes do show the capability of correcting their own erroneous guesses with self-critiquing, there seems to be no basis for that assumption in the case of LLMs.
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The parameters of modern LLMs encode a remarkable amount of world knowledge.
“It is well-accepted, even among skeptical researchers, that the parameters of modern LLMs encode a remarkable amount of world knowledge [2, 12, 23, 28, 31, 39, 62, 79], which can be leveraged by an LLM in flexible and robust ways [81, 83, 93].”
From HippoRAG: Neurobiologically Inspired Long-Term Memory for Large Language Models (2024) · cites this paper as [39]
There is a considerable body of work suggesting that the reasoning process in LLMs is fragile and not formal.
“There is a considerable body of work suggesting that the reasoning process in LLMs is fragile and not formal (Kambhampati, 2024; Valmeekam et al., 2022; 2024; Nezhurina et al., 2024; McCoy et al., 2023; Zhang et al., 2023), even though it appears that these models understand symbols and can work with them to some limited degree (Boix-Adserà et al., 2024).”
From GSM-Symbolic: Understanding the Limitations of Mathematical Reasoning in Large Language Models (2024) · cites this paper as Kambhampati, 2024
AGI represents visionary goal of crafting AI systems that emulate comprehensive and multifaceted aspects of human cognition, focused on developing AI with capability for holistic understanding and complex reasoning closely aligning with depth and breadth of human cognitive abilities.
“AGI, representing the visionary goal of crafting AI systems that emulate the comprehensive and multifaceted aspects of human cognition, is a subdomain focused on developing AI with the capability for holistic understanding and complex reasoning that closely aligns with the depth and breadth of human cognitive abilities [282], [283], [32].”
From From Google Gemini to OpenAI Q* (Q-Star): A Survey on Reshaping the Generative Artificial Intelligence (AI) Research Landscape (2025) · cites this paper as [32]
AGI Safety and Containment acknowledges potential risks associated with highly advanced AI systems, focused on ensuring these systems are technically proficient and ethically aligned with human values and societal norms.
“AGI Safety and Containment acknowledges the potential risks associated with highly advanced AI systems, focused on ensuring that these advanced systems are not only technically proficient but also ethically aligned with human values and societal norms [15], [32], [11].”
From From Google Gemini to OpenAI Q* (Q-Star): A Survey on Reshaping the Generative Artificial Intelligence (AI) Research Landscape (2025) · cites this paper as [32]
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Cites
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On the Planning Abilities of Large Language Models - A Critical InvestigationPreprint
Valmeekam, K., Marquez, M., Sreedharan, S., et al. (2023) · Neural Information Processing Systems · doi:10.48550/arxiv.2305.15771 · Cited by 525 · see citing works
On the Planning Abilities of Large Language Models - A Critical Investigation
Valmeekam, K., Marquez, M., Sreedharan, S., & Subbarao Kambhampati, B. (2023) · neural information processing systems · doi:10.52202/075280-3320 · Cited by 98 · see citing works
Valmeekam, K., Marquez, M., Olmo, A., Sreedharan, S., & Kambhampati, S. (2022) · arXiv · doi:10.48550/arxiv.2206.10498 · Cited by 70 · see citing works
Self-citationValmeekam, K., Marquez, M., Olmo, A., Sreedharan, S., & Kambhampati, S. (2023) · Advances in Neural Information Processing Systems 36 · doi:10.52202/075280-1693 · Cited by 70 · see citing works
Guan, L., Valmeekam, K., Sreedharan, S., & Kambhampati, S. (2023) · arXiv · doi:10.48550/arxiv.2305.14909 · Cited by 64 · see citing works
Guan, L., Valmeekam, K., Sreedharan, S., & Subbarao Kambhampati, B. (2023) · neural information processing systems · doi:10.52202/075280-3459 · Cited by 64 · see citing works
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Self-citationChanging the nature of AI research
Kambhampati, S. (2022) · Communications of the ACM · doi:10.1145/3546954 · Cited by 10 · see citing works
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Research works printed but not linked: 6
- Kambhampati, S. (2022). AI as (an ersatz) natural science? Communica- tions of the ACM, 65(9), 8-9.
- Valmeekam, K., Olmo, A., Sreedharan, S., & Kambhampati, S. (2022). Large language models still can't plan (a benchmark for LLMs on planning and reasoning about change). arXiv preprint arXiv:2206.104 98.
- Kambhampati, S., Valmeekam, K., Guan, L., Stechly, K., Verma, M., Bhambri, S., Saldyt, L., & Murthy, A. (2024). LLMs can't plan, but can help planning in LLM-Modulo frameworks. arXiv preprint 2402. 01817.
- Valmeekam, K., Marquez, M., & Kambhampati, S. (2023). Can large language models really improve by self-critiquing their own plans? In NeurIPS 2023 Foundation Models for Decision Making Workshop.
- Stechly, K., Marquez, M., & Kambhampati, S. (2023). GPT-4 Doesn't Know It's Wrong: An Analysis of Iterative Prompting for Reasoning Problems. In NeurIPS 2023 Foundation Models for Decision Making Workshop.
- Kambhampati, S., Valmeekam, K., Marquez, M., & Guan, L. (2023). On the role of large language models in planning. Tutorial presented at the International Conference on Automated Planning and Scheduling (ICAPS), Prague.
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- Authors
- Kambhampati, S.
- Affiliations
- School of Computing & Augmented Intelligence Arizona State University
- Venue
- Annals of the New York Academy of Sciences
- Publisher
- Wiley
- Published
- 2024-03-06
- Volume
- 1534
- Issue
- 1
- Pages
- 15-18
- ISSN
- 0077-8923 (ISSN-L)
- 1749-6632
- OpenAlex
- W4392517661
- Cited by
- 83
- References
- 6
- Citations by year
- 2026: 36 · 2025: 37 · 2024: 10
- FWCI
- 12.30
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Annals of the New York Academy of Sciences (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by)
- arXiv (Cornell University) (cc-by)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › AI-based Problem Solving and Planning
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Keywords
- large language models, reasoning, planning, self-criticism, error correction, metacognition
- MeSH terms
- Artificial Intelligence, Humans, Language
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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