PlanBench: An Extensible Benchmark for Evaluating Large Language Models on Planning and Reasoning about Change (2022 · 222 citations)
Claims that large language models can plan often rest on commonsense tasks, where a correct answer may come from memorized world knowledge rather than actual reasoning.
On the Planning Abilities of Large Language Models : A Critical Investigation (2023 · 191 citations)
This NeurIPS 2023 study asks whether large language models can plan, given claims that web-trained LLMs show emergent reasoning.
Leveraging Pre-trained Large Language Models to Construct and Utilize World Models for Model-based Task Planning (2023 · 115 citations)
The paper argues that LLMs should not act as planners themselves. Instead, they should build the world model that a classical planner then uses. The authors give GPT-4 a list of actions with short natural-language descriptions and have it write PDDL action models, meaning predicates, preconditions and effects. Early…
LLMs Can't Plan, But Can Help Planning in LLM-Modulo Frameworks (2024 · 88 citations)
This position paper responds to two opposing errors in the debate on LLMs and planning. One overstates their reasoning ability. The other treats them as mere text-to-symbol converters for classical solvers. The authors review evidence, much of it from their own group, that autoregressive LLMs cannot reliably produce…

All works

5 works of theirs in our library, the most cited first; 4 with a page here, the others link their original until we summarise them.

PlanBench: An Extensible Benchmark for Evaluating Large Language Models on Planning and Reasoning about ChangeConference on Neural Information Processing Systems (NeurIPS)Karthik Valmeekam, Matthew Marquez, Alberto Olmo, et al.2022 · 222 citations
On the Planning Abilities of Large Language Models : A Critical InvestigationConference on Neural Information Processing Systems (NeurIPS)Karthik Valmeekam, Matthew Marquez, Sarath Sreedharan, et al.2023 · 191 citations
Leveraging Pre-trained Large Language Models to Construct and Utilize World Models for Model-based Task PlanningConference on Neural Information Processing Systems (NeurIPS)Lin Guan, Karthik Valmeekam, Sarath Sreedharan, et al.2023 · 115 citations
LLMs Can't Plan, But Can Help Planning in LLM-Modulo FrameworksarXiv:2402.01817 · Proceedings of the 41 st International Conference on Machine Learning, Vienna, Austria. PMLR 235, 2024Subbarao Kambhampati, Karthik Valmeekam, Lin Guan, et al.2024 · 88 citations
On the Self-Verification Limitations of Large Language Models on Reasoning and Planning Tasks ↗arXiv:2402.08115 · no summary yetKaya Stechly, Karthik Valmeekam, Subbarao Kambhampati2024 · 42 citations

Co-authors here

4 co-authors with a page, the most papers together first.

Where their works here were published, the most works first, with the editions of those that have a page.

Papers of our library cite their works 443 times (of the 149,439 papers whose bibliographies we read); 79 citing papers with a page here →.

Updated 2 November 2026