🧑‍🔬 Subbarao Kambhampati

🏛️ Affiliations

🎓Arizona State UniversityTempe · United States1992–2026 · 342 paperscurrent🎓Stanford UniversityStanford · United States1990–2004 · 9 papers🎓University of Maryland, College ParkCollege Park · United States1986–2005 · 5 papersEducation until 1989

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 3 more institutions on single bylines are not listed.

🧭 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📅 Works by year

2020s

📄 Papers

The most cited first (OpenAlex and our library).

1. 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.
2. Learning and Leveraging Verifiers to Improve Planning Capabilities of Pre-trained Language Models (2023 · 10 citations)
   This paper examines why fine-tuned language models plan poorly. In the Blocksworld domain, the authors find that the main failure is proposing actions whose preconditions do not hold in the current state. They fine-tune GPT-2 as a generator that reads a textual goal and current state and outputs the next action and…

All works

7 works of theirs in our library, the most cited first; 2 with a page here, the others link their original until we summarise them.

1. Plan Explanations as Model Reconciliation: Moving Beyond Explanation as Soliloquy ↗International Joint Conference on Artificial Intelligence · no summary yetTathagata Chakraborti et al.2017 · 250 citations
2. 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
3. On the Planning Abilities of Large Language Models : A Critical Investigation ↗arXiv:2305.15771 · no summary yetKarthik Valmeekam et al.2023 · 191 citations
4. Leveraging Pre-trained Large Language Models to Construct and Utilize World Models for Model-based Task Planning ↗arXiv:2305.14909 · no summary yetLin Guan et al.2023 · 115 citations
5. LLMs Can't Plan, But Can Help Planning in LLM-Modulo Frameworks ↗arXiv:2402.01817 · Proceedings of the 41 st International Conference on Machine Learning, Vienna, Austria. PMLR 235, 2024 · no summary yetSubbarao Kambhampati et al.2024 · 88 citations
6. 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
7. Learning and Leveraging Verifiers to Improve Planning Capabilities of Pre-trained Language ModelsarXiv:2305.17077Daman Arora, Subbarao Kambhampati2023 · 10 citations

Venues

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

• Conference on Neural Information Processing Systems (NeurIPS): NeurIPS 2022
• International Joint Conference on Artificial Intelligence

🔗 Cited by

Papers of our library cite their works 452 times (of the 149,439 papers whose bibliographies we read); 62 citing papers with a page here →.

Updated 30 October 2026

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