🧑‍🔬 Sarath Sreedharan

🏛️ Affiliations

🎓Colorado State UniversityFort Collins · United States2021–2026 · 40 paperscurrent🎓Arizona State UniversityTempe · United States2015–2022 · 54 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine.

🧭 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. 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.
3. 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…

All works

4 works of theirs in our library, the most cited first; 3 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 InvestigationConference on Neural Information Processing Systems (NeurIPS)Karthik Valmeekam, Matthew Marquez, Sarath Sreedharan, et al.2023 · 191 citations
4. 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

🤝 Co-authors here

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

• Karthik Valmeekam ×3
• Subbarao Kambhampati ×3
• Matthew Marquez ×2
• Lin Guan

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) ×3: NeurIPS 2023 ×2, NeurIPS 2022
• International Joint Conference on Artificial Intelligence

🔗 Cited by

Papers of our library cite their works 329 times (of the 149,439 papers whose bibliographies we read); 63 citing papers with a page here →.

Updated 2 November 2026

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