53 citations · 178 across the 12 of their papers we have counts for
6 papers · 1 filter
Planning in Strawberry Fields: Evaluating and Improving the Planning and Scheduling Capabilities of LRM o1
Karthik Valmeekam, Kaya Stechly, Atharva Gundawar +1
The ability to plan a course of action that achieves a desired state of affairs has long been considered a core competence of intelligent agents and has been an integral part of AI…
LLMs Still Can't Plan; Can LRMs? A Preliminary Evaluation of OpenAI's o1 on PlanBench
Karthik Valmeekam, Kaya Stechly, Subbarao Kambhampati
The ability to plan a course of action that achieves a desired state of affairs has long been considered a core competence of intelligent agents and has been an integral part of AI…
Robust Planning with LLM-Modulo Framework: Case Study in Travel Planning
Atharva Gundawar, Mudit Verma, Lin Guan +3
As the applicability of Large Language Models (LLMs) extends beyond traditional text processing tasks, there is a burgeoning interest in their potential to excel in planning and re…
Chain of Thoughtlessness? An Analysis of CoT in Planning
Kaya Stechly, Karthik Valmeekam, Subbarao Kambhampati
Large language model (LLM) performance on reasoning problems typically does not generalize out of distribution. Previous work has claimed that this can be mitigated with chain of t…
On the Self-Verification Limitations of Large Language Models on Reasoning and Planning Tasks
Kaya Stechly, Karthik Valmeekam, Subbarao Kambhampati
There has been considerable divergence of opinion on the reasoning abilities of Large Language Models (LLMs). While the initial optimism that reasoning might emerge automatically w…
LLMs Can't Plan, But Can Help Planning in LLM-Modulo Frameworks
Subbarao Kambhampati, Karthik Valmeekam, Lin Guan +5
There is considerable confusion about the role of Large Language Models (LLMs) in planning and reasoning tasks. On one side are over-optimistic claims that LLMs can indeed do these…