53 citations · 178 across the 12 of their papers we have counts for
11 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…
Can Large Language Models Really Improve by Self-critiquing Their Own Plans?
Karthik Valmeekam, Matthew Marquez, Subbarao Kambhampati
There have been widespread claims about Large Language Models (LLMs) being able to successfully verify or self-critique their candidate solutions in reasoning problems in an iterat…
On the Planning Abilities of Large Language Models : A Critical Investigation
Karthik Valmeekam, Matthew Marquez, Sarath Sreedharan +1
Intrigued by the claims of emergent reasoning capabilities in LLMs trained on general web corpora, in this paper, we set out to investigate their planning capabilities. We aim to e…
On the Planning Abilities of Large Language Models (A Critical Investigation with a Proposed Benchmark)
Karthik Valmeekam, Sarath Sreedharan, Matthew Marquez +2
Intrigued by the claims of emergent reasoning capabilities in LLMs trained on general web corpora, in this paper, we set out to investigate their planning capabilities. We aim to e…
Relative Behavioral Attributes: Filling the Gap between Symbolic Goal Specification and Reward Learning from Human Preferences
Lin Guan, Karthik Valmeekam, Subbarao Kambhampati
Generating complex behaviors that satisfy the preferences of non-expert users is a crucial requirement for AI agents. Interactive reward learning from trajectory comparisons (a.k.a…
RADAR-X: An Interactive Mixed Initiative Planning Interface Pairing Contrastive Explanations and Revised Plan Suggestions
Karthik Valmeekam, Sarath Sreedharan, Sailik Sengupta +1
Decision support systems seek to enable informed decision-making. In the recent years, automated planning techniques have been leveraged to empower such systems to better aid the h…