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Karthik Valmeekam

8 accepted papers

2026

Position: Stop Anthropomorphizing Intermediate Tokens as Reasoning/Thinking Traces!

Subbarao Kambhampati, Karthik Valmeekam, Siddhant Bhambri, Vardhan Palod, Lucas Saldyt, Kaya Stechly +3
ICML 2026poster

Intermediate token generation (ITG), where a model produces output before the solution, has become a standard method to improve the performance of language models on reasoning tasks. These intermediate tokens have been called \say{reasoning traces} or even \say{thoughts} -- implicitly anthropomorphi…

Cited by 39Source ↗Scholar ↗
2025

On the self-verification limitations of large language models on reasoning and planning tasks

Kaya Stechly, Karthik Valmeekam, Subbarao Kambhampati
ICLR 2025poster

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 with scale has been tempered thanks to a slew of counterexamples--ranging from multiplication to simple planning--there p…

Cited by 57Source ↗PDF ↗Scholar ↗
2024

Chain of Thoughtlessness? An Analysis of CoT in Planning

Kaya Stechly, Karthik Valmeekam, Subbarao Kambhampati
NeurIPS 2024poster

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 thought prompting--a method of demonstrating solution procedures--with the intuition that it is possible to in-context te…

Cited by 47Source ↗PDF ↗Scholar ↗
2024

Position: LLMs Can’t Plan, But Can Help Planning in LLM-Modulo Frameworks

Subbarao Kambhampati, Karthik Valmeekam, Lin Guan, Mudit Verma, Kaya Stechly, Siddhant Bhambri +2
ICML 2024spotlight

We argue that auto-regressive LLMs cannot, by themselves, do planning or self-verification (which is after all a form of reasoning), and shed some light on the reasons for misunderstandings in the literature. We will also argue that LLMs should be viewed as universal approximate knowledge sources th…

Cited by 192Source ↗PDF ↗Scholar ↗
2023

Leveraging Pre-trained Large Language Models to Construct and Utilize World Models for Model-based Task Planning

Lin Guan, Karthik Valmeekam, Sarath Sreedharan, Subbarao Kambhampati
NeurIPS 2023poster

There is a growing interest in applying pre-trained large language models (LLMs) to planning problems. However, methods that use LLMs directly as planners are currently impractical due to several factors, including limited correctness of plans, strong reliance on feedback from interactions with simu…

Cited by 201Source ↗PDF ↗code ↗Scholar ↗
2023

On the Planning Abilities of Large Language Models - A Critical Investigation

Karthik Valmeekam, Matthew Marquez, Sarath Sreedharan, Subbarao Kambhampati
NeurIPS 2023spotlight

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 evaluate (1) the effectiveness of LLMs in generating plans autonomously in commonsense planning tasks and (2) the potenti…

Cited by 280Source ↗PDF ↗Scholar ↗
2023

PlanBench: An Extensible Benchmark for Evaluating Large Language Models on Planning and Reasoning about Change

Karthik Valmeekam, Matthew Marquez, Alberto Olmo, Sarath Sreedharan, Subbarao Kambhampati
NeurIPS 2023poster

Generating plans of action, and reasoning about change have long been considered a core competence of intelligent agents. It is thus no surprise that evaluating the planning and reasoning capabilities of large language models (LLMs) has become a hot topic of research. Most claims about LLM planning…

Cited by 191Source ↗PDF ↗code ↗Scholar ↗
2023

Relative Behavioral Attributes: Filling the Gap between Symbolic Goal Specification and Reward Learning from Human Preferences

Lin Guan, Karthik Valmeekam, Subbarao Kambhampati
ICLR 2023poster

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. RLHF) is one way to allow non-expert users to convey complex objectives by expressing preferences over short clips of…

Cited by 13Source ↗PDF ↗code ↗Scholar ↗