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AI planning

topic2 events
papersSEP 10 04:00 UTC

Researchers propose learned chain-of-thought verification to improve LLM reasoning

A new preprint on arXiv (2603.03538) introduces an approach in which a learned verifier checks the step-by-step reasoning chains produced by large language models, with the goal of catching mistakes in complex reasoning and planning tasks. The authors argue that adding this verification stage makes model outputs more reliable despite the inherent error-proneness of LLM-generated reasoning.

llms/reasoning/verification/chain-of-thought
papersSEP 10 04:00 UTC

Researchers propose grounded evaluation and repair for LLM-generated PDDL planning problems

A new arXiv paper examines how large language models convert natural-language planning descriptions into PDDL problem instances, arguing that common checks like syntactic validity or planner success can overstate actual quality. The authors introduce an evaluation and repair framework that grounds assessment more firmly in the underlying planning task to better catch and fix flawed outputs.

llm/planning/pddl/evaluation