Automating Thought of Search: A Journey Towards Soundness and Completeness (Student Abstract)

Abstract Large language models (LLMs) now turn their attention to search. Recently, Thought of Search (ToS) proposed defining the search space with code, having an LLM produce that code. ToS requires a human in the loop, collaboratively producing a sound successor function and goal test, achieving impressive 100% accuracy on all the tested datasets. In this work, we automate ToS (AutoToS), completely taking the human out of the loop of solving planning problems. AutoToS guides the language model step by step towards the generation of sound and complete search components, through feedback from both generic and domain specific unit tests. We achieve 100% accuracy, with minimal feedback iterations, using LLMs of various sizes on all evaluated domains.