Constructing coherent spatial memory in LLM agents through graph rectification

The paper introduces LLM-MapRepair, a framework for constructing and repairing topological navigation graphs using large language models (LLMs). Key features include a Version Control mechanism for graph construction and an Edge Impact Score for prioritizing repairs, evaluated across seven LLMs including GPT-4.1, achieving 94.3% node recall and 88.2% edge recall on the Dream of the Red Chamber deployment. This work is significant for practitioners as it enhances LLM capabilities in spatial reasoning and map construction, addressing challenges in larger environments through incremental updates and improved accuracy in pathfinding tasks.