Constructing coherent spatial memory in LLM agents through graph rectification

Given a map description through global traversal navigation instructions, an LLM can often infer the implicit spatial layout and answer user queries by providing shortest paths. However, such context-dependent querying becomes incapable as environments grow larger, motivating the need for incremental map construction that builds a complete topological graph from stepwise observations. We propose LLM-MapRepair, a framework for LLM-driven construction and map repair, designed to detect, localize, and correct structural inconsistencies in incrementally constructed navigation graphs. Our contributions include a Version Control mechanism for graph construction, an Edge Impact Score for repair prioritization, and a cleaned variant of the MANGO benchmark tailored for LLM-driven map construction and repair. We evaluate the framework on four evaluation settings: a synthetic per-component ablation (gpt-4.1, n=20 seeds per cell), a cross-vendor sweep over seven LLMs from OpenAI, Anthropic, and Google on both synthetic and TextWorld procedurally-generated text-adventure games, a repair-stage evaluation on all 42 cleaned-MANGO games with non-zero residual conflicts (534 conflicts; three vendors x three modes plus two non-LLM references), and an end-to-end natural-text deployment on Chapters 16-17 of Dream of the Red Chamber. On the DRC deployment, LLM-MapRepair achieves 94.3% node recall (+8.6 pp over direct LLM mapping) and 88.2% edge recall (+55.8 pp), using GPT-4.1; the recall improvements come with predicted node and edge counts that are roughly 4x the ground-truth counts (Table 4), reflecting the discretization-driven over-generation trade-off we discuss in the Limitations.