arXiv:2510.04195v2 Announce Type: replace Abstract: 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 increm
The increasing complexity of AI agent applications in real-world environments necessitates robust spatial reasoning and memory, pushing research towards more coherent and scalable solutions.
Improving LLM spatial memory and error correction is critical for developing autonomous agents capable of navigating and understanding large, dynamic environments, crucial for tasks ranging from logistics to robotics.
LLMs can move beyond simple context-dependent querying to incrementally build and repair complex topological graphs for spatial understanding, enabling more sophisticated and reliable agent behaviors.
- · AI agent developers
- · Robotics companies
- · Logistics and mapping services
- · Developers of autonomous systems
- · Systems relying on static or purely context-dependent spatial understanding
More reliable and scalable autonomous agents become feasible for complex spatial tasks.
This improved reliability could accelerate the deployment of AI agents in physical and digital infrastructure management.
Advanced spatial reasoning in agents may lead to new forms of environmental interaction and infrastructure optimization across various industries.
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Read at arXiv cs.AI