AI RESEARCH
EVGeoQA: Benchmarking LLMs on Dynamic, Multi-Objective Geo-Spatial Exploration
arXiv CS.LG
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ArXi:2604.07070v1 Announce Type: cross While Large Language Models (LLMs) nstrate remarkable reasoning capabilities, their potential for purpose-driven exploration in dynamic geo-spatial environments remains under-investigated. Existing Geo-Spatial Question Answering (GSQA) benchmarks predominantly focus on static retrieval, failing to capture the complexity of real-world planning that involves dynamic user locations and compound constraints. To bridge this gap, we