Year: 2026
Type: conference-paper
Abstract: While Large Language Models (LLMs) demonstrate 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 introduce EVGeoQA, a novel benchmark built upon Electric Vehicle (EV) charging scenarios that features a distinct location-anchored and dual-objective design.Specifically, each query in EVGeoQA is explicitly bound to a user's real-time coordinate and integrates the dual objectives of a charging necessity and a co-located activity preference.To systematically assess models in such complex settings, we further propose GeoRover, a general evaluation framework based on a tool-augmented agent architecture to evaluate the LLMs' capacity for dynamic, multi-objective exploration.Our experiments reveal that while LLMs successfully utilize tools to address sub-tasks, they struggle with long-range spatial exploration.Notably, we observe an emergent capability: LLMs can summarize historical exploration trajectories to enhance exploration efficiency.These findings establish EVGeoQA as a challenging testbed for future geo-spatial intelligence.The dataset and prompts are available at https://github.com/kg-bnu/EVGeoQA.
Authors: Jianfei Wu, Zhichun Wang, Zhensheng Wang, Zhiyu He
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Awards: G2417891945, G3861646076
OpenAlex ID: W7166857323