VisCritic-GIS: A Visualization-Critic–Empowered Framework for Multi-Agent Geospatial Task Reasoning and Execution

Multi-agent GIS systems are increasingly emerging as a general paradigm for complex geospatial tasks. However, many existing approaches rely on text-only large language models (LLMs) as the primary reasoning substrate. In the absence of explicit geometric constraints and verifiable evidence, spatial relations are often indirectly represented through linguistic statistical correlations. This makes LLMs prone to inconsistency when interpreting and inferring topological, directional, and distance relations in geospatial data, and leads to error accumulation across multi-step tool invocations and long-horizon decision-making, ultimately degrading the accuracy and efficiency of task reasoning and execution. In this work, we propose VisCritic-GIS, a multi-agent framework for geospatial task reasoning and execution driven by visualized evidence review. VisCritic-GIS introduces a Visualization Generation Agent and a Visualization Critic Agent into conventional multi-agent GIS pipelines. The generation agent renders key spatial data and intermediate results into 2D maps, explicitly externalizing spatial relations in a visual form. The critic agent leverages multimodal LLMs to read and critically review these map-based evidence, producing textual feedback on spatial relations, anomalous results, and reasoning deviations, which constrains and drives iterative refinement of other agents’ reasoning trajectories and toolchain configurations. We build evaluation protocols over representative remote sensing and geospatial tasks, and systematically demonstrate that VisCritic-GIS improves task accuracy, execution efficiency, and interpretability. Overall, our framework provides a mechanism for shifting geospatial reasoning from “text-only probabilistic completion” toward “visually grounded, verifiable inference,” thereby strengthening the robustness of spatial relation understanding in multi-agent GIS systems.