GeoEvolve: Automating Geospatial Model Discovery via Multi-Agent Large Language Models

• URL: http://arxiv.org/abs/2509.21593v1
• Date: Thu, 25 Sep 2025 21:03:57 GMT
• Title: GeoEvolve: Automating Geospatial Model Discovery via Multi-Agent Large Language Models
• Authors: Peng Luo, Xiayin Lou, Yu Zheng, Zhuo Zheng, Stefano Ermon,
• Abstract summary: We introduce GeoEvolve, a multi-agent LLM framework that couples evolutionary search with geospatial domain knowledge.<n>We evaluate it on two fundamental and classical tasks: spatial (kriging) and spatial uncertainty.<n>It reduces spatial error (RMSE) by 13-21% and enhances uncertainty estimation performance by 17%.
• Score: 49.257706111340134
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Geospatial modeling provides critical solutions for pressing global challenges such as sustainability and climate change. Existing large language model (LLM)-based algorithm discovery frameworks, such as AlphaEvolve, excel at evolving generic code but lack the domain knowledge and multi-step reasoning required for complex geospatial problems. We introduce GeoEvolve, a multi-agent LLM framework that couples evolutionary search with geospatial domain knowledge to automatically design and refine geospatial algorithms. GeoEvolve operates in two nested loops: an inner loop leverages a code evolver to generate and mutate candidate solutions, while an outer agentic controller evaluates global elites and queries a GeoKnowRAG module -- a structured geospatial knowledge base that injects theoretical priors from geography. This knowledge-guided evolution steers the search toward theoretically meaningful and computationally efficient algorithms. We evaluate GeoEvolve on two fundamental and classical tasks: spatial interpolation (kriging) and spatial uncertainty quantification (geospatial conformal prediction). Across these benchmarks, GeoEvolve automatically improves and discovers new algorithms, incorporating geospatial theory on top of classical models. It reduces spatial interpolation error (RMSE) by 13-21% and enhances uncertainty estimation performance by 17\%. Ablation studies confirm that domain-guided retrieval is essential for stable, high-quality evolution. These results demonstrate that GeoEvolve provides a scalable path toward automated, knowledge-driven geospatial modeling, opening new opportunities for trustworthy and efficient AI-for-Science discovery.

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