用AI自动发现更精准的地理空间算法，提升气候与可持续性研究效率。
GeoEvolve: Automating Geospatial Model Discovery via Multi-Agent Large Language Models
- 多智能体LLM结合地理知识库，引导算法进化方向。
- 在空间插值和不确定性量化任务上，误差降低13%-21%，性能提升17%。
- 适合地理信息、环境科学等领域研究者，推动可信科学计算。
地理空间建模为应对可持续性和气候变化等全球挑战提供关键解决方案。现有基于大语言模型（LLM）的算法发现框架如AlphaEvolve擅长生成通用代码，但缺乏解决复杂地理空间问题所需的领域知识和多步推理能力。我们提出GeoEvolve，一种将进化搜索与地理空间领域知识结合的多智能体LLM框架，可自动设计并优化地理空间算法。GeoEvolve采用双层嵌套循环：内层由代码进化器生成并变异候选方案；外层由智能控制器评估全局最优解，并调用GeoKnowRAG模块——一个结构化地理知识库，注入地理学理论先验。这种知识引导的进化使搜索聚焦于理论合理且计算高效的算法。我们在两个经典基础任务上评估：空间插值（克里金法）和空间不确定性量化（地理空间置信预测）。在这些基准测试中，GeoEvolve自动改进并发现了新算法，在经典模型基础上融入地理空间理论。空间插值误差（RMSE）降低13%-21%，不确定性估计性能提升17%。消融实验表明，领域引导检索对稳定高质量进化至关重要。结果表明，GeoEvolve为可扩展的知识驱动地理空间建模提供了路径，开启了可信高效人工智能科学发现的新机遇。
原文摘要 · Abstract (English)
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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