Geospatial algorithms start rewriting their own code: seeded with Two-stage K-Models, an evolved version beats its seed across 150 simulated grids
Geospatial algorithm improvement has long relied on researchers hand-crafting each structural fix; an LLM-driven multi-agent framework now delegates candidate generation, evaluation and selection to automated search while the researcher defines the problem and the evaluation. Seeded with Two-stage K-Models and using the Rand Index against the ground-truth partition as fitness, the evolved algorithm was frozen and applied unchanged to 11 real datasets, reaching the lowest cross-dataset mean RMSE and the highest SSR Reduction across climate, health, hydrology and politics.