Delegating Earth science model calibration to knowledge-guided LLM agents
Applying process-based Earth science models to new sites and questions often requires expert calibration decisions. Reliable delegation to language-model agents must preserve scientific competence and reproducibility. Here we develop a general calibration harness (GCH) for end-to-end delegation from a calibration request to assessed results. Constrained by a knowledge infrastructure (KI) and a validation convention, agents define parameter domains, observation mappings and objectives, and generate model-specific code. GCH checks the formulations against the same declarations and freezes them before numerical search and assessment, which require no further agent judgement. Agent-authored w...
| Authors | Zhanliang Zhu, Ziwei Li, Liujun Zhu et al. |
|---|---|
| Primary category | cs.AI |
| Published | 13 October 2026 |
| Last updated at source | 13 October 2026 |
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