将地球科学模型校准委托给知识引导的 LLM 智能体

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Abstract: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 workflows executed across six model families, with assessment identifying unmet criteria. In snow-model tests, five of five knowledge-guided specifications executed on the first attempt, compared with none of five given only the model name, achieving validation Nash-Sutcliffe efficiency (NSE) of 0.81-0.94 against 0.57 for defaults. Under a common objective, independent GR4J formulations yielded scores within 0.001 NSE; re-evaluating eleven saved parameter vectors reproduced their metrics. In ten paired VIC tests with matched budgets, fixed search was non-inferior to continuing agent proposals (margin, -0.02 NSE; median difference, -0.003). This separation enables end-to-end calibration delegation with inspectable scientific choices and reproducible numerical evaluation.

Subjects:	Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE)
Cite as:	arXiv:2610.18378 [cs.AI]
	(or arXiv:2610.18378v1 [cs.AI] for this version)
	https://doi.org/10.48550/arXiv.2610.18378 arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Ziwei Li [view email]
[v1] Tue, 13 Oct 2026 13:32:20 UTC (7,764 KB)