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Can LLM Agents Improve Imperfect Earth System Model Parameterizations?

Paper recorded by Signals 4 on 2026-11-02 in cs.LG. Abstract reproduced from arXiv; link to the original below.

Published 2026-11-02 on arXiv · recorded by Signals 4 on 2026-11-03

Category: cs.LG · 机器学习 · first seen 2026-11-03

Abstract

Earth system models (ESMs) rely on parameterizations to represent unresolved processes such as convection, cloud microphysics, and turbulence. However, translating advances in physical understanding into stable, validated parameterizations and integrating them into ESMs often takes years. Agentic AI could accelerate this process and enable more systematic exploration of design choices. Using warm-

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#28 most recent of 703 cs.LG papers we have recorded · ↑ newer: Nonparametric Intensity Estimation for Covariate-Driven Poisson Proces · ↓ older: MIRA: Mutual Information guided calibration for Reliable Test-Time Ada
Cite this page: Can LLM Agents Improve Imperfect Earth System Model Parameterizations?: the #28 most recent of 703 cs.LG papers we have recorded (as of 2026-11-02). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/can-llm-agents-improve-imperfect-earth-system-model-parameterizations.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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