A Living AI Platform for the Earth System Science

Earth system science relies on the integration of knowledge from many branches of geoscience, including climate dynamics, hydrology, ecology, land use and biogeochemical cycles. However, the scientific literature informing these domains has become vast and increasingly difficult to navigate due to its rapid development and disciplinary spread. This complexity makes it difficult to maintain an integrated overview of relevant findings and to identify scientific connections in a systematic manner. Recent advances in generative artificial intelligence (AI) and large language models (LLMs) provide opportunities to support these tasks, particularly when combined with retrieval methods and transparent source attribution.

Here we propose a retrieval-augmented AI platform designed to assist scientific knowledge integration in Earth system science. The platform is conceived as a living system, built on a continuously expanding and updateable knowledge base that aggregates scholarly literature from major scientific databases. User queries initiate targeted retrieval of relevant documents followed by the generation of concise, source-linked summaries using locally hosted open-weighted LLMs. By explicitly grounding outputs in retrieved literature, the platform alleviates the need for manual screening and limits hallucination risks that currently constrain the use of general-purpose LLMs in geoscientific research.

Evaluation of the initial prototype demonstrates that domain-specific retrieval-augmented generation systems can provide reliable, traceable synthesis of Earth system knowledge and help address the growing gap between accelerating publication rates and the need for timely, verifiable scientific assessment.