We introduce Earth Virtual Expert (EVE), the first open-source, end-to-end initiative for developing and deploying domain-specialized LLMs for Earth Intelligence. At its core is EVE-Instruct, a domain-adapted 24B model built on Mistral Small 3.2 and optimized for reasoning and question answering. On newly constructed Earth Observation and Earth Sciences benchmarks, it outperforms comparable models while preserving general capabilities. We release curated training corpora and the first systematic domain-specific evaluation benchmarks, covering MCQA, open-ended QA, and factuality. EVE further integrates RAG and a hallucination-detection pipeline into a production system deployed via API and GUI, supporting 350 pilot users so far. All models, datasets, and code are ready to be released under open licenses as contributions to our field at huggingface.co/eve-esa and github.com/eve-esa.
Cite
@article{arxiv.2604.13071,
title = {EVE: A Domain-Specific LLM Framework for Earth Intelligence},
author = {Àlex R. Atrio and Antonio Lopez and Jino Rohit and Yassine El Ouahidi and Marcello Politi and Vijayasri Iyer and Umar Jamil and Sébastien Bratières and Nicolas Longépé},
journal= {arXiv preprint arXiv:2604.13071},
year = {2026}
}
Comments
To be published in the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026)