@inproceedings{atrio-etal-2026-eve,
    title = "{EVE}: A Domain-Specific {LLM} Framework for Earth Intelligence",
    author = "Atrio, {\`A}lex R.  and
      Lopez, Antonio  and
      Rohit, Jino  and
      Ouahidi, Yassine El  and
      Politi, Marcello  and
      Iyer, Vijayasri  and
      Jamil, Umar  and
      Brati{\`e}res, S{\'e}bastien  and
      Long{\'e}p{\'e}, Nicolas",
    editor = "Li, Yunyao  and
      Rehm, Georg  and
      Tu, Mei",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics ({ACL} 2026)",
    month = jul,
    year = "2026",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-industry.72/",
    pages = "1037--1054",
    ISBN = "979-8-89176-394-4",
    abstract = "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. All models, datasets, and code are publicly available."
}