@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 (Volume 6: Industry Track)",
month = jul,
year = "2026",
address = "San Diego, California, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-industry.72/",
doi = "10.18653/v1/2026.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."
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<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.</abstract>
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%0 Conference Proceedings
%T EVE: A Domain-Specific LLM Framework for Earth Intelligence
%A Atrio, Àlex R.
%A Lopez, Antonio
%A Rohit, Jino
%A Ouahidi, Yassine El
%A Politi, Marcello
%A Iyer, Vijayasri
%A Jamil, Umar
%A Bratières, Sébastien
%A Longépé, Nicolas
%Y Li, Yunyao
%Y Rehm, Georg
%Y Tu, Mei
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-394-4
%F atrio-etal-2026-eve
%X 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.
%R 10.18653/v1/2026.acl-industry.72
%U https://aclanthology.org/2026.acl-industry.72/
%U https://doi.org/10.18653/v1/2026.acl-industry.72
%P 1037-1054
Markdown (Informal)
[EVE: A Domain-Specific LLM Framework for Earth Intelligence](https://aclanthology.org/2026.acl-industry.72/) (Atrio et al., ACL 2026)
ACL
- Àlex R. Atrio, Antonio Lopez, Jino Rohit, Yassine El Ouahidi, Marcello Politi, Vijayasri Iyer, Umar Jamil, Sébastien Bratières, and Nicolas Longépé. 2026. EVE: A Domain-Specific LLM Framework for Earth Intelligence. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track), pages 1037–1054, San Diego, California, USA. Association for Computational Linguistics.