EVE: A Domain-Specific LLM Framework for Earth Intelligence
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arXiv
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| Format: | Preprint |
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2026
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| author | Atrio, Àlex R. Lopez, Antonio Rohit, Jino Ouahidi, Yassine El Politi, Marcello Iyer, Vijayasri Jamil, Umar Bratières, Sébastien Longépé, Nicolas |
| author_facet | Atrio, Àlex R. Lopez, Antonio Rohit, Jino Ouahidi, Yassine El Politi, Marcello Iyer, Vijayasri Jamil, Umar Bratières, Sébastien Longépé, Nicolas |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_13071 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | EVE: A Domain-Specific LLM Framework for Earth Intelligence Atrio, Àlex R. Lopez, Antonio Rohit, Jino Ouahidi, Yassine El Politi, Marcello Iyer, Vijayasri Jamil, Umar Bratières, Sébastien Longépé, Nicolas Computation and Language Artificial Intelligence 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. |
| title | EVE: A Domain-Specific LLM Framework for Earth Intelligence |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2604.13071 |