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EVE: A Domain-Specific LLM Framework for Earth Intelligence

About

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.

\`Alex R. Atrio, Antonio Lopez, Jino Rohit, Yassine El Ouahidi, Marcello Politi, Vijayasri Iyer, Umar Jamil, S\'ebastien Brati\`eres, Nicolas Long\'ep\'e• 2026

Related benchmarks

TaskDatasetResultRank
Multiple Choice Question Answering (Single)Earth Observation
Accuracy96.35
7
Hallucination DetectionEarth Observation
F1 Score84.7
7
Multiple Choice Question Answering (Multiple)Earth Observation
IoU86.12
7
Open-ended Question AnsweringEarth Observation
Judge Score96.4
7
Open-Ended Question Answering (with Context)Earth Observation
Judge Score78.28
7
Hallucination DetectionEO and Earth Sciences Hallucination
F1 Score84.7
5
Multiple-choice Question AnsweringEO and Earth Sciences MCQA Multiple
IoU86.12
5
Multiple-choice Question AnsweringEO and Earth Sciences MCQA Single
Accuracy96.35
5
Open-ended Question AnsweringEO and Earth Sciences Open-Ended QA
Judge Score96.4
5
Overall Performance RankingEO and Earth Sciences Combined Benchmarks
Rank1.33
5
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