mlsanityData to 14 Oct 2026

EVE: A Domain-Specific LLM Framework for Earth Intelligence

arXiv 2604.13071 ↗First posted 20 Mar 2026cs.CL cs.AIcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 18 of its 44 references that are in the map. First posted on arXiv 20 Mar 2026.

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18 of 44 references.

References · 18

  1. 2021TruthfulQA: Measuring How Models Mimic Human Falsehoodscited by 2,975
  2. 2020The Pile: An 800GB Dataset of Diverse Text for Language Modelingcited by 2,411
  3. 2020Don't Stop Pretraining: Adapt Language Models to Domains and Taskscited by 1,565
  4. 2018FEVER: a large-scale dataset for Fact Extraction and VERificationcited by 1,375
  5. 2025Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Modelscited by 1,261
  6. 2024The FineWeb Datasets: Decanting the Web for the Finest Text Data at Scalecited by 1,160
  7. 2023SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Modelscited by 810
  8. 2024Dolma: an Open Corpus of Three Trillion Tokens for Language Model Pretraining Researchcited by 483
  9. 2024Replacing Judges with Juries: Evaluating LLM Generations with a Panel of Diverse Modelscited by 326
  10. 2022RARR: Researching and Revising What Language Models Say, Using Language Modelscited by 287
  11. 2023Nougat: Neural Optical Understanding for Academic Documentscited by 245
  12. 2024Mitigating Catastrophic Forgetting in Large Language Models with Self-Synthesized Rehearsalcited by 118
6 more
  1. 2024UrbanGPT: Spatio-Temporal Large Language Modelscited by 81
  2. 2023K2: A Foundation Language Model for Geoscience Knowledge Understanding and Utilizationcited by 69
  3. 2023AstroLLaMA: Towards Specialized Foundation Models in Astronomycited by 41
  4. 2024Online DPO: Online Direct Preference Optimization with Fast-Slow Chasingcited by 39
  5. 2024An LLM Agent for Automatic Geospatial Data Analysiscited by 21
  6. 2024Truth or Mirage? Towards End-to-End Factuality Evaluation with LLM-Oasiscited by 8

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  1. 2026Earth-Agent-Pro: Towards Real-World Full-Chain Earth Observation with Agents
  2. 2025Earth AI: Unlocking Geospatial Insights with Foundation Models and Cross-Modal Reasoning
  3. 2025Towards LLM Agents for Earth Observation
  4. 2026Geo-Expert: Towards Expert-Level Geological Reasoning via Parameter-Efficient Fine-Tuning
  5. 2026Earth Science Foundation Models: From Perception to Reasoning and Discovery
  6. 2026Risk-Aware LLM Agents for Geospatial Data Retrieval: Design and Preliminary Adversarial Evaluation
  7. 2026EO-Agents: A Three-Agent LLM Pipeline for Earth Observation Hypothesis Generation
  8. 2026Google Earth AI Geospatial Reasoning Agent: Capabilities and a Systematic Evaluation