Llamarine: Open-source Maritime Industry-specific Large Language Model
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Abstract (arXiv)
Large Language Models (LLMs) have demonstrated substantial potential in addressing complex reasoning tasks, yet their general-purpose nature often limits their effectiveness in specialized domains such as maritime navigation. To bridge this gap, we introduce Llamarine, the first open-source LLM designed specifically for maritime navigation. Llamarine 1.0 is developed through continued pretraining and fine-tuning on a high-quality corpus comprising maritime textbooks, research publications, and web text from Wikipedia. This domain-specific training enables the model to acquire expert-level knowledge in navigational principles, collision avoidance, route optimization, and regulatory compliance. Our key contributions include (a) the curation of a comprehensive maritime dataset from authoritative sources, ensuring depth and reliability in the model's knowledge base; (b) the development of a foundational model capable of reasoning about complex navigational challenges with greater accuracy than general-purpose LLMs; and (c) the establishment of a benchmark to evaluate performance in maritime-specific decision-making tasks. Experimental results demonstrate that Llamarine outperforms both general-purpose and commercial LLMs in critical navigation-related tasks, such as trajectory planning, risk assessment, and compliance with maritime regulations. By providing an open-source foundation model trained exclusively on high-quality maritime literature, Llamarine paves the way for AI-driven advancements in maritime safety, efficiency, and operational decision-making.
Key facts. Cited by 4 papers in the map (data through 13 Oct 2026). Builds on 17 of its 43 references that are in the map. First posted on arXiv 28 Feb 2025.
Builds on
17 of 43 references.
References · 17
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5 more
- 2024Exploring Boundary of GPT-4V on Marine Analysis: A Preliminary Case Studycited by 9
- 2024KUNPENG: An Embodied Large Model for Intelligent Maritimecited by 4
- 2024SemiKong: Curating, Training, and Evaluating A Semiconductor Industry-Specific Large Language Modelcited by 4
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Built on by
Citing papers per year: 2025: 2; 2026: 2.
4 papers.
- 2025DREAM: Domain-aware Reasoning for Efficient Autonomous Underwater Monitoringcited by 2
- 2025Multi-Model Synthetic Training for Mission-Critical Small Language Modelscited by 1
- 2026Generating Realistic, Protocol-Compliant Maritime Radio Dialogues using Self-Instruct and Low-Rank Adaptationcited by 0
- 2026Earth Science Foundation Models: From Perception to Reasoning and Discoverycited by 0
- 2026Earth Science Foundation Models: From Perception to Reasoning and Discovery9 May 2026
- 2026Generating Realistic, Protocol-Compliant Maritime Radio Dialogues using Self-Instruct and Low-Rank Adaptation16 Feb 2026
- 2025DREAM: Domain-aware Reasoning for Efficient Autonomous Underwater Monitoring17 Sep 2025
- 2025Multi-Model Synthetic Training for Mission-Critical Small Language Models16 Sep 2025