Academic Journal
Deploying Efficient LLM Agents on Maritime Autonomous Surface Ships: Fine-Tuning, RAG, and Function Calling in a Mid-Size Model.
| Τίτλος: | Deploying Efficient LLM Agents on Maritime Autonomous Surface Ships: Fine-Tuning, RAG, and Function Calling in a Mid-Size Model. |
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| Συγγραφείς: | Ren, Yiling, Chen, Mozi, Weng, Junjie, Zhang, Shengkai, Xiao, Xuedou, Liu, Kezhong |
| Πηγή: | Information; Mar2026, Vol. 17 Issue 3, p284, 15p |
| Θεματικοί όροι: | Ships, Intelligent agents, Subroutines (Computer programs), Edge computing, Machine learning, Navigation, Natural language processing, Language models |
| Περίληψη: | Deploying Large Language Models (LLMs) on Maritime Autonomous Surface Ships (MASS) entails a critical trade-off between reasoning depth, inference latency, and hardware constraints. To fill the existing gap, we introduce MARTIAN (Maritime Agent for Real-time Tactical Inference And Navigation), a 14B-parameter decision support agent engineered for edge deployment on standard vessel hardware (e.g., the NVIDIA Jetson AGX Orin). Central to our approach is the Cognitive Core architecture, which utilizes a verified dataset of 21,800 Chain-of-Thought (CoT) instruction–response pairs to align general linguistic capabilities with maritime procedural logic. Empirical evaluations demonstrate that MARTIAN achieves an overall accuracy of 73.23% (SFT only) and 81.16% (SFT + RAG) on the Bilingual Maritime Multiple-Choice Questionnaire (BM-MCQ), a standardized assessment dataset constructed based on Officer of the Watch (OOW) competencies. Notably, the SFT-only configuration attains 78.53% on pure-logic-intensive COLREG tasks—surpassing the 72B-parameter Qwen-2.5 foundation model in this domain—while maintaining a real-time inference latency of 22.4 ms/token. Crucially, our ablation studies support a nuanced Interference Hypothesis: while RAG significantly enhances factual recall in knowledge-intensive domains (boosting total accuracy from 73.23% to 81.16%), it concurrently introduces semantic noise that degrades performance in pure logic reasoning tasks (e.g., COLREG maneuvering accuracy decreases from 78.53% to 77.36%). On the basis of this finding, we identify and empirically motivate a decoupled cognitive design principle that separates procedural reflexes (via SFT) from declarative knowledge (via RAG). While the full implementation of an adaptive routing mechanism is deferred to future work, the ablation results presented herein offer a validated, cost-effective reference architecture for deploying transparent and regulation-compliant AI on resource-constrained merchant vessels. [ABSTRACT FROM AUTHOR] |
| Copyright of Information is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Βάση Δεδομένων: | Complementary Index |
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| Items | – Name: Title Label: Title Group: Ti Data: Deploying Efficient LLM Agents on Maritime Autonomous Surface Ships: Fine-Tuning, RAG, and Function Calling in a Mid-Size Model. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Ren%2C+Yiling%22">Ren, Yiling</searchLink><br /><searchLink fieldCode="AR" term="%22Chen%2C+Mozi%22">Chen, Mozi</searchLink><br /><searchLink fieldCode="AR" term="%22Weng%2C+Junjie%22">Weng, Junjie</searchLink><br /><searchLink fieldCode="AR" term="%22Zhang%2C+Shengkai%22">Zhang, Shengkai</searchLink><br /><searchLink fieldCode="AR" term="%22Xiao%2C+Xuedou%22">Xiao, Xuedou</searchLink><br /><searchLink fieldCode="AR" term="%22Liu%2C+Kezhong%22">Liu, Kezhong</searchLink> – Name: TitleSource Label: Source Group: Src Data: Information; Mar2026, Vol. 17 Issue 3, p284, 15p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Ships%22">Ships</searchLink><br /><searchLink fieldCode="DE" term="%22Intelligent+agents%22">Intelligent agents</searchLink><br /><searchLink fieldCode="DE" term="%22Subroutines+%28Computer+programs%29%22">Subroutines (Computer programs)</searchLink><br /><searchLink fieldCode="DE" term="%22Edge+computing%22">Edge computing</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Navigation%22">Navigation</searchLink><br /><searchLink fieldCode="DE" term="%22Natural+language+processing%22">Natural language processing</searchLink><br /><searchLink fieldCode="DE" term="%22Language+models%22">Language models</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Deploying Large Language Models (LLMs) on Maritime Autonomous Surface Ships (MASS) entails a critical trade-off between reasoning depth, inference latency, and hardware constraints. To fill the existing gap, we introduce MARTIAN (Maritime Agent for Real-time Tactical Inference And Navigation), a 14B-parameter decision support agent engineered for edge deployment on standard vessel hardware (e.g., the NVIDIA Jetson AGX Orin). Central to our approach is the Cognitive Core architecture, which utilizes a verified dataset of 21,800 Chain-of-Thought (CoT) instruction–response pairs to align general linguistic capabilities with maritime procedural logic. Empirical evaluations demonstrate that MARTIAN achieves an overall accuracy of 73.23% (SFT only) and 81.16% (SFT + RAG) on the Bilingual Maritime Multiple-Choice Questionnaire (BM-MCQ), a standardized assessment dataset constructed based on Officer of the Watch (OOW) competencies. Notably, the SFT-only configuration attains 78.53% on pure-logic-intensive COLREG tasks—surpassing the 72B-parameter Qwen-2.5 foundation model in this domain—while maintaining a real-time inference latency of 22.4 ms/token. Crucially, our ablation studies support a nuanced Interference Hypothesis: while RAG significantly enhances factual recall in knowledge-intensive domains (boosting total accuracy from 73.23% to 81.16%), it concurrently introduces semantic noise that degrades performance in pure logic reasoning tasks (e.g., COLREG maneuvering accuracy decreases from 78.53% to 77.36%). On the basis of this finding, we identify and empirically motivate a decoupled cognitive design principle that separates procedural reflexes (via SFT) from declarative knowledge (via RAG). While the full implementation of an adaptive routing mechanism is deferred to future work, the ablation results presented herein offer a validated, cost-effective reference architecture for deploying transparent and regulation-compliant AI on resource-constrained merchant vessels. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Information is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/info17030284 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 15 StartPage: 284 Subjects: – SubjectFull: Ships Type: general – SubjectFull: Intelligent agents Type: general – SubjectFull: Subroutines (Computer programs) Type: general – SubjectFull: Edge computing Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Navigation Type: general – SubjectFull: Natural language processing Type: general – SubjectFull: Language models Type: general Titles: – TitleFull: Deploying Efficient LLM Agents on Maritime Autonomous Surface Ships: Fine-Tuning, RAG, and Function Calling in a Mid-Size Model. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Ren, Yiling – PersonEntity: Name: NameFull: Chen, Mozi – PersonEntity: Name: NameFull: Weng, Junjie – PersonEntity: Name: NameFull: Zhang, Shengkai – PersonEntity: Name: NameFull: Xiao, Xuedou – PersonEntity: Name: NameFull: Liu, Kezhong IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 03 Text: Mar2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 20782489 Numbering: – Type: volume Value: 17 – Type: issue Value: 3 Titles: – TitleFull: Information Type: main |
| ResultId | 1 |