ASMR: Agentic Schema Generation for Ship Maintenance Report Writing

2026-07-11 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Expert, extended

Summary

ASMR is an agentic framework designed for automatically generating structured schemas from historical ship maintenance and operational reports. This modular system employs two specialized agents: a Field Generator Agent, which utilizes an LLM (GPT-4o Mini) to extract semantic concepts from unstructured narratives, cluster them adaptively, and produce candidate schema fields; and a Structural Optimizer Agent, which applies reinforcement learning to refine these candidates into compact, informative, and non-redundant schemas. The framework aims to enhance report completeness and consistency by guiding authors with topic-specific information requirements, while also supporting downstream analytics such as predictive maintenance and anomaly detection. Preliminary evaluations across multiple ship operational form categories, each containing approximately 500 historical forms, demonstrate ASMR's effectiveness in improving schema quality, compactness, and reducing semantic redundancy compared to raw concepts or initial candidate schemas.

Key takeaway

For Machine Learning Engineers or Data Scientists tasked with improving data quality from unstructured operational reports, you should explore agentic frameworks like ASMR. This approach automates schema generation, enhancing report consistency and completeness, which is crucial for reliable downstream analytics such as predictive maintenance. Implementing such a system can significantly reduce manual effort and adapt to evolving reporting requirements, providing structured data from free-form text.

Key insights

Agentic AI and reinforcement learning can automate schema generation from unstructured text.

Principles

• Schema design is multi-objective optimization.
• LLMs excel at semantic concept extraction.
• RL can optimize schema compactness.

Method

ASMR uses a Field Generator Agent for LLM-based concept extraction and clustering, then a Structural Optimizer Agent with reinforcement learning to select, merge, or discard fields for optimal schemas.

In practice

• Guide report authors with structured questions.
• Standardize reports for predictive maintenance.
• Improve interoperability across reporting systems.

Topics

• Agentic AI
• Schema Generation
• Reinforcement Learning
• Large Language Models
• Ship Maintenance Reports
• Unstructured Data Processing

Best for: AI Scientist, Machine Learning Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.