ASMR: Agentic Schema Generation for Ship Maintenance Report Writing

In this paper, we study the automatic schema generation problem: given a collection of historical ship maintenance and operational reports across multiple form categories, automatically discover compact and informative schemas that capture the essential information requirements of each report type.

To address this challenge, we propose ASMR, a modular agentic framework consisting of two specialized agents.

A Field Generation Agent extracts semantic concepts from historical narratives and generates candidate schema fields through adaptive multi-granularity clustering, while a Structural Optimizer Agent employs reinforcement learning to identify compact, informative, and non-redundant schema representations.

The resulting schemas can guide report authors toward producing more complete, consistent, and actionable reports.

Preliminary results demonstrate the promise of the proposed approach and highlight several open research challenges at the intersection of data management, agentic AI, and human-centered AI.