[提交于2026年7月9日]

ASMR：面向船舶维修报告撰写的智能体模式生成

查看本文PDF：PDF HTML（实验性）
作者：Sohrab Namazi Nia 等8人

摘要：本文研究自动模式生成问题：给定跨多个表单类别的历史船舶维修和运营报告集合，自动发现紧凑且信息丰富的模式，以捕获每种报告类型的基本信息需求。为应对这一挑战，我们提出 ASMR，一个由两个专用智能体组成的模块化智能体框架。字段生成智能体（Field Generation Agent）从历史叙述中提取语义概念，并通过自适应多粒度聚类生成候选模式字段；结构优化智能体（Structural Optimizer Agent）则采用强化学习来识别紧凑、信息丰富且无冗余的模式表示。由此产生的模式可引导报告作者撰写出更完整、一致且可操作的报告。初步结果展示了所提方法的前景，并突出了在数据管理、智能体人工智能与人本人工智能交叉领域的若干开放性研究挑战。

提交历史

发件人：Sohrab Namazi Nia 查看邮件
[v1] Thu, 9 Jul 2026 07:25:28 UTC (6,648 KB)

[Submitted on 9 Jul 2026]

Title:ASMR: Agentic Schema Generation for Ship Maintenance Report Writing

View a PDF of the paper titled ASMR: Agentic Schema Generation for Ship Maintenance Report Writing, by Sohrab Namazi Nia and 8 other authors

View PDF HTML (experimental)

Abstract: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.

Submission history

From: Sohrab Namazi Nia [

view email

]

[v1]

Thu, 9 Jul 2026 07:25:28 UTC (6,648 KB)