发表机构
University Of Texas At Arlington（阿灵顿得克萨斯大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对模式工程缺乏通用框架的问题，提出类型化工作流框架及LLM智能体执行管线，实现模式操作的可组合查询，并在WIPO数据上验证三种查询。
AI 中文摘要
组织日益依赖从数据中获得的洞察来做出重要决策。其中许多洞察涉及结构性行为，我们称之为模式。生成这些模式可能需要处理多种模式类型，对它们执行不同的操作，并将这些操作组合成工作流，我们将这一活动称为模式工程。目前尚无原则性框架来组织此类工作流：分析师手工设计每个工作流，现有系统自动化特定的分析目标，而非以通用、可组合的形式表示模式操作。我们引入一个类型化框架，通过一组固定的模块化组件表示每种模式，并将模式操作表达为指定输入和输出组件的查询。链接查询可组合工作流，而任何单个查询的执行保持不受约束。我们用基于LLM的智能体执行器实例化该框架，并使用世界知识产权组织（WIPO）报告和专利数据，对两种时间模式评估示例挖掘、示例对齐和模式归纳查询。所有三种查询在四种LLM设置中执行，这些设置因目标组件而异，而追踪使我们能够按执行阶段定位错误。
英文摘要
Organizations increasingly depend on insights drawn from data to inform consequential decisions. Many concern structural behaviors, which we call patterns. Producing them may require working with multiple pattern types, performing different operations over them, and composing those operations into workflows, an activity we call pattern engineering. No principled framework exists for organizing such workflows: analysts devise each by hand, and existing systems automate particular analytical objectives rather than representing pattern operations in a general, composable form. We introduce a typed framework that represents each pattern through a fixed set of modular components, and expresses pattern operations as queries which specify input and output components. Chaining queries composes workflows, while execution of any individual query remains unconstrained. We instantiate the framework with an LLM-based agentic executor and evaluate example-mining, example-alignment, and pattern-induction queries over two temporal patterns, using World Intellectual Property Organization (WIPO) reports and patent data. All three queries are carried out across four LLM settings, which perform differently by target component, while the traces allow us to localize errors by execution stage.
