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arXiv 2608.25462cs.HCcs.GR

TailorCoPilot:实现带版本控制状态跟踪的智能体式制版

TailorCoPilot: Enabling Agentic Pattern Making with Version-Controlled State Tracking

Yuexin Sun, Zhaohui Wang, Ruiyang Liu, Demian Kong, Qian He, Gaofeng He, Huamin Wang

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中文总结 AI 辅助

TailorCoPilot是基于TailorTrace版本控制后端的智能体式制版系统,可记录专家隐性制版知识,在用户研究中提升了新手的制版任务表现,为生成式AI和学徒制提供支持。

中文摘要 AI 辅助

以经验为驱动的制造业(如服装制版)面临严重的代际技能断层,因为其核心专业知识依赖于日常实践中形成的未成文隐性知识。为解决这一挑战,我们提出TailorCoPilot,这是一个基于专门设计的版本控制后端TailorTrace构建的智能体式制版系统。TailorTrace将制版建模为结构化离散状态,并将制版过程中的转换记录为针对制版几何基元(衣片、边、顶点和针迹)定义的显式操作序列。TailorTrace集成到传统制版GUI中,可在不中断资深专家日常工作流程的情况下无缝记录其隐性制版知识。所记录的知识不仅为新手提供交互式教学支架,还为驱动TailorCoPilot及训练未来生成式AI模型提供坚实基础。在针对新手和进阶新手的用户研究中,与技能适配的基线相比,TailorCoPilot提高了任务完成率,减少了时间消耗和感知工作量,并生成了更高质量的成品。最终,TailorCoPilot展示了一条可行途径,用于捕获基于实践的专业知识,将其付诸应用以支持生成式AI的进步和人类学徒制。

英文摘要

Experience-driven manufacturing, such as garment pattern making, faces a severe generational skills gap because its core expertise relies on undocumented tacit knowledge forged through day-to-day practice. To address this challenge, we present TailorCoPilot, an agentic pattern-making system built upon a specially designed version-control backend TailorTrace. TailorTrace models sewing patterns as structured, discrete states and records their transformations during the pattern-making process as explicit operation sequences defined upon the geometry primitives in the sewing pattern (panels, edges, vertices and stitches). Integrated into a conventional pattern-making GUI, TailorTrace enables seamless documentation of senior experts' tacit pattern-making knowledge without breaking their daily workflow. The documented knowledge further offers interactive, pedagogical scaffolding for novices, while providing a robust foundation to power TailorCoPilot and train future generative AI models. In a user study with novices and advanced novices, TailorCoPilot improved task completion rates, reduced time and perceived workload, and yielded higher-quality artifacts compared to skill-appropriate baselines. Ultimately, TailorCoPilot demonstrates a viable pathway to capture practice-based expertise, operationalizing it to support both generative AI advancements and human apprenticeship.

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