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arXiv · 2608.25462

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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Abstract

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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Yuexin Sun, Zhaohui Wang, Ruiyang Liu, Demian Kong, Qian He, Gaofeng He, Huamin Wang. 2026-08-30. TailorCoPilot: Enabling Agentic Pattern Making with Version-Controlled State Tracking. https://arxiv.org/abs/2608.25462

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