发表机构
Carnegie Mellon University; Toyota Research Institute（卡内基梅隆大学; 丰田研究院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
Scanvas是一个AI支持系统，通过分解想法和三种策略操作符在生成式设计空间中系统发现并开发协同机会，实验证明其能显著提升设计概念质量。
AI 中文摘要
优秀的设计往往具有协同性，通过关联目标使现有资源产生更优成果，从而创造超加性价值。然而，在稀疏的设计空间中发现这些协同机会十分困难，当前由大语言模型（LLM）支持的创意工具大多默认采用加性范式，如特征融合、变体生成或局部修补。我们提出Scanvas，一个AI支持的系统，用于系统地发现和开发协同设计机会。Scanvas通过两步计算过程实现协同：首先，将种子想法分解为显式属性（组件、行为、盈余和问题），以丰富设计空间；其次，使用三种基于理论策略的操作符系统性地搜索丰富的想法：解锁或强化目标、将弱点转化为资源，以及跨功能共享组件。我们将Scanvas实现为自动生成流水线和交互式系统。流水线消融实验和一项包含12名专业设计师的用户研究表明，与LLM创意基线相比，Scanvas使用户能够浮现并开发出显著更高质量、更具协同性的概念。
英文摘要
Good design is often synergistic, creating super-additive value by linking goals so that existing resources produce greater outcomes. However, finding these synergistic opportunities in sparse design spaces is difficult, and current LLM-supported ideation tools largely default to additive paradigms such as feature blending, variant generation, or local patching. We present Scanvas, an AI-supported system for systematically discovering and developing synergistic design opportunities. Scanvas operationalizes synergy through a two-step computational process: first, it decomposes seed ideas into explicit properties (components, behaviors, surpluses, and issues) to enrich the design space; second, it systematically searches across enriched ideas using three theory-grounded strategy operators: unlocking or strengthening goals, turning weaknesses into resources, and sharing components across functions. We instantiate Scanvas as an auto-generation pipeline and an interactive system. Pipeline ablations and a user study with 12 professional designers demonstrate that Scanvas enables users to surface and develop significantly higher-quality, synergistic concepts compared to LLM ideation baselines.
