多智能体协作评估设计，生成可操作反馈。
Agentic Design Review System
- 多个智能体在元智能体协调下协同分析设计
- 在DRS-BENCH基准上超越现有方法，生成有效反馈
- 适合需要专业设计评审的AI系统开发者
图形设计评估需从对齐、构图、美学和配色等多方面综合判断。传统方式依赖多位专家评审意见整合。为此，我们提出Agentic Design Review System（AgenticDRS），由多个智能体在元智能体协调下协同分析设计。通过基于图匹配的上下文样例选择与独特的提示扩展方法，使各智能体具备设计感知能力。为评估该框架，我们构建了DRS-BENCH基准。在该基准上，与适配问题设定的先进基线模型进行充分实验对比，并辅以关键消融实验，验证了AgenticDRS在设计评估与生成可操作反馈方面的有效性。希望本工作能引起对这一实用但研究不足方向的关注。
原文摘要 · Abstract (English)
Evaluating graphic designs involves assessing it from multiple facets like alignment, composition, aesthetics and color choices. Evaluating designs in a holistic way involves aggregating feedback from individual expert reviewers. Towards this, we propose an Agentic Design Review System (AgenticDRS), where multiple agents collaboratively analyze a design, orchestrated by a meta-agent. A novel in-context exemplar selection approach based on graph matching and a unique prompt expansion method plays central role towards making each agent design aware. Towards evaluating this framework, we propose DRS-BENCH benchmark. Thorough experimental evaluation against state-of-the-art baselines adapted to the problem setup, backed-up with critical ablation experiments brings out the efficacy of Agentic-DRS in evaluating graphic designs and generating actionable feedback. We hope that this work will attract attention to this pragmatic, yet under-explored research direction.
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