CANDO：面向布局设计优化的协作式智能体网络
原标题：CANDO: Cooperative Agentic Network for Layout Design Optimization
AI 摘要
该论文提出 ALPS-Bench 基准，包含 1000 个专业标注的真实设施布局及基于结构化设计手册的评分协议，用于评估约束感知的布局生成。同时提出 CANDO，一个无需训练的多智能体协作框架，通过验证驱动的迭代循环精炼布局。CANDO 在 PubLayNet、RICO 和 PKU-PosterLayout 基准上超越了现有训练模型和基于 LLM 的基线，表明协作式智能体设计是约束感知布局合成的有效方案。
正文节选
CANDO: Cooperative Agentic Network for Layout Design Optimization Abstract Layout generation for real-world facilities is a challenging problem, requiring reasoning over irregular site boundaries, heterogeneous orientations, access-aware placements, and motion-planning feasibility. Yet, most existing layout benchmarks in the generative AI space target simpler placements over rectangular domains and rely on distributional metrics such as FID and IoU that reward conformity to dataset priors, thus