CANDO: Cooperative Agentic Network for Layout Design Optimization

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 discounting design innovation. Motivated by these gaps, we introduce $\textbf{ALPS-Bench}$, a benchmark of $1,000$ professionally annotated real-world facility layouts paired with an instance-specific scoring protocol grounded in a structured design manual. As a strong baseline for $\textbf{ALPS-Bench}$, we propose $\texttt{CANDO}$, a training-free multi-agent framework in which specialized agents iteratively refine layouts through a verification-grounded loop, concentrating reasoning on strategic spatial decisions. We demonstrate that $\texttt{CANDO}$ surpasses state-of-the-art baselines on the widely adopted $\textbf{PubLayNet}$ and $\textbf{RICO}$ benchmarks by a significant margin, establishing cooperative agentic design as a broadly effective recipe for constraint-aware layout synthesis. Code and benchmark will be released upon acceptance.