LivingFlow: Agentic Reinforcement Learning for Circulation-Aware Scene Refinement

Comfortable living spaces require both functional furniture arrangements and smooth circulation—the movement of people between functional locations during everyday activities. A collision-free layout can still force people through tight passages or unnecessary detours, even when every destination is reachable. We introduce LivingFlow, an agentic reinforcement learning framework that iteratively rearranges furniture in existing indoor layouts. To specify circulation requirements, a frozen large language model generates natural-language activity chains describing how people use the space throughout a day; transitions between successive uses define fixed movement tasks that a geometric executor evaluates for furniture access, clearance, and route efficiency as the layout changes. Our framework adapts Group-in-Group Policy Optimization to optimize a trainable editing policy over complete tool cycles, connecting information gathering and editing decisions with executable feedback. Experiments on original 3D-FRONT layouts show improved layout quality and circulation over existing refiners, and confirm the benefits of activity-conditioned evaluation and executable feedback.