Luis Lara

Generative Floor Plan Design with LLMs via Reinforcement Learning with Verifiable Rewards

Zhi Hao Luo

Ge Ya Luo

Christopher Pal

An AI system for professional floor plan design needs to be able to precisely control room dimensions and areas (quantitative constraints), … (voir plus)while also balancing functional considerations and design aesthetics. Existing generative approaches focus primarily on respecting the requested connectivity between rooms, but do not support generating floor plans with numerical constraints. We introduce a text‑based floor plan generation approach that fine-tunes a large language model (LLM) on real plans and then applies reinforcement learning with verifiable rewards (RLVR) to enforce both numerical (areas, dimensions) and spatial (topological) constraints. Furthermore, we design a set of constraint adherence metrics to measure how generated floor plans align with user-defined constraints systematically. Our model generates floor plans that satisfy numerical constraints and outperforms existing methods on realism, compatibility, and diversity scores. Specifically, our approach leads to an up to 94\% reduction in compatibility score. Our results demonstrate that LLMs can effectively handle quantitative constraints in structured design tasks, suggesting broader applications for text-based generative modeling.

2025-09-29

NeurIPS.cc/2025/Workshop/UrbanAI (présentation orale)

openreview.net

Ctrl-Crash: Controllable Diffusion for Realistic Car Crashes

Ge Ya Luo

D. Nowrouzezahrai

Alexia Jolicoeur-Martineau

Christopher Pal

Video diffusion techniques have advanced significantly in recent years; however, they struggle to generate realistic imagery of car crashes … (voir plus)due to the scarcity of accident events in most driving datasets. Improving traffic safety requires realistic and controllable accident simulations. To tackle the problem, we propose Ctrl-Crash, a controllable car crash video generation model that conditions on signals such as bounding boxes, crash types, and an initial image frame. Our approach enables counterfactual scenario generation where minor variations in input can lead to dramatically different crash outcomes. To support fine-grained control at inference time, we leverage classifier-free guidance with independently tunable scales for each conditioning signal. Ctrl-Crash achieves state-of-the-art performance across quantitative video quality metrics (e.g., FVD and JEDi) and qualitative measurements based on a human-evaluation of physical realism and video quality compared to prior diffusion-based methods.

2025-05-31

arXiv (publié)

doi.org

arxiv.org

Learning Action and Reasoning-Centric Image Editing from Videos and Simulation

Dheeraj Vattikonda

Varun Jampani

Christopher Pal