An AI system for professional floor plan design must precisely control room dimensions and areas while respecting the desired connectivity between rooms and maintaining functional and aesthetic quality.Existing generative approaches focus primarily on respecting the requested connectivity between rooms, but do not support generating floor plans that respect 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 improve adherence to topological and numerical constraints while discouraging invalid or overlapping outputs.Furthermore, we design a set of constraint adherence metrics to systematically measure how generated floor plans align with user-defined constraints.Our model generates floor plans that satisfy user-defined connectivity and numerical constraints and outperforms existing methods on Realism, Compatibility, and Diversity metrics. Across all tasks, our approach achieves at least a 94% relative reduction in Compatibility compared with existing methods.Our results demonstrate that LLMs can effectively handle constraints in this setting, suggesting broader applications for text-based generative modeling.

We describe our system for the AmericasNLP 2026 Shared Task on Cultural Image Captioning for Indigenous Languages of the Americas. Our post-training pipeline starts from Aya Vision 32B: the vision-language model is first fine-tuned on machine translation data from prior AmericasNLP shared tasks and then further fine-tuned on the cultural Image Captioning data. This approach uses translation as an intermediate training task, while the final system produces captions directly in the requested Indigenous language rather than translating a Spanish caption afterward. Our experiments show that machine translation fine-tuning is an important initialization step. The resulting fine-tuned vision-language model also shows translation capabilities for the languages considered in this work. In addition, our zero-shot GPT-5.5 submission ranks first in the Maya language track under the official human-evaluation stage.