Large Language Models and Vision Language Models demonstrate strong general reasoning capabilities but face challenges in spatial understanding and layout consistency for fine-grained visual editing tasks. This paper presents a Structured Reasoning framework that enables text-conditioned spatial layout editing through scene-graph reasoning. The system takes an input scene graph and natural-language instruction, then reasons over the graph structure to generate an updated scene graph satisfying the text condition while maintaining spatial coherence. By leveraging structured relational representations, the approach enhances both interpretability and control over spatial relationships. Evaluations on a text-guided layout editing benchmark covering sorting, spatial alignment, and room-editing tasks show that the training paradigm achieves an average 15% improvement in IoU and 25% reduction in center-distance error compared to Chain of Thought Fine-tuning baselines.