Transcrib3D can be extended to handle more complex spatial-semantic relationships by incorporating advanced reasoning mechanisms and expanding the set of guiding principles. One approach could involve integrating graph neural networks to capture intricate spatial dependencies among objects in the scene. By modeling the scene as a graph where objects are nodes and relationships are edges, Transcrib3D can leverage graph-based reasoning to infer complex spatial arrangements. Additionally, introducing hierarchical reasoning layers can enable the model to reason at different levels of abstraction, allowing for more nuanced understanding of spatial relationships. Furthermore, incorporating external knowledge bases or ontologies can provide additional context for resolving complex spatial-semantic relationships beyond the scope of the current benchmarks. By enhancing the reasoning capabilities and expanding the knowledge base, Transcrib3D can effectively handle a wider range of spatial-semantic relationships in diverse real-world scenarios.