The field of spatial reasoning in AI models is moving towards more efficient and effective methods for understanding and representing three-dimensional space. Recent developments have focused on improving the ability of models to reason about spatial relationships, navigate complex environments, and understand geospatial information. Notably, researchers are exploring the use of multimodal models that combine visual and language features to improve spatial understanding. Additionally, there is a growing emphasis on evaluating and benchmarking the spatial intelligence of AI models, with a focus on developing more comprehensive and challenging benchmarks. Overall, the field is advancing towards more sophisticated and human-like spatial reasoning capabilities.

Noteworthy papers include: SmolRGPT, which presents a compact vision-language architecture that achieves competitive results on warehouse spatial reasoning benchmarks with only 600M parameters. Conversational Orientation Reasoning, which introduces a new benchmark and framework for egocentric-to-allocentric navigation with multimodal chain-of-thought, achieving 100% orientation accuracy on clean transcripts and 98.1% with ASR transcripts.