This article presents a codebase for a study on how unified multimodal models (UMMs) enhance reasoning by integrating visual generation. The research introduces a new evaluation suite, VisWorld-Eval, which assesses multimodal reasoning capabilities across various tasks. Experiments show that interleaved visual-verbal reasoning outperforms purely verbal methods in specific contexts.

SpatialScore introduces a comprehensive benchmark for evaluating multimodal large language models (MLLMs) in spatial understanding, consisting of the VGBench dataset and an extensive collection of 28K samples. It features the SpatialAgent, a multi-agent system designed for enhanced spatial reasoning, and reveals persistent challenges and improvements in spatial tasks through quantitative and qualitative evaluations.