The field of multimodal research is moving towards improving the reasoning capabilities of large language models (LLMs) and vision-language models (VLMs) in various domains, including visual question answering, 3D scene understanding, and psychological analysis. Researchers are exploring new frameworks and methods to enhance the interpretability and transparency of these models, such as introducing intermediate representations, using attention mechanisms, and developing novel evaluation benchmarks. Notably, the development of datasets like PartNeXt and SCENECOT-185K is facilitating progress in fine-grained and hierarchical 3D part understanding and grounded chain-of-thought reasoning. Furthermore, studies on the limitations of active reasoning in MLLMs and the importance of gradients for language-assisted image clustering are providing valuable insights into the underlying mechanisms of these models. Overall, the field is witnessing significant advancements in multimodal reasoning and interpretability, with potential applications in areas like robotics, computer vision, and human-computer interaction. Noteworthy papers include: COGS, which introduces a data-efficient framework for equipping MLLMs with advanced reasoning abilities; SceneCOT, which presents a novel framework for eliciting grounded chain-of-thought reasoning in 3D scenes; and Speculative Verdict, which proposes a training-free framework for information-intensive visual reasoning via speculation.