The field of multimodal reasoning and vision-language models is rapidly advancing, with a focus on developing more robust and generalizable models. Recent research has emphasized the importance of incorporating visual verification and grounding into the reasoning process, as well as improving the ability of models to reason over multiple images and complex visual contexts. The use of multi-agent systems, iterative self-evaluation, and chain-of-thought prompting has shown promise in enhancing the common sense reasoning capabilities of large language models and vision-language models. Noteworthy papers in this area include Analyze-Prompt-Reason, which proposes a collaborative agent-based framework for multi-image vision-language reasoning, and CoRGI, which introduces a modular framework for verified chain-of-thought reasoning with visual grounding. Additionally, Uni-cot presents a unified chain-of-thought framework for coherent and grounded multimodal reasoning, while ViFP proposes a general framework for enhancing visual reasoning reliability by detecting false positives. These innovative approaches are pushing the boundaries of multimodal reasoning and vision-language models, enabling more accurate and reliable performance in a wide range of applications.