The field of large language models is moving towards more sophisticated reasoning and decision-making capabilities. Researchers are exploring new frameworks and techniques to enable models to think more critically and make more informed decisions. This includes the development of metacognitive capabilities, which allow models to reflect on their own thought processes and adjust their behavior accordingly. Another area of focus is the integration of multimodal inputs, such as visual and textual information, to enhance reasoning and decision-making. Additionally, there is a growing interest in designing models that can adapt to different reasoning strategies and tasks, and that can provide more transparent and interpretable results. Overall, these advancements have the potential to significantly improve the performance and reliability of large language models in a wide range of applications. Noteworthy papers include: Mini-Omni-Reasoner, which enables reasoning within speech via a novel Thinking-in-Speaking formulation, and Meta-R1, which introduces a systematic and generic framework for endowing large reasoning models with explicit metacognitive capabilities. REFINE is also notable for its teacher-student framework that systematically structures errors and provides targeted feedback to enhance multimodal reasoning.