The recent advancements in the realm of large language models (LLMs) and multimodal language models (MLLMs) have collectively propelled the field towards more sophisticated, adaptive, and efficient reasoning mechanisms. A common thread across these developments is the emphasis on enhancing reasoning capabilities, scalability, and real-world applicability. In the domain of LLMs, novel paradigms such as continuous latent reasoning and temperature-guided reasoning are being explored to improve model performance and interpretability. Multi-objective optimization frameworks are also being developed to enhance the diversity and quality of reasoning paths, addressing the limitations of current methods. The integration of multi-agent systems for lateral thinking and dynamic self-correction strategies is emerging as a powerful approach for handling complex, uncertain scenarios. Notable innovations include 'SpecFuse,' which leverages collaborative potential among LLMs to generate higher-quality responses, and 'Dynamic Ensemble Reasoning for LLM Experts,' which optimizes performance with minimal resources. In the realm of MLLMs, significant strides have been made in instruction tuning with high-quality datasets, fostering chain-of-thought reasoning and achieving state-of-the-art performance on diverse benchmarks. The introduction of scalable methods to construct large-scale multimodal instruction-tuning datasets with rich rationales has significantly improved reasoning capabilities. Additionally, the role of instruction templates in model evaluation and training is being thoroughly explored, revealing high sensitivities to template variations. Open-source datasets and benchmarks are also being emphasized to facilitate transparency and reproducibility, fostering innovation within the community. Notable advancements include the development of fully open-source models and novel methodologies for multimodal multi-hop question answering and preference optimization. These innovations collectively push the boundaries of current model performance, addressing critical challenges such as hallucinations and misalignment between modalities. Furthermore, there is a growing interest in evaluating how well these models perceive and interpret visual information, with benchmarks being developed to assess alignment with human visual systems. Overall, the field is moving towards more robust, explainable, and human-aligned multimodal AI systems, with a strong focus on open science and practical real-world applications.