The field of multimodal reasoning and generative models is witnessing significant advancements, driven by innovative approaches to guidance, verification, and optimization. Researchers are exploring new ways to improve the quality and efficiency of generative models, such as flow-based models, and developing more effective methods for evaluating and optimizing multimodal reasoning processes. Notably, the development of novel reward models and verification techniques is enabling more accurate and robust evaluation of complex reasoning tasks. Furthermore, advancements in reinforcement learning and post-training pipelines are enhancing the capabilities of large language models in code generation and other applications. Overall, these developments are pushing the boundaries of what is possible in multimodal reasoning and generative modeling, with potential applications in a wide range of fields. Noteworthy papers include: RAAG, which proposes a ratio-aware adaptive guidance schedule for flow-based generative models, enabling up to 3x faster sampling while maintaining generation quality. CompassVerifier, which introduces a unified and robust verifier model for evaluation and outcome reward, demonstrating multi-domain competency and effectiveness in identifying abnormal responses. GM-PRM, which presents a generative multimodal process reward model that provides fine-grained analysis and corrective capabilities, achieving state-of-the-art results on multimodal math benchmarks.