The field of multi-subject generation and flow matching is rapidly advancing, with a focus on improving the fidelity and diversity of generated images and videos. Recent research has introduced new frameworks and techniques, such as spatially disentangled attention and identity-aware reinforcement learning, to address the challenges of attribute leakage and subject entanglement. Additionally, there is a growing interest in flow matching methods, including diffusion bridge and flow matching, which have been shown to be effective in various applications, including image and video generation. Noteworthy papers in this area include MultiCrafter, which proposes a novel framework for high-fidelity multi-subject generation, and Diffusion Bridge or Flow Matching?, which provides a unified theoretical and experimental validation of diffusion bridge and flow matching methods. Other notable papers include Optimal Control Meets Flow Matching, which introduces a principled route to multi-subject fidelity, and DisCo, which achieves state-of-the-art multi-subject fidelity across models.