arXiv:2602.15772v2 Announce Type: replace-cross
Abstract: Current research in multimodal models faces a key challenge where enhancing generative capabilities often comes at the expense of understanding, and vice versa. We analyzed this trade-off and identify the primary cause might be the potential conflict between generation and understanding, which creates a competitive dynamic within the model. To address this, we propose the Reason-Reflect-Refine (R3) framework. This innovative algorithm re-frames the single-step generation task into a multi-step process of “generate-understand-regenerate”. By explicitly leveraging the model’s understanding capability during generation, we successfully mitigate the optimization dilemma, achieved stronger generation results and improved understanding ability which are related to the generation process. This offers valuable insights for designing next-generation unified multimodal models. Code is available at https://github.com/sen-ye/R3.
TR-EduVSum: A Turkish-Focused Dataset and Consensus Framework for Educational Video Summarization
arXiv:2604.07553v1 Announce Type: cross Abstract: This study presents a framework for generating the gold-standard summary fully automatically and reproducibly based on multiple human summaries of