A forthcoming research paper, "AdaCodec: A Predictive Visual Code for Video MLLMs," published on arXiv in June 20261, introduces a novel method to enhance the efficiency of video multimodal large language models (MLLMs). Current video MLLMs typically process video by independently encoding each sampled frame as a standard RGB image. This conventional approach frequently generates redundant visual tokens because successive video frames inherently share substantial commonalities, such as persistent objects, backgrounds, and overall scene layouts. AdaCodec counters this inefficiency by proposing a predictive visual coding scheme. Instead of redundantly re-encoding static elements across frames, this system aims to transmit a complete reference frame only when a significant alteration in the visual scene occurs. This intelligent design promises a more direct and resource-optimized video interface for MLLMs, minimizing the repetition of visual information. Such architectural improvements in processing video streams for AI have broad implications for computational efficiency, model scalability, and the strategic evolution of AI applications in sectors demanding high-fidelity, real-time visual interpretation.