Quicksviewer: An LMM with Early Video Compression using Annealed Gumbel-Softmax

Large Multimodal Models (LMMs) uniformly perceive video frames, creating computational inefficiency for videos with inherently varying temporal redundancy. This paper present Quicksviewer, an LMM with new perceiving paradigm that partitions a nonuniform redundant video into varying cubes using Gumbel Softmax, followed by a unified resampling for each cube to achieve efficient video understanding. We further propose a Gumbel-noise annealing strategy that enables stable end-to-end LMM training with the discrete cubing network. This simple and intuitive approach dynamically compress video online based on the momentum of frame differences, significantly reducing spatiotemporal complexity (overall 45$\times$ compression rate), while enabling efficient training with large receptive field. We train the model from a language backbone through three progressive stages, each incorporating lengthy videos on average of 420s/1fps thanks to the perceiving efficiency. With only 0.8M total video samples, our model outperforms contemporary LMMs with the same backbone and comparable parameters amount. An experiment in A/B pretraining with same backbone and training data shows that, w/ cubing achieves lower training loss and higher benchmark accuracy (+2.1) than the fixed partitioning baseline, strictly validating the benefits. Moreover, the front-loaded compression in encoding substantially reduces latency in the computationally expensive prefill and decoding, making it suited to mainstream EPD disaggregated inference deployment.