HIMMEL: Hierarchical Interleaved Multi-stream Motion Encoding for Long Video Understanding

arXiv:2605.08158v3 Announce Type: replace-cross Abstract: Long video understanding with multimodal language models suffers from three compounding bottlenecks: heavy decode cost to obtain dense RGB frames, token growth with frame count, and weak motion perception under sparse keyframe sampling. Existing remedies either prune visual tokens after decoding, which leaves the expensive RGB pipeline untouched, or they discretise codec motion vectors with a generative tokeniser, which couples motion modelling to a heavy pre-training stage. In this paper we present HIMMEL, a hierarchical video-language

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