SUNTA: Hierarchical Video Prediction with Surprise-based Chunking
Hierarchical state-space models (HSSMs) offer a promising approach to long-horizon prediction by segmenting sequences into temporal chunks. However, their performance hinges on how chunk boundaries are determined. While prior HSSMs typically rely on fixed-length chunking or similarity-based boundary detection, these methods often misalign with the intrinsic temporal structure of the data. We argue that chunking should instead be driven by prediction errors, which more directly indicate when longer-range context becomes necessary. Nevertheless, integrating surprise-based chunking into HSSMs introduces critical challenges, including hierarchical collapse during end-to-end training and the a...
| Authors | Tomoshi Iiyama, Masahiro Suzuki, Yutaka Matsuo |
|---|---|
| Primary category | cs.AI |
| Published | 2 July 2026 |
| Last updated at source | 10 July 2026 |
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