arXiv:2603.00198v2 Announce Type: replace-cross Abstract: Token reduction accelerates long-video vision--language models (VLMs), but existing methods target Transformers, where reduction is treated as token pruning. We study token reduction in hybrid Mamba--Transformer VLMs and find that it is \emph{stateful}: Mamba layers maintain a recurrent state that accumulates information from earlier tokens, allowing discarded tokens to persist, so reduction behaves more like compression than dropping.We support this view with a representation-based probing method measuring how much information from dis
This research is emerging as the field of large language models rapidly expands into multimodal capabilities, particularly video, requiring more efficient processing techniques.
Improved token reduction for long-video VLMs could significantly enhance the scalability and efficiency of advanced AI applications, making complex video analysis more feasible.
The understanding of token reduction in hybrid Mamba-Transformer architectures shifts from simple pruning to a more nuanced stateful compression, potentially enabling new optimization strategies.
- · AI researchers
- · Video analytics companies
- · Cloud computing providers
- · Inefficient video processing models
More efficient and capable long-video understanding models will become available.
New applications requiring real-time, in-depth video analysis across various sectors, from security to entertainment, will accelerate.
The development of highly autonomous AI agents that can deeply understand and interact with their visual environment over extended periods could be significantly advanced.
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Read at arXiv cs.AI