AI RESEARCH
Unified Spatiotemporal Token Compression for Video-LLMs at Ultra-Low Retention
arXiv CS.CV
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ArXi:2603.21957v1 Announce Type: new Video large language models (Video-LLMs) face high computational costs due to large volumes of visual tokens. Existing token compression methods typically adopt a two-stage spatiotemporal compression strategy, relying on stage-specific metrics and an implicit assumption of spatiotemporal separability. Under extremely low retention ratios, however, such approaches often result in unbalanced allocation and loss of visual evidence essential for question answering.