V-CoLA compresses vision tokens using linear-attention intrinsic signals, retaining 88% performance at 12.5% tokens with up to 6.15x prefill speedup
The authors propose V-CoLA, a training-free vision token compression framework for hybrid vision-language models with linear attention, deriving a uniqueness-aware importance criterion from the retention preferences of the bounded recurrent state, combined with adaptive chunk-wise merging and early exiting of deep-layer vision tokens; on Qwen3.5-9B and related models it reaches 99.5% of original performance at 50% vision tokens and over 88.0% at 12.5%, with 1.86 to 6.15 prefill speedup.