AI

V-CoLA: Vision Token Compression with Linear Attention

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Based on a feed excerpt; full article content has not been acquired. Source evidence retrieved 09/10/2026, 02:18:25 UTC.

Briefings

V-CoLA is a training-free token compression framework for Vision-Language Models (VLMs) that addresses performance degradation in hybrid architectures using linear attention. The study identifies that existing methods fail to generalize to this regime, necessitating a uniqueness-aware importance criterion and adaptive merging strategy. Experiments show V-CoLA achieves 99.5% of original performance with 50% of vision tokens and over 88% with 12.5% tokens, delivering 1.86x to 6.15x prefill speedups.

Key points

• V-CoLA introduces a uniqueness-aware importance criterion and adaptive token merging strategy specifically for linear attention architectures.
• The framework achieves 99.5% of original performance with only 50.0% of vision tokens, and over 88.0% with as few as 12.5% tokens.

Context

Vision-language models (VLMs) have demonstrated impressive capabilities but suffer from substantial computational overhead due to vision tokens dominating the input sequence. Hybrid architectures incorporating linear attention, such as Qwen3.5, have emerged, but prior methods designed for softmax attention struggle to generalize. This study addresses the urgent need for compression methods tailored to the linear attention regime.

Limitations

The evidence is limited to a single research article published in October 2026. The study reports performance metrics and speedups but does not include user feedback, real-world deployment data, or long-term stability results. The findings are based on controlled experiments and may not reflect all practical constraints or edge cases.

Generated by Bittrees-hosted qwen35-2b sha256:aaf42c8b7c3c on 14/11/2026, 07:33:05 UTC. Based on publisher-provided feed evidence; may not cover the full original article. Check the source for details.

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