Attention-Free and Lightweight Token Reduction for Efficient Vision-Language Models

Jul 16, 2026

Researchers have introduced a plug-and-play token reduction framework for vision-language models that eliminates the need for attention maps and pairwise similarity comparisons. By leveraging entropy-based importance estimation and transformation-induced consistency signals, the method selects a compact and diverse set of visual tokens. Experiments across multiple benchmarks show that the approach maintains competitive accuracy even under aggressive token compression, offering a favorable accuracy-efficiency trade-off.

Why it matters: This work could make vision-language models more practical for deployment on resource-constrained edge devices by reducing computational overhead without significant loss in performance.

Full story at: arXiv Computer Vision ↗