Who Speaks for the Pruned? Visual Token Pruning as Coverage Optimization

Title:Who Speaks for the Pruned? Visual Token Pruning as Coverage Optimization

Abstract:Visual token pruning reduces the inference cost of vision-language models (VLMs), but most methods only ask which tokens to keep. This retained-token view can keep redundant high-scoring tokens while leaving discarded evidence without a close representative. We propose CoverPruner, a training-free pruner that asks the complementary demand-side question: after a token is removed, which surviving original token represents it for the target VLM? CoverPruner formulates pruning as Representational Coverage Maximization (RCM), covering the full projected visual-token set with query-weighted demand. It instantiates RCM with projector-space coverage and a lightweight first-layer attention probe. Across multiple VLM architectures and compression rates, CoverPruner achieves the best average accuracy among all compared methods, with the largest gains usually appearing under aggressive compression.

Computer Vision and Pattern Recognition (cs.CV); Computation and Language (cs.CL); Machine Learning (cs.LG)

arXiv:2609.03158 [cs.CV]

(or arXiv:2609.03158v1 [cs.CV] for this version)

https://doi.org/10.48550/arXiv.2609.03158 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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