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    <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.</abstract>
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%0 Conference Proceedings
%T Who Speaks for the Pruned? Visual Token Pruning as Coverage Optimization
%A Zhu, Qingchan
%A You, Weihang
%A Jiang, Hanqi
%A Yang, Changdi
%A Liu, Tianming
%A Yuan, Geng
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F zhu-etal-2026-speaks
%X 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.
%U https://aclanthology.org/2026.emnlp-main.349/
%P 8018-8037

Markdown (Informal)

[Who Speaks for the Pruned? Visual Token Pruning as Coverage Optimization](https://aclanthology.org/2026.emnlp-main.349/) (Zhu et al., EMNLP 2026)

• Who Speaks for the Pruned? Visual Token Pruning as Coverage Optimization (Zhu et al., EMNLP 2026)

ACL

• Qingchan Zhu, Weihang You, Hanqi Jiang, Changdi Yang, Tianming Liu, and Geng Yuan. 2026. Who Speaks for the Pruned? Visual Token Pruning as Coverage Optimization. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 8018–8037, Budapest, Hungary. Association for Computational Linguistics.