← Back to all news
arXiv Computer Vision By Kai Liu, Anqi Li, Junxian Li, Zhixin Wang, Zhikai Chen, Renjing Pei, Yulun Zhang

TwinPrune: Density-Aware Two-Phase Token Pruning for Vision-Language Models

Read the original on arXiv Computer Vision →

TwinPrune introduces a unified spatial‑density signal to score token importance for both inference phases of vision‑language models: a bipartite merge during prefill and KV eviction during decode. By separating benchmarks into prefill‑only and full‑pipeline probes, the authors show that decode‑phase pruning must be evaluated on full‑pipeline tasks, as prefill probes can be misleading. Experiments on seven model variants demonstrate that density‑based prefill compression preserves accuracy while scaling linearly with visual token count, and that density‑top‑k KV eviction outperforms strong baselines on TextVQA and MM‑Vet across multiple retention ratios, achieving a Pareto frontier in compute, memory, and accuracy.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

Related stories

arXiv AI
Oct 2

MWOP: Modality-aware Width-wise Operation Pruning for Efficient MLLMs

MWOP (Modality-aware Width-wise Operation Pruning) is a method that independently prunes visual‑to‑visual, text‑to‑visual, and text‑to‑text attention paths within each layer of multimodal large language models, and separately selects feed‑forward network channels for visual and textual inputs. It uses a first‑order Taylor criterion to guide pruning, re‑evaluates FFN importance after attention pruning, and applies LoRA‑based recovery training. The approach is paired with path‑sparse Triton attention kernels and compact visual‑side FFN execution to achieve practical acceleration, preserving token sequences while reducing computation. "whyItMatters":"MWOP achieves a 1.6× prefill speedup on LLaVA‑OneVision‑7B while retaining 99.7% performance, and further boosts token‑compression methods to 2.9× and 2.7× speedups, demonstrating its effectiveness across architectures."

By Xudong Wang, Hao Wu, Haozhe Hu, Peiran Yin, Xinghao Chen, Yunpu Ma, Wei Zhang, Xiaoyu Shen
llmsnlpfine-tuningefficiencymultimodalbenchmarks
More like this →
arXiv AI
Sep 10

STAR-Pro: Stage-Wise Token Adaptive Reduction with Progressive Refinement for Efficient Large Vision-Language Models

arXiv:2609.05916v1 Announce Type: cross Abstract: Large vision-language models (LVLMs) achieve strong multimodal understanding, but the hundreds to thousands of visual tokens they process impose subs...

By Yichen Guo, Tinghao Wang, Qizhe Zhang, Lingbei Meng, Yuan Zhang, Jiajun Cao, Hao Jiang, Chenwei Wu, Jixian Wu, Sixiang Chen, Tao Luo, Hongyang Cheng, Kai Tang, Chenxi Li, Renyuan Li, Xiande Huang, Wenya Wang, Shanghang Zhang
llmsefficiencymultimodalbenchmarks
More like this →