arXiv AI July 7, 2026 By Zhenfeng Su, Kang Zhao, Han Bao, Tao Yuan, Zhongzhe Hu, Xianzhi Yu, Wenxuan Wang

Rethinking Depth Pruning for Vision Transformers: A Heterogeneity-Aware Perspective

arXiv:2607. 03784v1 Announce Type: cross Abstract: While prior studies have successfully compressed vision Transformers (ViTs) through various pruning techniques, most have concentrated on width pruning to achieve significant reductions in model size.

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 AI.

arXiv AI

Jun 10

NuWa: Deriving Lightweight Class-Specific Vision Transformers for Edge Devices

arXiv:2504. 03118v2 Announce Type: replace-cross Abstract: Vision Transformers (ViTs) often need to be compressed for deployment on resource-constrained edge devices like drones and smart vehicles.

By Ziteng Wei, Qiang He, Bing Li, Feifei Chen, Hai Jin, Yun Yang

llms efficiency benchmarks

arXiv AI

Jun 3

PSViT: A Methodology for Structurally Pruning Spiking Vision Transformers

arXiv:2606. 03257v1 Announce Type: cross Abstract: Spiking Vision Transformer (SViT) models are promising low-power ViT models for solving vision-based tasks with state-of-the-art performance.

By Rachmad Vidya Wicaksana Putra, Achyuta Muthuvelan, Alberto Marchisio, Muhammad Shafique

llms computer-vision fine-tuning efficiency benchmarks

arXiv AI

Jun 3

PrimeSVT: An Automated Memory-aware Pruning Framework with Prioritized Compression Policy for Spiking Vision Transformers

arXiv:2606. 03428v1 Announce Type: cross Abstract: The large sizes of Spiking Vision Transformers (SViTs) still hinder their embedded implementation, highlighting the need for model compression.

By Rachmad Vidya Wicaksana Putra, Achyuta Muthuvelan, Alberto Marchisio, Muhammad Shafique

llms fine-tuning efficiency benchmarks

arXiv Machine Learning

Sep 1

MOONSHOT : A Framework for Multi-Objective Pruning of Vision and Large Language Models

arXiv:2604.13287v2 Announce Type: replace Abstract: Weight pruning is a common technique for compressing large neural networks. We focus on the challenging post-training one-shot setting, where a pre...

By Gabriel Afriat, Xiang Meng, Shibal Ibrahim, Hussein Hazimeh, Rahul Mazumder

llms efficiency benchmarks

arXiv Machine Learning

Jun 9

Beyond FLOPs: Benchmarking Real Inference Acceleration of LLM Pruning under a GEMM-Centric Taxonomy

arXiv:2606. 09080v1 Announce Type: new Abstract: Pruning has emerged as a dominant paradigm for accelerating large language model (LLM) inference, spanning a broad spectrum of methods that remove computation across tokens, layers, heads, dimensions, and attention patterns.

By Haozhe Hu, Hao Wu, Anhao Zhao, Longwei Ding, Peiran Yin, Yunpu Ma, Xiaoyu Shen

llms nlp efficiency benchmarks

arXiv Machine Learning

Jul 31

WIDE: Boosting Adaptive LLM Inference via Token-level Dynamic Width Pruning

arXiv:2607. 28418v1 Announce Type: cross Abstract: Pruning is a promising approach for improving the efficiency of LLMs.

By Haozhe Hu, Hao Wu, Peiran Yin, Chao Han, Yunpu Ma, Xiaoyu Shen

llms nlp efficiency benchmarks

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Related stories

NuWa: Deriving Lightweight Class-Specific Vision Transformers for Edge Devices

PSViT: A Methodology for Structurally Pruning Spiking Vision Transformers

PrimeSVT: An Automated Memory-aware Pruning Framework with Prioritized Compression Policy for Spiking Vision Transformers

MOONSHOT : A Framework for Multi-Objective Pruning of Vision and Large Language Models

Beyond FLOPs: Benchmarking Real Inference Acceleration of LLM Pruning under a GEMM-Centric Taxonomy

WIDE: Boosting Adaptive LLM Inference via Token-level Dynamic Width Pruning