arXiv AI June 17, 2026 By Marco Deano, Filippo Ziche, Nicola Bombieri

S4oP: Operator-level Pruning of Structured State Space Models for Resource-Constrained Devices

arXiv:2606. 18096v1 Announce Type: cross Abstract: Structured State Space Models (SSMs), including the S4 and S4D architectures, have recently emerged as powerful alternatives to attention-based models for capturing long-range dependencies in sequential data.

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

Jul 28

Multi-Objective Structured Pruning of LLMs for Latency and Model Size Optimization

arXiv:2607. 22583v1 Announce Type: new Abstract: Large Language Models (LLMs) have achieved widespread adoption because of their strong reasoning and query-response capabilities.

By Muhammad Junaid Ali, Smail Niar, El-Ghazali Talbi

llms efficiency

arXiv Computation and Language

Aug 31

Pruning Laws for Large Language Models

arXiv:2504.04342v2 Announce Type: replace Abstract: Scaling up model parameters and training data consistently improves the performance of large language models (LLMs), but at the cost of rapidly gro...

By Ayan Sengupta, Siddhant Chaudhary, Tanmoy Chakraborty

llms efficiency

arXiv Machine Learning

Jul 14

Prune, Update and Trim: Robust Structured Pruning for Large Language Models

arXiv:2605. 18331v2 Announce Type: replace Abstract: Large Language Models (LLMs) have experienced significant growth and development in recent years.

By Diego Coello de Portugal Mecke, Tom Hanika, Lars Schmidth-Thieme

llms efficiency benchmarks

arXiv Machine Learning

4d ago

On State Reduction in Linear Attention

arXiv:2602.04852v3 Announce Type: replace Abstract: Linear attention offers a computationally efficient yet expressive alternative to softmax attention. However, recent empirical results indicate tha...

By Philipp Nazari, T. Konstantin Rusch

efficiency

arXiv Machine Learning

Aug 11

High-Layer Attention Pruning with Rescaling

arXiv:2507. 01900v3 Announce Type: replace-cross Abstract: Pruning is a highly effective approach for compressing large language models (LLMs), significantly reducing inference latency.

By Songtao Liu, Peng Liu

llms efficiency

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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