arXiv AI July 14, 2026 By Jiayin Hu, Kai Yuan, Vanessa Hu, Xuetao Yin, Jianhua Li, Sean Suchter

Adaptive Model Compression (AMC): Saliency-Driven Resource Allocation for Ultra-Low-Power Transformer Inference

arXiv:2607. 10109v1 Announce Type: cross Abstract: Deploying large-scale transformer models on resource-constrained edge devices remains a challenge due to the high energy and memory overhead inherent in static inference, which processes simple and complex tokens with uniform intensity.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

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 Machine Learning

2d ago

KV Cache Compression Through the Lens of Transform Coding

arXiv:2608. 14191v1 Announce Type: new Abstract: The key-value (KV) cache stores information from past tokens and is a major memory bottleneck in long-context inference.

By Hannah Laus, Claudio Mayrink Verdun, Hao Wang, Flavio du Pin Calmon, Felix Krahmer

llms 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

Jul 1

RaBitQCache: Rotated Binary Quantization for KVCache in Long Context LLM Inference

arXiv:2606. 31519v1 Announce Type: new Abstract: Long-context Large Language Model inference is severely bottlenecked by the massive Key-Value (KV) cache, yet existing sparse attention methods often suffer from static fixed-budget (Top-k) retrieval or rely on proxy scores that are computationally expensive and biased.

By Wenhao Li, Jinhao Dong, Hailin Zhang, Wenhang Shi, Wei Lu, Xiaoyong Du

llms 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

arXiv AI

Jun 6

Minimizing the Hidden Cost of Scales: Graph-Guided Ultra-Low-Bit Quantization for Large Language Models

arXiv:2606. 05429v1 Announce Type: new Abstract: Post-training quantization (PTQ) is critical for the efficient deployment of large language models (LLMs).

By Rayyan Abdalla, Amir Hussein, Min Wu, Dinesh Manocha

llms efficiency benchmarks

Related stories

PSViT: A Methodology for Structurally Pruning Spiking Vision Transformers

KV Cache Compression Through the Lens of Transform Coding

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

RaBitQCache: Rotated Binary Quantization for KVCache in Long Context LLM Inference

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

Minimizing the Hidden Cost of Scales: Graph-Guided Ultra-Low-Bit Quantization for Large Language Models