arXiv AI June 10, 2026 By Kohga Tanaka, Hiroaki Nishi

Sigma-Branch: Hierarchical Single-Path Network Reconstruction for Dynamic Inference with Reduced Active Parameters

arXiv:2606. 09924v1 Announce Type: cross Abstract: Deploying deep neural networks on memory-constrained edge accelerators is bottlenecked by per-inference off-chip weight transfer rather than computation: the dense network cannot be retained on-chip, and every parameter must be loaded for every input.

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

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

Aug 7

APQF: Agentic Profiling-Guided Structured Pruning and Mixed-Precision Quantization with Adaptive Fine-Tuning

arXiv:2608. 05499v1 Announce Type: cross Abstract: Modern deep neural networks achieve strong performance, but their scale makes them costly and slow, especially on resource-constrained edge devices.

By Sadegh Jafari, Mohiuddin Bilwal, Fan Zhou, Brian Gelder, Ali Jannesari

llms agents fine-tuning efficiency

arXiv Machine Learning

Jun 30

HiAP: A Multi-Granular Stochastic Auto-Pruning Framework for Vision Transformers

arXiv:2603. 12222v2 Announce Type: replace-cross Abstract: Vision Transformers require significant computational resources and memory bandwidth, severely limiting their deployment on resource-constraint hardware.

By Andy Li, Aiden Durrant, Milan Markovic, Georgios Leontidis

llms fine-tuning efficiency

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 Machine Learning

Jul 9

Protocol Models: Scaling Decentralized Training with Communication-Efficient Model Parallelism

arXiv:2506. 01260v2 Announce Type: replace Abstract: Scaling models has led to significant advancements in deep learning, but training these models in decentralized settings remains challenging due to communication bottlenecks.

By Sameera Ramasinghe, Thalaiyasingam Ajanthan, Gil Avraham, Yan Zuo, Alexander Long

llms

arXiv Machine Learning

Jul 10

Subspace Networks: Scaling Decentralized Training with Communication-Efficient Model Parallelism

arXiv:2506. 01260v3 Announce Type: replace Abstract: Scaling models has led to significant advancements in deep learning, but training these models in decentralized settings remains challenging due to communication bottlenecks.

By Sameera Ramasinghe, Thalaiyasingam Ajanthan, Gil Avraham, Yan Zuo, Alexander Long

llms

Related stories

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

APQF: Agentic Profiling-Guided Structured Pruning and Mixed-Precision Quantization with Adaptive Fine-Tuning

HiAP: A Multi-Granular Stochastic Auto-Pruning Framework for Vision Transformers

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

Protocol Models: Scaling Decentralized Training with Communication-Efficient Model Parallelism

Subspace Networks: Scaling Decentralized Training with Communication-Efficient Model Parallelism