This paper proposes an improved structured pruning method for large language models (LLMs) that addresses key challenges in adapting Adaptive Feature Retention (AFR), an unstructured pruning technique, to structured pruning. When applying AFR to structured pruning, three major problems arise: distribution mismatch between heterogeneous pruning scores, loss of sign information indicating optimization direction consistency, and influence of outliers. To address these issues, we propose a unified approach combining power transformation for nonlinear distribution alignment, sign-preserving score aggregation, and percentile-based outlier removal. Experiments on Llama-3-8B, Vicuna-v1.5-13B, and LLaVA-v1.5-13B demonstrate that our method maintains accuracy comparable to unstructured pruning while achieving practical inference speedup through structured pruning.
Nüwa: Mending the Spatial Integrity Torn by VLM Token Pruning
Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ)（人工智能与数字经济广东实验室）；School of Artificial Intelligence, Xidian University（西安电子科技大学人工智能学院）；The Hong Kong Polytechnic University（香港理工大学）；Great Bay University（大鹏大学）；Shenzhen University（深圳大学）；Huawei（华为）
On the Limitations of Language Targeted Pruning: Investigating the Calibration Language Impact in Multilingual LLM Pruning
Department of Computer Science, TU Dortmund University（图恩-多特蒙德大学计算机科学系）；Bonn-Aachen International Center for Information Technology, University of Bonn（波恩-亚琛国际信息科技中心，波恩大学）；Computer Science School, University of Sheffield（谢菲尔德大学计算机科学学院）；Lamarr Institute for Machine Learning and Artificial Intelligence（拉马尔人工智能与机器学习研究所）
Garbage Attention in Large Language Models: BOS Sink Heads and Sink-aware Pruning
Graduate School of Data Science, Seoul National University（数据科学研究生院，首尔国立大学）；Department of Rural Systems Engineering, Seoul National University（农村系统工程系，首尔国立大学）；Department of Aerospace Engineering, Seoul National University（航空航天工程系，首尔国立大学）
Unifying Depth and Width Pruning for LLMs via Binary Knapsack Optimization
Yardi School of Artificial Intelligence, Indian Institute of Technology Delhi（德里印度理工学院亚迪人工智能学院）；Indian Institute of Technology Delhi（德里印度理工学院）；Microsoft Research (MSR) India（微软研究院印度分部）
VisCache: Visual KV Cache Pruning for Efficient Vision Large Language Model Inference
Shenzhen International Center for Industrial and Applied Mathematics（深圳国际工业与应用数学中心）；Shenzhen Research Institute of Big Data（深圳大数据研究院）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Shenzhen Loop Area Institute（深圳河套学院）
DOTResize: Reducing LLM Width via Discrete Optimal Transport-based Neuron Merging
Center for Language and Speech Processing, Johns Hopkins University, Baltimore, MD, USA（语言与语音处理中心，约翰霍普金斯大学，巴尔的摩，MD，美国）；Human Language Technology Center of Excellence, Johns Hopkins University, Baltimore, MD, USA（人工智能语言技术卓越中心，约翰霍普金斯大学，巴尔的摩，MD，美国）
The Structural Scalpel: Automated Contiguous Layer Pruning for Large Language Models
Institute of Cyberspace Security, College of Information Engineering, Zhejiang University of Technology（浙江工业大学信息工程学院网络安全研究所）；Binjiang Institute of Artificial Intelligence, Zhejiang University of Technology（浙江工业大学滨江人工智能研究院）；City College of New York, City University of New York（纽约市立大学城市学院）；Australian AI Institute, Faculty of Engineering and Information Technology, University of Technology（澳大利亚人工智能研究院，技术大学工程与信息学院）
VISA: Group-wise Visual Token Selection and Aggregation via Graph Summarization for Efficient MLLMs Inference
Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China, Xiamen University（多媒体可信感知与高效计算重点实验室，教育部，厦门大学）；Tencent Youtu Lab（腾讯优图实验室）
HeadRouter: Dynamic Head-Weight Routing for Task-Adaptive Audio Token Pruning in Large Audio Language Models
EPIC Lab, Shanghai Jiao Tong University（上海交通大学EPIC实验室）；DAIL Tech（DAIL科技）；Northeastern University（东北大学）；Sichuan University（四川大学）；Huazhong University of Science and Technology（华中科技大学）
Growing a Multi-head Twig via Distillation and Reinforcement Learning to Accelerate Large Vision-Language Models
Zhejiang Key Laboratory of Space Information Sensing and Transmission, School of Computer Science, Hangzhou Dianzi University（浙江省空间信息感知与传输重点实验室，计算机学院，杭州电子科技大学）；Li Auto Inc.（理想汽车）；School of Intelligence Science and Engineering, Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳）智能科学与工程学院）
SPP: Sparsity-Preserved Parameter-Efficient Fine-Tuning for Large Language Models
Multimedia Laboratory (MMLab), The Chinese University of Hong Kong（香港中文大学多媒体实验室）；Salesforce AI Research（Salesforce 人工智能研究院）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；CPII under InnoHK（InnoHK 创新香港研发平台旗下产品可靠性及系统安全研发中心）
