Long-context inference in large language models (LLMs) is bottlenecked by the memory and compute of the key-value (KV) cache. Structured pruning is a direct way to shrink it: dropping the least useful channels of the W_k, W_v projection weights to reduce the output KV dimensions. However, modern LLMs apply Rotary Position Embedding (RoPE) after the QK projections, which rotates feature dimensions in pairs. Therefore, removing individual channels breaks these pairs, corrupting RoPE's positional semantics and rendering the pruned model unusable. We propose RoPE-Aligned Pruning (RAP), which constrains the pruning granularity to RoPE-aligned pairs rather than individual channels: removing whole pairs to keep the rotation intact. Our evaluation across Llama, Mistral, and Qwen models from 3B to 14B shows that RAP preserves accuracy at 30% KV compression (retain ratio \r{ho} = 0.7), far outperforms RoPE-blind channel pruning, stays near the strongest low-rank method at lower attention cost, and composes with orthogonal methods such as quantization.
Faculty of Science and Technology, University of Macau, Macau, China（澳门大学科学与技术学院）；School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, China（电子科技大学信息与软件工程学院）
Towards Efficient Large Language Model Serving: A Survey on System-Aware KV Cache Optimization
School of Computing and Information Systems, The University of Melbourne（墨尔本大学计算与信息系统学院）；School of Computer Science and Technology, Huazhong University of Science and Technology（华中科技大学计算机科学与技术学院）
Towards Economical Inference: Enabling DeepSeek's Multi-Head Latent Attention in Any Transformer-based LLMs
School of Computer Science, Fudan University（复旦大学计算机科学学院）；School of Computer Science and Technology, East China Normal University（华东师范大学计算机科学与技术学院）；Institute of Modern Languages and Linguistics, Fudan University（复旦大学现代语言与语言学研究所）；Institute of Trustworthy Embodied Artificial Intelligence, Fudan University（复旦大学可信具身人工智能研究所）；Shanghai Collaborative Innovation Center of Intelligent Visual Computing（上海智能视觉计算协同创新中心）；Pengcheng Laboratory（鹏城实验室）；Hikvision Inc（海康威视公司）
TokenSelect: Efficient Long-Context Inference and Length Extrapolation for LLMs via Dynamic Token-Level KV Cache Selection
University of Science and Technology of China（中国科学技术大学）；Tsinghua University（清华大学）；Alibaba Cloud Computing（阿里云计算）；Xiaohongshu Inc.（小红书公司）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
HybridKV: Hybrid KV Cache Compression for Efficient Multimodal Large Language Model Inference
The State Key Laboratory of Blockchain and Data Security, Zhejiang University（浙江大学区块链与数据安全国家重点实验室）；Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security（杭州高新区（滨江）区块链与数据安全研究院）；Hangzhou Dianzi University（杭州电子科技大学）
VLA-Cache: Efficient Vision-Language-Action Manipulation via Adaptive Token Caching
School of Computer Science, University of Sydney（悉尼大学计算机科学学院）；John Hopcropt Center for Computer Science, Shanghai Jiao Tong University（上海交通大学约翰·霍普克罗夫特计算机科学中心）
