LLM Pruning

LLM: Large Language Model

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12 papers in the last four weeks, up 200% on the four weeks before. 0.1% of all new papers.

Jul 13Week of Sep 28

Latest papers 113

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SWE-Pruner Pro: The Coder LLM Already Knows What to Prune
Jul 20, 2026Yuhang Wang, Yuling Shi, Shaoqiu Zhang +6LLM Pruning AI Coding Agents
LLM4SE Lab, Shanghai Jiao Tong University · Douyin Group
CoCurve: Cross-Module Co-Pruning Curvature for Training-Free Structured LLM Pruning
Jul 20, 2026Zhiren Gong, Zihao Zeng, Zijie Wang +3LLM Pruning LLM Compression
College of Computing and Data Science, Nanyang Technological University, Singapore · Interdisciplinary Graduate Programme, Nanyang Technological University, Singapore · School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore
ShortOPD: Recovering Pruned LLMs with Short-to-Long On-Policy Distillation
Jul 14, 2026Qingyu Zhang, Qianhao Yuan, Hongyu Lin +7Open-Ended Generation LLM Pruning
2Chinese Information Processing Laboratory, Institute of Software, Chinese Academy of Sciences · University of Chinese Academy of Sciences · 1ByteDance
Sensitivity-Aware Thresholding and Token Routing for Activation Sparsification in Large Language Models
Jul 9, 2026Bishmoy Paul, Youngmin Yi, Hoeseok YangLLM Pruning Activation Sparsity
Santa Clara University · Sogang University
Super Weights in LLMs and the Failure of Selective Training
Jul 9, 2026Shreyas Subramanian, Adewale Akinfaderin, Akarsha SehwagLLM Pruning Fine-Tuning
Amazon Web Services
It Takes a MAESTRO To Prune Bad Experts
Jul 9, 2026Palaash Goel, Ayush Maheshwari, Tanmoy ChakrabortyMixture-of-Experts Pruning LLM Pruning
Indian Institute of Technology Delhi, India · NVIDIA, India
Structured Pruning of Large Language Models via Power Transformation and Sign-Preserving Score Aggregation with Adaptive Feature Retention
Jul 9, 2026Ryota Kobayashi, Tsubasa Hirakawa, Takayoshi Yamashita +4LLM Pruning LLM Compression
1Chubu University · 2Panasonic Holdings Corporation
PALS: Percentile-Aware Layerwise Sparsity for LLM Pruning
Jul 8, 2026Yazdan Jamshidi, Alexey ShvetsLLM Pruning LLM Compression
Palo Alto Networks
Nemotron-Labs-3-Puzzle-75B-A9B: Compressing Hybrid MoE LLMs
Jul 5, 2026Akhiad Bercovich, Talor Abramovich, Daniel Afrimi +67Mixture-of-Experts Pruning LLM Pruning
Generic Expert Coverage for Pruning SparseMixture-of-Experts Language Models
Jul 2, 2026Yongqin Zeng, Sicheng Pan, Jiale Wang +4Mixture-of-Experts Pruning LLM Pruning
FlexMoE: One-for-All Nested Intra-Expert Pruning for MoE Language Models
Jun 26, 2026Fan Mo, Yuxuan Han, Geng Zhang +2Mixture-of-Experts Pruning LLM Pruning
National University of Singapore
Cascaded Multi-Granularity Pruning for On-Device LLM Inference in Industrial IoT
Jun 25, 2026Jinghan Wang, Yanjun Chen, Wei Zhang +3LLM Pruning On-Device Language Model Inference
School of Mechanical and Electrical Engineering, Harbin Institute of Technology, Harbin 150001, China · School of Information Science and Technology, Eastern Institute of Technology, Ningbo, China
EPTS: Elastic Post-Training Sparsity for Efficient Large Language Model Compression
Jun 24, 2026Ke Xu, Jiaqi Wan, Wenhao Hu +2LLM Pruning LLM Compression
Scaling Laws for Task-Specific LLM Distillation
Jun 23, 2026Lavinia Ghita, Dhruv Desai, Ioana BoierLLM Pruning LLM Compression
NVIDIA
Don't Go Breaking My LLM: The Impact of Pruning Attention Layers on Explanation Faithfulness and Confidence Calibration
Jun 23, 2026Pietro Tropeano, Maria Maistro, Tuukka Ruotsalo +1LLM Pruning LLM Interpretability
University of Copenhagen Copenhagen, Denmark · LUT University Lahti, Finland
SVD-Surgeon: Optimal Singular-Value Surgery for Large Language Model Compression
Jun 22, 2026Mahmoud Safari, Frank HutterLLM Pruning LLM Compression
University of Freiburg · Prior Labs · ELLIS Institute Tübingen
The Benchmark Illusion: Pruned LLMs Can Pass Multiple Choice but Fail to Answer
Jun 16, 2026Rui Wen, Lu Sun, Jiayang Liu +3Open-Ended Generation LLM Evaluation
Institute of Science Tokyo · Tohoku University · Nanyang Technological University +2
Attribution-Guided and Coverage-Maximized Pruning for Structural MoE Compression
Jun 16, 2026Yifu Ding, Jiacheng Wang, Ge Yang +4Mixture-of-Experts Pruning LLM Pruning
State Key Laboratory of Complex & Critical Software Environment, Beihang University · School of Computer Science and Engineering, Beihang University · Nanyang Technological University +1
How to Score Experts for One-Shot MoE Expert Pruning: A Unified Formulation and Selection Principle
Jun 14, 2026Zongfang Liu, Jinghui Zhang, Zijian Ma +2Mixture-of-Experts Pruning LLM Pruning
1Zhejiang University · 2Westlake University · 3Mohamed bin Zayed University of Artificial Intelligence +1
Accelerating GPU Inference of Large Language Models with Moderately Unstructured Sparse Weight Matrices
Jun 13, 2026Tao Lu, Haoyu Wang, Zonghui Wang +3LLM Pruning GPU Kernel Optimization
National University of Singapore · Zhejiang University
Beyond Layer Importance in Layer-wise Sparsity: An Inter-Layer Perturbation-Absorption Perspective
Jun 13, 2026Tao Jing, Ningxin Wu, Chen Kang +3LLM Pruning LLM Compression
Persona-Pruner: Sculpting Lightweight Models for Role-Playing
Jun 12, 2026Jinsu Kim, Jihoon Tack, Noah Lee +1LLM Pruning Large Language Model-Based Role-Play Simulation
Department of Artificial Intelligence, Korea University, Seoul, South Korea · Korea Advanced Institute of Science and Technology (KAIST), Daejeon, South Korea
Small LLMs: Pruning vs. Training from Scratch
Jun 12, 2026Yufeng Xu, Taiming Lu, Kunjun Li +3Language Model Pretraining LLM Pruning
Princeton University · New York University · Carnegie Mellon University
SpenseGPT: Practical One-shot Pruning Enabling Sparse and Dense GEMMs for LLM Inference
Jun 9, 2026Jaeseong Lee, Seung-won Hwang, Samyam RajbhandariLLM Pruning High-Performance Computing
Snowflake AI Research* · Seoul National University†
BUDDY: BUdget-Driven DYnamic Depth Routing for Adaptive Large Language Model Inference
Jun 8, 2026Yuhua Zhou, Shaoqi Yu, Shichao Weng +4LLM Pruning Cost-Aware Inference
College of Computer Science and Technology, Zhejiang University · Zhejiang Lab · Ant Group +1
Beyond FLOPs: Benchmarking Real Inference Acceleration of LLM Pruning under a GEMM-Centric Taxonomy
Jun 8, 2026Haozhe Hu, Hao Wu, Anhao Zhao +4LLM Pruning LLM Inference Acceleration
Ningbo Institute of Digital Twin, Eastern Institute of Technology, Ningbo · Department of Computing, The Hong Kong Polytechnic University · Munich Center for Machine Learning, LMU Munich
Joint Structural Pruning and Mixed-Precision Quantization for LLM Compression
Jun 5, 2026Hoang-Loc La, Truong-Thanh Le, Amir Taherkordi +1Mixed-Precision Quantization LLM Quantization
UiT The Arctic University of Norway · University of Oslo, Norway
Less is MoE: Trimming Experts in Domain-Specialist Language Models
Jun 4, 2026Haoze He, Xinkai Zou, Xuan Jiang +4Mixture-of-Experts Pruning LLM Pruning
Carnegie Mellon University · UCSD · MIT
SHAPE: Coalition-Aware Expert Pruning for Sparse Mixture-of-Experts LLMs
Jun 3, 2026Yuhao ZhangMixture-of-Experts Pruning LLM Pruning
Beihang University
TENP: Trapezoidal Expert Neuron Pruning For Mixture-of-Experts
Jun 3, 2026Jiangyang He, Shaolin Zhu, Deyi XiongMixture-of-Experts Pruning LLM Pruning
TJUNLP Lab, School of Computer Science and Technology, Tianjin University, China

Jul 20, 2026, cs.CLJ/K move · Enter open · S save

SWE-Pruner Pro: The Coder LLM Already Knows What to Prune

Yuhang Wang, Yuling Shi, Shaoqiu Zhang, Jialiang Liang+5

LLM4SE Lab, Shanghai Jiao Tong University · Douyin Group

LLM Pruning AI Coding Agents LLM Context Management

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Pruning long context for coding agents has been a vital technology for efficient context management. While existing context pruning methods such as SWE-Pruner realize this by attaching a separate code classifier, we find the agent itself encodes internal representations indicating the relevance of code context when reading tool output. Based on this finding, we propose SWE-Pruner Pro, which prunes tool outputs directly inside the agent. Concretely, a small head turns the agent's own internal representations into a keep-or-prune label for each line, with a length-aware embedding keyed to each tool output's line count. Across two open-weight backbones and four multi-turn benchmarks, SWE-Pruner Pro saves up to 39% of prompt and completion tokens while preserving task quality, with bounded inference overhead. Notably, on MiMo-V2-Flash SWE-Pruner Pro additionally raises the SWE-Bench Verified resolve rate by +3.8% and the long-context Oolong accuracy by +2.2 points.

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Latest papers 113