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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TAP: Efficient Long-Horizon Agent Pruning via Trajectory-Anchored Recovery
Oct 6, 2026Yuanzhe Li, Pengxin Wang, Yuxin Ren +5LLM Pruning LLM Inference Acceleration
University of Arizona · University of Pittsburgh · University of Central Florida
A Self-Pruning Transformer: Extreme KV-Cache Compression with Universal Attention
Oct 6, 2026Davis Wertheimer, Haochen Shen, Ahan Gupta +6KV-Cache Compression Transformer Attention
IBM Research, USA · SSAIL Lab, University of Illinois Urbana-Champaign
Differentiable Bit-Widths: Co-optimizing Pruning and Quantization via SVD for Ultra-Efficient LLM Compression
Oct 5, 2026Hankyul Kang, Jongbin RyuLLM Quantization LLM Pruning
Ajou University
QK-Wanda: Coupling Queries and Keys for Unstructured Pruning
Oct 1, 2026Ivan Ilin, Peter RichtárikLLM Pruning Neural Network Pruning
KAUST
MWOP: Modality-aware Width-wise Operation Pruning for Efficient MLLMs
Oct 1, 2026Xudong Wang, Hao Wu, Haozhe Hu +5LLM Pruning Efficient VLM Inference
EIT-NLP Lab, Eastern Institute of Technology, Ningbo · Shanghai Jiao Tong University · The Hong Kong Polytechnic University +1
Pruning for Efficiency, Paying in Fairness: Demographic Disparities in Pruned Speech-LLMs
Sep 29, 2026Ganesh Pavan Kartikeya Bharadwaj Kolluri, Michael Kampouridis, Ravi ShekharLLM Pruning Algorithmic Fairness
School of Computer Science and Electronic Engineering, University of Essex, UK
Output-aware Residual Stream Pruning for Large Language Models
Sep 28, 2026Chayne Thrash, Kevin Chen, Soheil KolouriLLM Pruning LLM Compression
Department of Computer Science Vanderbilt University Nashville, TN 37235, USA
GroupMask: Layer-Adaptive Group-wise Sparsity for Semi-Structured LLM Pruning
Sep 27, 2026Zhengao Li, Shuoqiu Li, Xiaofang Zhang +7LLM Pruning LLM Compression
Florida State University · University of Connecticut · College of William & Mary +1
RAZOR: Pruning Replaceable Experts in LLMs
Sep 24, 2026Mingyang Song, Mao ZhengMixture-of-Experts Pruning LLM Pruning
Foundation Model Department, Tencent, China
Speech Block Influence: Component-Specific Layer Scoring for Pruning Speech LLMs
Sep 24, 2026Siyu Yao, Du Q. Huynh, Lian Xu +1LLM Pruning Speech Language Models
The University of Western Australia
OBC-Prune: Outcome-Based Calibration for Large Reasoning Model Pruning
Sep 15, 2026Ha Lan Nguyen, Huy Hoang Tran, Trac-Duy Tran +1LLM Pruning Large Reasoning Models
VinUniversity · Johns Hopkins University
What Breaks Under Pruning in Smart Homes, and When? Evaluating LLM Degradation Across Architectures and Task Complexity
Sep 15, 2026Congjing Zhang, Vashishtha Patil, Henning Lange +1LLM Grounding LLM Pruning
Alexa Home AI, Amazon.com · University of Washington
ESTS at WMT26: Routing-Informed Expert Pruning for Model Compression
Sep 14, 2026Liu O. Martin, Lucas Bandarkar, Nanyun PengModel Compression Mixture-of-Experts Pruning
University of California, Los Angeles
LILA: Calibration-Free Structured Pruning of Large Language Models via Latent Spectral Geometry
Sep 11, 2026Sankar Behera, Dhruv Singh, Anshika Agnihotri +3LLM Pruning LLM Compression
CSE, IIT Jammu · Mathematics, IIT Jammu · IT, EC Bikaner +1
Forward-Free LLM Depth Pruning via Weight Redundancy
Sep 9, 2026Vincent-Daniel Yun, Woosang LimLLM Pruning LLM Inference Acceleration
University of Southern California, United States · 2Seoul National University, Republic of Korea
Debias-SparseGPT: Bias-Aware Pruning for Large Language Models
Sep 2, 2026Irina Proskurina, Guillaume Metzler, Antoine Gourru +1LLM Pruning Neural Network Pruning
1Laboratoire Hubert Curien, UMR CNRS 5516, Saint-Étienne, France · 2Université Claude Bernard Lyon 1, Université Lumière Lyon 2, ERIC · 3École Centrale de Lyon, LIRIS, CNRS UMR 5205
Budget-Aware Compression Pipeline for Single-GPU LLM Inference: Methods, Trade-offs, and Coupling Effects
Aug 30, 2026Hongyu Yu, Yifei ShenLLM Quantization LLM Pruning
Lenovo Research · University of Washington
Unifying Depth and Width Pruning for LLMs via Binary Knapsack Optimization
Aug 13, 2026Palaash Goel, Ayan Sengupta, Akshay Nambi +1LLM Pruning Structured Pruning
Yardi School of Artificial Intelligence, Indian Institute of Technology Delhi · Department of Electrical Engineering, Indian Institute of Technology Delhi · Microsoft Research (MSR) India
Benchmarking Trustworthiness of SLMs: Pre-trained vs. Compressed
Aug 12, 2026Haokun Lin, Kaijie Zhu, Haobo Xu +4LLM Quantization LLM Pruning
Institute of Automation, CAS Beijing, China · Tsinghua University Beijing, China · Harvard Medical School Boston, U.S. +1
The Sparsity Whisperer
Aug 6, 2026Linghao Kong, Inimai Subramanian, Micah Adler +3LLM Pruning LLM Inference Acceleration
MIT · IST Austria · IBM +1
BnBERT-iPET: Sparse Few-Shot Language Modeling for Bengali via Lottery Ticket Pruning
Aug 5, 2026Sajib Hossain, Md Kamrus Samad, Anan Ghosh +2LLM Pruning Few-Shot Learning
Department of Electrical and Computer Engineering, North South University, Dhaka, Bangladesh
TaskPress: Query-Agnostic KV Cache Compression via Task-Guided Pruning
Aug 4, 2026Wonpyo Park, Seung-won HwangLLM Pruning KV Caching
Seoul National University · Google
When Compression Scores Cannot Decide: Information Boundaries for Group-Robust LLM Pruning
Aug 3, 2026Andrew ZhangModel Selection Mixture-of-Experts Pruning
KTH Royal Institute of Technology
F-WANDA: Fisher-Reweighted Post-Training Pruning for Sustainable Deployment of Large Language Models
Aug 1, 2026Himanshu MishraLLM Pruning LLM Compression
University of British Columbia
WIDE: Boosting Adaptive LLM Inference via Token-level Dynamic Width Pruning
Jul 30, 2026Haozhe Hu, Hao Wu, Peiran Yin +3LLM Pruning Token Pruning
Ningbo Institute of Digital Twin, Eastern Institute of Technology, Ningbo · Munich Center for Machine Learning, LMU Munich
Fairness Pruning: Locating Demographic Bias in GLU-MLP Layers via Differential Activations
Jul 30, 2026Pere Martra, Eugenio Martínez Cámara, Alfonso Ureña LópezLLM Pruning LLM Interpretability
Universidad Internacional Men´endez Pelayo Barcelona, Spain · Departamento de Inform´atica Universidad de Ja´en Ja´en, Spain
Unified Static-Dynamic Pruning for Efficient LLM Inference
Jul 24, 2026Jinhyeok Kim, Yejoon Lee, Jaeyoung DoEfficient Neural Network Inference LLM Pruning
Seoul National University Seoul, South Korea
Learning What Matters: Supervising Global Context Pruning with Causal Evidence Sets
Jul 23, 2026James E. AllchinLLM Pruning Long-Context Language Model Inference
Independent Researcher
Adaptive Depth Sparse Framework: Similarity-Driven Resource Allocation for Pre-Trained LLMs
Jul 23, 2026Yidu Wu, Xiang Wang, Kejie Zhao +3LLM Pruning LLM Inference Acceleration
Department of Electronic and Electrical Engineering, Southern University of Science and Technology, Shenzhen, China · ACS Lab, Huawei Technologies Co., Ltd., Shenzhen, China · Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China
CausalGate: Causal Importance Distillation for Transformer Module Pruning
Jul 21, 2026Kiran Nair, Smriti Regmi, Rodrigue RizkEfficient Transformer Inference LLM Pruning
USD Artificial Intelligence Research Lab, Department of Computer Science University of South Dakota, Vermillion, SD 57069, USA
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

Oct 6, 2026, cs.LGJ/K move · Enter open · S save

TAP: Efficient Long-Horizon Agent Pruning via Trajectory-Anchored Recovery

Yuanzhe Li, Pengxin Wang, Yuxin Ren, Jianing Deng+4

University of Arizona · University of Pittsburgh · University of Central Florida

LLM Pruning LLM Inference Acceleration Long-Horizon Agent Tasks On-Policy Distillation

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Emerging long-horizon agentic tasks require repeated model calls, worsening the inference cost of already-costly language models. While narrow agentic tasks suggest potential for aggressive model pruning without performance drop, empirical results show existing methods proposed for question answering tasks severely degrade task performance when applied to agentic models. We trace this failure to two decisions: what to prune and how to recover. For pruning, one-shot importance estimates fail to track how the pruned model adapts. For recovery, offline distillation covers only teacher prefixes, while full-trajectory on-policy distillation causes student errors to compound across turns. In this work, we propose Trajectory-Anchored Pruning (TAP), the first structural pruning framework for reinforcement learning (RL)-trained agents. TAP couples structural pruning with efficient on-policy recovery, anchoring interactions to teacher trajectories while allowing the student to generate each reasoning-action response. A frozen dense teacher supervises the student's response prefixes, addressing within-response training-inference mismatch while preventing student-induced deviations from propagating across training turns. Instead of one-shot pruning, TAP re-scores channels using gradients of the recovery objective on the recovered student, connecting iterative channel selection to the evolving policy. With 60% of FFN channels removed, TAP retains 99.2% and 88.0% of the dense 7B agents' task success rates on ALFWorld and WebShop, respectively, while reducing GPU time per successful task by approximately 22% and 17%. These results demonstrate effective structural compression of long-horizon agents under a limited recovery budget.

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Latest papers 113