Cover Image

POP: Prefill-Only Pruning for Efficient Large Model Inference

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	He, Junhui, Fu, Zhihui, Wang, Jun, Li, Qingan
Format:	Preprint
Published:	2026
Subjects:	Computation and Language Artificial Intelligence Computer Vision and Pattern Recognition
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

AIM: Adaptive Inference of Multi-Modal LLMs via Token Merging and Pruning
by: Zhong, Yiwu, et al.
Published: (2024)

CoViPAL: Layer-wise Contextualized Visual Token Pruning for Large Vision-Language Models
by: Tang, Zicong, et al.
Published: (2025)

ChemVA: Advancing Large Language Models on Chemical Reaction Diagrams Understanding
by: Rao, Mingyang, et al.
Published: (2026)

TOPA: Extending Large Language Models for Video Understanding via Text-Only Pre-Alignment
by: Li, Wei, et al.
Published: (2024)

Index-Preserving Lightweight Token Pruning for Efficient Document Understanding in Vision-Language Models
by: Son, Jaemin, et al.
Published: (2025)

Measuring Social Bias in Vision-Language Models with Face-Only Counterfactuals from Real Photos
by: Chen, Haodong, et al.
Published: (2026)

Efficient Pruning of Text-to-Image Models: Insights from Pruning Stable Diffusion
by: Ramesh, Samarth N, et al.
Published: (2024)

Decoder-Only LLMs are Better Controllers for Diffusion Models
by: Dong, Ziyi, et al.
Published: (2025)

Prefill-Time Intervention for Mitigating Hallucination in Large Vision-Language Models
by: Zhang, Chengsheng, et al.
Published: (2026)

TransPrune: Token Transition Pruning for Efficient Large Vision-Language Model
by: Li, Ao, et al.
Published: (2025)

Uni3D-LLM: Unifying Point Cloud Perception, Generation and Editing with Large Language Models
by: Liu, Dingning, et al.
Published: (2024)

SpecVLM: Enhancing Speculative Decoding of Video LLMs via Verifier-Guided Token Pruning
by: Ji, Yicheng, et al.
Published: (2025)

Evolutionary Negative Module Pruning for Better LoRA Merging
by: Cao, Anda, et al.
Published: (2026)

Bridging the Missing-Modality Gap: Improving Text-Only Calibration of Vision Language Models
by: Kim, Mingyeong, et al.
Published: (2026)

Speed Always Wins: A Survey on Efficient Architectures for Large Language Models
by: Sun, Weigao, et al.
Published: (2025)

Why Only Text: Empowering Vision-and-Language Navigation with Multi-modal Prompts
by: Hong, Haodong, et al.
Published: (2024)

AVG-LLaVA: An Efficient Large Multimodal Model with Adaptive Visual Granularity
by: Lan, Zhibin, et al.
Published: (2024)

ML-Mamba: Efficient Multi-Modal Large Language Model Utilizing Mamba-2
by: Huang, Wenjun, et al.
Published: (2024)

Better Supervised Fine-tuning for VQA: Integer-Only Loss
by: Qian, Baihong, et al.
Published: (2025)

A Survey on Benchmarks of Multimodal Large Language Models
by: Li, Jian, et al.
Published: (2024)

BLINK: Multimodal Large Language Models Can See but Not Perceive
by: Fu, Xingyu, et al.
Published: (2024)

Vision Token Reduction via Attention-Driven Self-Compression for Efficient Multimodal Large Language Models
by: Deniz, Omer Faruk, et al.
Published: (2026)

Resource-Efficient Medical Report Generation using Large Language Models
by: Abdullah, et al.
Published: (2024)

Efficient Vision-Language Models by Summarizing Visual Tokens into Compact Registers
by: Wen, Yuxin, et al.
Published: (2024)

PixelPrune: Pixel-Level Adaptive Visual Token Reduction via Predictive Coding
by: Wang, Nan, et al.
Published: (2026)

MMEvalPro: Calibrating Multimodal Benchmarks Towards Trustworthy and Efficient Evaluation
by: Huang, Jinsheng, et al.
Published: (2024)

LLMs can Compress LLMs: Adaptive Pruning by Agents
by: Kodathala, Sai Varun, et al.
Published: (2026)

ResAdapt: Adaptive Resolution for Efficient Multimodal Reasoning
by: Liao, Huanxuan, et al.
Published: (2026)

Commonsense-T2I Challenge: Can Text-to-Image Generation Models Understand Commonsense?
by: Fu, Xingyu, et al.
Published: (2024)

Can Large Vision-Language Models Detect Images Copyright Infringement from GenAI?
by: Xu, Qipan, et al.
Published: (2025)

HSSBench: Benchmarking Humanities and Social Sciences Ability for Multimodal Large Language Models
by: Kang, Zhaolu, et al.
Published: (2025)

Rotation-Aligned Key Channel Pruning for Efficient Vision-Language Model Inference
by: Kang, Beomseok, et al.
Published: (2026)

FoPru: Focal Pruning for Efficient Large Vision-Language Models
by: Jiang, Lei, et al.
Published: (2024)

To Sink or Not to Sink: Visual Information Pathways in Large Vision-Language Models
by: Luo, Jiayun, et al.
Published: (2025)

Jailbreaking Multimodal Large Language Models using Multi-Clip Video
by: Kang, Choongwon, et al.
Published: (2026)

Nüwa: Mending the Spatial Integrity Torn by VLM Token Pruning
by: Huang, Yihong, et al.
Published: (2026)

ECoFLaP: Efficient Coarse-to-Fine Layer-Wise Pruning for Vision-Language Models
by: Sung, Yi-Lin, et al.
Published: (2023)

MME-CoT: Benchmarking Chain-of-Thought in Large Multimodal Models for Reasoning Quality, Robustness, and Efficiency
by: Jiang, Dongzhi, et al.
Published: (2025)

Large Language Models can Share Images, Too!
by: Lee, Young-Jun, et al.
Published: (2023)

An Image is Worth 1/2 Tokens After Layer 2: Plug-and-Play Inference Acceleration for Large Vision-Language Models
by: Chen, Liang, et al.
Published: (2024)