Large Language Models (LLMs) struggle with long-context code due to window limitations. Existing textual code compression methods mitigate this via selective filtering but often disrupt dependency closure, causing semantic fragmentation. To address this, we introduce LongCodeOCR, a visual compression framework that renders code into compressed two-dimensional image sequences for Vision-Language Models (VLMs). By preserving a global view, this approach avoids the dependency breakage inherent in filtering. We systematically evaluate LongCodeOCR against the state-of-the-art LongCodeZip across four benchmarks spanning code summarization, code question answering, and code completion.
Our results demonstrate that visual code compression serves as a viable alternative for tasks requiring global understanding. At comparable compression ratios ($\sim$1.7$\times$), LongCodeOCR improves CompScore on Long Module Summarization by 36.85 points over LongCodeZip. At a 1M-token context length with Glyph (a specialized 9B VLM), LongCodeOCR maintains higher accuracy than LongCodeZip while operating at about 4$\times$ higher compression. Moreover, compared with LongCodeZip, LongCodeOCR drastically reduces compression-stage overhead (reducing latency from $\sim$4.3 hours to $\sim$1 minute at 1M tokens). Finally, our results characterize a fundamental coverage--fidelity trade-off: visual code compression retains broader context coverage to support global dependencies, yet faces fidelity bottlenecks on exactness-critical tasks; by contrast, textual code compression preserves symbol-level precision while sacrificing structural coverage.
Extreme Model Compression for Edge Vision-Language Models: Sparse Temporal Token Fusion and Adaptive Neural Compression
University of Information Technology and Sciences（信息科技与科学大学）；The University of Burdwan（布尔德万大学）；Tarakeswar Degree College（塔拉克瓦尔学位学院）；University of Missouri, Columbia（密苏里大学，哥伦比亚）
LLMC+: Benchmarking Vision-Language Model Compression with a Plug-and-play Toolkit
Nanyang Technological University（南洋理工大学）；SenseTime Research（商汤科技研究院）；Beihang University（北京航空航天大学）；Hong Kong University of Science and Technology（香港科技大学）
On the Adversarial Robustness of Large Vision-Language Models under Visual Token Compression
The Hong Kong Polytechnic University, Hong Kong（香港理工大学）；Nanyang Technological University, Singapore（南洋理工大学）；Chongqing University, Chongqing, China（重庆大学）；Research Centre for Privacy and Security Technologies in Future Smart Systems, PolyU（未来智能系统中的隐私与安全技术研究中心）
PVC: Progressive Visual Token Compression for Unified Image and Video Processing in Large Vision-Language Models
Tsinghua University（清华大学）；OpenGVLab（开放通用实验室）；Shanghai AI Laboratory（上海人工智能实验室）；SenseTime Research（商汤科技研究院）；Nanjing University（南京大学）；The Chinese University of Hong Kong（香港中文大学）
Feature-Aware Token Attack for Compression-Triggered Stealthy Failures in Large Vision-Language Models
Beijing University of Posts and Telecommunications（北京邮电大学）；Jiangnan University（江南大学）；Nanyang Technological University（南洋理工大学）；Chongqing University of Posts and Telecommunications（重庆邮电大学）
Just Noticeable Difference Modeling for Token Compression in Vision-Language-Action Models
The Hong Kong Polytechnic University（香港理工大学）；Nanyang Technological University（南洋理工大学）；Bytedance Inc.（字节跳动公司）；Shandong University（山东大学）；CNRS（法国国家科学研究中心）；CentraleSupelec（中央理工学院）；Université Paris-Saclay（巴黎萨克雷大学）
VTCBench: Can Vision-Language Models Understand Long Context with Vision-Text Compression?
1 Institute of Automation, Chinese Academy of Sciences；2 School of Artificial Intelligence, University of Chinese Academy of Sciences；3 Centre for Artificial Intelligence；Robotics, Hong Kong Institute of Science \& Innovation, CAS；4 Independent Researcher
UHR-BAT: Budget-Aware Token Compression Vision-Language model for Ultra-High-Resolution Remote Sensing
School of Artificial Intelligence Science and Technology, Nanjing University（南京大学人工智能科学与技术学院）；School of Electronic Science and Engineering, Nanjing University（南京大学电子科学与工程学院）；School of Physics, Nanjing University（南京大学物理学院）
Language-Guided Token Compression with Reinforcement Learning in Large Vision-Language Models
School of Computer Science and Engineering（计算机科学与工程学院）；University of Electronic Science and Technology of China（电子科学与技术大学）；School of Robotics and Advanced Manufacture（机器人与先进制造学院）；Harbin Institute of Technology（哈尔滨工业大学）；College of Information and Control Engineering（信息与控制工程学院）
Video-XL: Extra-Long Vision Language Model for Hour-Scale Video Understanding
Beijing Academy of Artificial Intelligence（北京人工智能研究院）；Renmin University of China（中国人民大学）；Chinese Academy of Sciences（中国科学院）；Beijing University of Posts and Telecommunications（北京邮电大学）；Peking University（北京大学）；Shanghai Jiaotong University（上海交通大学）
MoPE-CLIP: Structured Pruning for Efficient Vision-Language Models with Module-wise Pruning Error Metric
School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；City University of Hong Kong（香港城市大学）；Huawei Noah’s Ark Lab（华为诺亚方舟实验室）；The Hong Kong University of Science and Technology（香港科技大学）；Nanyang Technological University（南洋理工大学）
Seeing is Coding: On the Effectiveness of Vision Language Models in Code Understanding
Shanghai Jiao Tong University China；Hohai University China；Singapore Management University Singapore；Imperial College London United Kingdom；East China Normal University \& Shanghai Innovation Institute China；Chongqing University China；Shanghai Jiao Tong University；Hohai University；Singapore Management University；Imperial College London；East China Normal University \& Shanghai Innovation Institute；Chongqing University
