Initial release of V-Skip project page

README.md

@@ -1,16 +1,32 @@
-# Nerfies
-
-This is the repository that contains source code for the [Nerfies website](https://nerfies.github.io).
-
-If you find Nerfies useful for your work please cite:
-```
-@article{park2021nerfies
-  author    = {Park, Keunhong and Sinha, Utkarsh and Barron, Jonathan T. and Bouaziz, Sofien and Goldman, Dan B and Seitz, Steven M. and Martin-Brualla, Ricardo},
-  title     = {Nerfies: Deformable Neural Radiance Fields},
-  journal   = {ICCV},
-  year      = {2021},
-}
-```
-
-# Website License
-<a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/"><img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by-sa/4.0/88x31.png" /></a><br />This work is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/">Creative Commons Attribution-ShareAlike 4.0 International License</a>.
+# V-skip
+
+<div align="center">
+
+[![arXiv](https://img.shields.io/badge/arXiv-2601.13879-b31b1b.svg)](https://arxiv.org/pdf/2601.13879)
+[![Project Page](https://img.shields.io/badge/Project-Page-green)](https://dongxu-zhang.github.io/v-skip.github.io/)
+[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
+
+**V-Skip: Efficient Multimodal Reasoning via Dual-Path Anchoring**
+<br>
+[Dongxu Zhang](https://dongxu-zhang.github.io/)<sup>1,*</sup>, [Yiding Sun](https://github.com/Issac-Sun)<sup>1,*</sup>, [Cheng Tan](https://chengtan9907.github.io/)<sup>3</sup>, [Wenbiao Yan](#)<sup>4</sup>, [Ning Yang](http://ningyangcasia.cn/)<sup>2,†</sup>, [Jihua Zhu](https://gr.xjtu.edu.cn/web/zhujh)<sup>1,†</sup>, [Haijun Zhang](https://scce.ustb.edu.cn/shiziduiwu/jiaoshixinxi/2018-04-13/100.html)<sup>5</sup>
+
+<sup>1</sup>State Key Laboratory of Human-Machine Hybrid Augmented Intelligence, XJTU, <sup>2</sup>CASIA, <sup>3</sup>Shanghai AI Laboratory, <sup>4</sup>HITSZ, <sup>5</sup>USTB
+</div>
+
+---
+
+## 🚀 Introduction
+
+This repository contains the official implementation (and project page source) for the paper **"Chain-of-Thought Compression Should Not Be Blind: V-Skip for Efficient Multimodal Reasoning via Dual-Path Anchoring"**.
+
+**V-Skip** is a novel token pruning framework designed for Multimodal Large Language Models (MLLMs). It solves the **"Visual Amnesia"** problem found in standard text-centric compression methods. By employing a dual-path gating mechanism (Linguistic Surprisal + Visual Attention Flow), V-Skip preserves visually salient tokens while reducing latency.
+
+![V-Skip Teaser](./static/images/fig1.png)
+*Figure 1: Comparison of compression paradigms. V-Skip successfully rescues visual anchors (e.g., "red") that are blindly pruned by text-only methods.*
+
+## 📈 Key Results
+- **Speedup:** Achieves **2.9x** inference speedup on Qwen2-VL.
+- **Accuracy:** Outperforms baselines by over **30%** on the DocVQA benchmark.
+- **Robustness:** Effectively prevents object hallucination caused by over-pruning.
+
+## 🛠️ Usage