Link the arXiv paper and complete its citation

README.md

@@ -11,6 +11,7 @@ tags:
   - autoregressive
   - diffusion
   - adversarial-gradient-forcing
+  - arxiv:2610.14553
 ---
 
 <h1 align="center" style="font-family: Georgia, 'Times New Roman', serif; font-size: 2.15em; font-weight: 500; line-height: 1.2; margin: 12px 0 8px;">Adversarial <span style="color: #658bc9;">Gradient Forcing</span></h1>
@@ -18,12 +19,12 @@ tags:
 <p align="center" style="margin: 8px 0 14px;">
   <sub>Zhejiang University &nbsp;·&nbsp; Alibaba Group &nbsp;·&nbsp; Nanjing University</sub>
 </p>
-<div align="center" style="display: flex; flex-wrap: wrap; justify-content: center; align-items: center; gap: 8px; margin: 12px 0 22px;"> <a href="https://github.com/wuli-art/Adversarial-Gradient-Forcing"><img src="assets/github.svg" height="44" style="display: inline-block; vertical-align: middle; margin: 4px 3px;" alt="GitHub — coming soon"></a> <a href="https://wuli-art.github.io/Adversarial-Gradient-Forcing/"><img src="assets/project.svg" height="44" style="display: inline-block; vertical-align: middle; margin: 4px 3px;" alt="Project page — coming soon"></a> <img src="assets/arxiv.svg" height="44" style="display: inline-block; vertical-align: middle; margin: 4px 3px;" alt="arXiv — coming soon">
+<div align="center" style="display: flex; flex-wrap: wrap; justify-content: center; align-items: center; gap: 8px; margin: 12px 0 22px;"> <a href="https://github.com/wuli-art/Adversarial-Gradient-Forcing"><img src="assets/github.svg" height="44" style="display: inline-block; vertical-align: middle; margin: 4px 3px;" alt="GitHub code"></a> <a href="https://wuli-art.github.io/Adversarial-Gradient-Forcing/"><img src="assets/project.svg" height="44" style="display: inline-block; vertical-align: middle; margin: 4px 3px;" alt="Project page — coming soon"></a> <a href="https://arxiv.org/abs/2610.14553"><img src="assets/arxiv.svg" height="44" style="display: inline-block; vertical-align: middle; margin: 4px 3px;" alt="arXiv: 2610.14553"></a>
 </div>
 
 **AGF** improves long-video generation by training models to withstand **adversarial perturbations in their generated history**. Trained on five-second rollouts, it supports generation up to four minutes with the same four-step sampler.
 
-<p align="center"> <a href="https://wuli-art.github.io/Adversarial-Gradient-Forcing/#gallery"><img src="https://shark-console.oss-cn-shanghai.aliyuncs.com/shuju/project/kv_reasoning/runs/AGF/reports/project-page/20261010-9024de6/assets/showcase/agf-scenes.gif" width="960" alt="Selected AGF video excerpts: pirate ships, a robot, felt creatures, a creature by candlelight, a watercolor dance, and a yawning child."></a>
+<p align="center"> <a href="https://wuli-art.github.io/Adversarial-Gradient-Forcing/#gallery"><img src="https://raw.githubusercontent.com/wuli-art/Adversarial-Gradient-Forcing/main/project-page/assets/showcase/agf-scenes.gif" width="960" alt="Selected AGF video excerpts: pirate ships, a robot, felt creatures, a creature by candlelight, a watercolor dance, and a yawning child."></a>
   <br>
   <sub>Generated with AGF · Selected video excerpts</sub>
 </p>
@@ -62,7 +63,7 @@ hf download Wuli-art/Adversarial-Gradient-Forcing model.pt \
   --local-dir checkpoints/AGF
 ```
 
-Once the code is released, follow the [installation and inference guide](https://github.com/wuli-art/Adversarial-Gradient-Forcing/blob/main/docs/inference.md) for the Python/CUDA environment and Wan 1.3B assets. Place the downloaded checkpoint at `checkpoints/AGF/model.pt` and run from the code repository root:
+Follow the [installation and inference guide](https://github.com/wuli-art/Adversarial-Gradient-Forcing/blob/main/docs/inference.md) for the Python/CUDA environment and Wan 1.3B assets. Place the downloaded checkpoint at `checkpoints/AGF/model.pt` and run from the code repository root:
 
 ```bash
 AGF_RUN_ID=preview bash scripts/infer.sh \
@@ -90,11 +91,15 @@ AGF is intended for text-to-video generation and research on autoregressive vide
 
 ```bibtex
 @misc{xia2026adversarial,
-  title  = {Adversarial Gradient Forcing for Robust Few-Step Autoregressive Video Generation},
-  author = {Chenxi Xia and Yixuan Xu and Shan Liu and Jiapeng Wang
-            and Maohua Li and Hanlin Tang and Kan Liu and Tao Lan
-            and Weiyi Lu and Xinggao Liu},
-  year   = {2026}
+  title         = {Adversarial Gradient Forcing for Robust Few-Step Autoregressive Video Generation},
+  author        = {Chenxi Xia and Yixuan Xu and Shan Liu and Jiapeng Wang
+                   and Maohua Li and Hanlin Tang and Kan Liu and Tao Lan
+                   and Weiyi Lu and Xinggao Liu},
+  year          = {2026},
+  eprint        = {2610.14553},
+  archivePrefix = {arXiv},
+  primaryClass  = {cs.CV},
+  url           = {https://arxiv.org/abs/2610.14553}
 }
 ```