Align model card style with VBVR-Pro releases

README.md

@@ -21,7 +21,63 @@ tags:
 - vbvr-pro
 ---
 
-# VBVR-Pro-Wan2.2-TI2V-5B-RLVR
+# VBVR-Pro: A Scalable and Verifiable Suite for Native Visual Reasoning
+
+<div align="center">
+
+<p align="center">
+    <a href="https://video-reason.com/?v=pro" target="_blank">
+        <img alt="Project Page" src="https://img.shields.io/badge/Project%20-%20Homepage-4285F4" height="20" />
+    </a>
+    <a href="https://huggingface.co/papers/2608.26105" target="_blank">
+        <img alt="arXiv" src="https://img.shields.io/badge/arXiv-VBVR_Pro-red?logo=arxiv" height="20" />
+    </a>
+    <a href="https://github.com/Video-Reason/VBVR-Pro" target="_blank">
+        <img alt="Training and Inference" src="https://img.shields.io/badge/Training_&_Inference-VBVR_Pro-100000?style=flat-square&logo=github&logoColor=white" height="20" />
+    </a>
+    <a href="https://github.com/Video-Reason/VBVR-Pro-Bench" target="_blank">
+        <img alt="Evaluation Code" src="https://img.shields.io/badge/Evaluation_code-VBVR_Pro_Bench-100000?style=flat-square&logo=github&logoColor=white" height="20" />
+    </a>
+    <a href="https://huggingface.co/datasets/Video-Reason/VBVR-Pro-RL" target="_blank">
+        <img alt="RL Dataset" src="https://img.shields.io/badge/%F0%9F%A4%97%20_VBVR_Pro_RL-Dataset-ffc107?color=ffc107&logoColor=white" height="20" />
+    </a>
+    <a href="https://huggingface.co/datasets/Video-Reason/VBVR-Pro-Bench/tree/main" target="_blank">
+        <img alt="Bench Data" src="https://img.shields.io/badge/%F0%9F%A4%97%20_VBVR_Pro_Bench-Data-ffc107?color=ffc107&logoColor=white" height="20" />
+    </a>
+    <a href="https://video-reason.com/pro/bench/#leaderboard" target="_blank">
+        <img alt="Leaderboard" src="https://img.shields.io/badge/%F0%9F%A4%97%20_VBVR_Pro_Bench-Leaderboard-ffc107?color=ffc107&logoColor=white" height="20" />
+    </a>
+</p>
+
+</div>
+
+## Overview
+
+Native visual reasoning, i.e., reasoning through visual generation, has recently emerged as a promising direction for studying visual intelligence beyond language. Yet progress remains bottlenecked by the lack of scalable training tasks, reliable feedback, and controlled comparisons across generative substrates. In this work, we introduce **VBVR-Pro**, a closed-loop testbed that makes native visual reasoning through generation trainable, verifiable, optimizable, and experimentally controllable. **1) Task scaling.** VBVR-Pro turns visual reasoning into a controlled task space of *300* procedurally generated tasks. Models trained on VBVR-Pro show strong transfer beyond the proposed suite across *six* held-out visual reasoning benchmarks such as RISE-Video, MME-CoF-Pro, and BabyVision. Further analysis validates that these gains reflect visual reasoning rather than instruction-pattern fitting. **2) Verifiable rewards.** VBVR-Pro provides verifiable reward scorers for task-grounded evaluation. Through a systematic study of leading MLLMs as judges, we identify recurring failure modes of the prevalent *VLM-as-a-judge* paradigm. In contrast, the proposed scorers are grounded on verifiable task-specific rules, achieve fine-grained alignment with human judgments. Importantly, they serve as reliable reward signals for large-scale multi-task reinforcement learning and demonstrate stronger post-RL performance across visual reasoning tasks. **3) Mechanism study.** VBVR-Pro enables controlled modality studies across more than *30* image, video, and interleaved generators. Our analysis shows that video generation remains strongest for tasks requiring persistent spatiotemporal state tracking, while interleaved generation provides a compute-efficient alternative by externalizing intermediate visual states. Critically, ablations and probing confirm the presence of vision-native trajectories, that are a more crucial substrate than explicit linguistic chains of thought for visual reasoning. We release all data, models, scorers, and code to facilitate future research.
+
+The models are presented in the paper [VBVR-Pro: A Scalable and Verifiable Suite for Native Visual Reasoning](https://huggingface.co/papers/2608.26105).
+
+## Models Zoo
+
+<table border="1" cellspacing="0" cellpadding="4" style="border-collapse: collapse; width: 100%;">
+  <thead>
+    <tr>
+      <th>Model</th>
+      <th>Base Architecture</th>
+      <th>Other Remarks</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr style="border-top: 4px solid #6b7280; background-color: #e5e7eb;"><th colspan="3" align="left">Video Generation Models</th></tr>
+    <tr><td><a href="https://huggingface.co/Video-Reason/VBVR-Pro-Wan2.2-TI2V-5B">VBVR-Pro-Wan2.2-TI2V-5B</a></td><td>Wan2.2-TI2V-5B</td><td>Complete model, supervised fine-tuning</td></tr>
+    <tr><td><a href="https://huggingface.co/Video-Reason/VBVR-Pro-Wan2.2-TI2V-5B-RLVR"><strong>VBVR-Pro-Wan2.2-TI2V-5B-RLVR</strong></a></td><td>Wan2.2-TI2V-5B</td><td>Complete model, RL with verifiable rewards</td></tr>
+    <tr><td><a href="https://huggingface.co/Video-Reason/VBVR-Pro-Wan2.2-TI2V-5B-RLVLM-Qwen3.6-27B-Reward">VBVR-Pro-Wan2.2-TI2V-5B-RLVLM-Qwen3.6-27B-Reward</a></td><td>Wan2.2-TI2V-5B</td><td>Complete model, RL with Qwen3.6-27B VLM rewards</td></tr>
+  </tbody>
+</table>
+
+## Release Information
+
+### VBVR-Pro-Wan2.2-TI2V-5B-RLVR
 
 This repository contains a complete Diffusers checkpoint for the VBVR-Pro
 Wan2.2 TI2V-5B model optimized with reinforcement learning with verifiable
@@ -31,11 +87,11 @@ and is intended for research on image-conditioned video generation and visual
 reasoning.
 
 The repository includes the transformer, text encoder, tokenizer, VAE, and
-scheduler. It now also includes [`pipeline.py`](./pipeline.py), a custom
+scheduler. It also includes [`pipeline.py`](./pipeline.py), a custom
 image-to-video pipeline exposing all six inference configurations evaluated in
 the VBVR-Pro paper.
 
-## Inference samplers
+## VBVR-Pro Benchmark Results
 
 The sampler is selected per call; the model weights do not change.
 
@@ -53,7 +109,7 @@ matched settings below. The model was trained with Flow-CPS coefficient 0.7.
 Reported scores are evaluation results, not guarantees for other prompts or
 runtime configurations.
 
-## Recommended evaluation settings
+### Recommended evaluation settings
 
 - Resolution: 512 × 512
 - Frames: 81
@@ -61,7 +117,9 @@ runtime configurations.
 - Inference steps: 30
 - Guidance scale: 1.0
 
-## Usage with all six samplers
+## Quick Start
+
+### Custom sampler pipeline
 
 Use Diffusers 0.37.1 or newer. Because this loads Python code from the model
 repository, review `pipeline.py`, pass `trust_remote_code=True`, and pin a
@@ -113,7 +171,7 @@ default, the fresh Flow-CPS transition noise. Pass a separate
 Loading the complete pipeline requires substantial CPU and accelerator
 memory. CPU offloading is recommended on smaller GPUs.
 
-## Standard Diffusers compatibility
+### Standard Diffusers pipeline
 
 The bundled scheduler remains UniPC and `model_index.json` is unchanged. Users
 who only need the standard deterministic path can load the checkpoint without
@@ -153,13 +211,15 @@ does not accept the first-frame `image` argument in Diffusers 0.37.1.
   formal score provenance. Exact output bytes can vary with PyTorch,
   Diffusers, attention backend, dtype, and device.
 
-## Training summary
+## Training Details
+
+### Summary
 
 The model was optimized on VBVR-Pro image-to-video tasks using Flow-CPS
 rollouts and deterministic task-specific rule rewards. Training and evaluation
 targeted 512 × 512 videos with 81 frames.
 
-## Training resources
+### Resources
 
 - Release training code: [`pufanyi/vbvr-rl`](https://github.com/pufanyi/vbvr-rl)
 - Training dataset: [`Video-Reason/VBVR-Pro-RL`](https://huggingface.co/datasets/Video-Reason/VBVR-Pro-RL), revision `ca0aaffea93b07d269c6fe2fbfe533f1fdab9aa1`
@@ -173,9 +233,21 @@ targeted 512 × 512 videos with 81 frames.
   model and its training data.
 - Do not use generated outputs as the sole basis for high-stakes decisions.
 
-## License and attribution
+## License
 
 The model is released under Apache License 2.0. See `LICENSE`. Please also
 follow the terms and attribution guidance of the upstream Wan2.2 model.
 
-Citation information will be added with the paper release.
+## Citation
+
+```bibtex
+@misc{xu2026vbvrproscalableverifiablesuite,
+      title={VBVR-Pro: A Scalable and Verifiable Suite for Native Visual Reasoning},
+      author={Junxiang Xu and Ruisi Wang and Fanyi Pu and Maijunxian Wang and Ran Ji and Tongxi Zhou and Chenyang Gu and Jing Zuo and Hongcan Xiao and Yimeng Geng and Wanqi Yin and Wei Chen and Oscar Qian and Zhengan Yan and Ziqi Huang and Haiwen Diao and Liang Pan and Bo Li and Xiangyu Fan and Dezhi Luo and Fengyuan Yu and Zehong Zhao and Qingying Gao and Tinghui Zhu and Yilan Zhang and Jingqi Tong and Pinyuan Feng and Zhengze Jiang and Letian Wang and Ziyu Guo and Renrui Zhang and Jieneng Chen and Sonia Joseph and Constantin Venhoff and Saman Motamed and Mengyue Yang and Chandra Sripada and Alan Yuille and Philip Torr and Lvmin Zhang and Vikash Kumar and Daniel Khashabi and Nikolaus Kriegeskorte and Raphaël Millière and Vincent C. Müller and Anyi Rao and Quan Wang and Ziwei Liu and Dahua Lin and Lei Yang and Hokin Deng and Zhongang Cai},
+      year={2026},
+      eprint={2608.26105},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2608.26105},
+}
+```