Update README.md

README.md

@@ -33,6 +33,9 @@ pipeline_tag: image-to-video
     <img alt="Leaderboard" src="https://img.shields.io/badge/%F0%9F%A4%97%20_VBVR_Bench-Leaderboard-ffc107?color=ffc107&logoColor=white" height="20" />
 </a>
 
+🔥Please check out our newly released [**VBVR-Wan2.1**](https://huggingface.co/Video-Reason/VBVR-Wan2.1) (Diffusers format),
+[**VBVR-Wan2.1-diffsynth**](https://huggingface.co/Video-Reason/VBVR-Wan2.1-diffsynth) (DiffSynth LoRA format), and
+[**VBVR-LTX2.3-diffsynth**](https://huggingface.co/Video-Reason/VBVR-LTX2.3-diffsynth) (DiffSynth LoRA format; Diffusers does not yet support LTX-Video-2.3, so only the DiffSynth LoRA format is released for this model).
 
 ## Overview
 Video reasoning grounds intelligence in spatiotemporally consistent visual environments that go beyond what text can naturally capture, 
@@ -49,6 +52,25 @@ to unseen reasoning tasks. **Together, VBVR lays a foundation for the next stage
 
 The model was presented in the paper [A Very Big Video Reasoning Suite](https://huggingface.co/papers/2602.20159).
 
+## Models Zoo
+
+| Model | Base Architecture | Other Remarks |
+|-------|-------------------|---------------|
+| [VBVR-Wan2.1](https://huggingface.co/Video-Reason/VBVR-Wan2.1) | Wan2.1-I2V-14B-720P | Diffusers format |
+| [**VBVR-Wan2.2**](https://huggingface.co/Video-Reason/VBVR-Wan2.2) | Wan2.2-I2V-A14B | Diffusers format |
+| [VBVR-Wan2.1-diffsynth](https://huggingface.co/Video-Reason/VBVR-Wan2.1-diffsynth) | Wan2.1-I2V-14B-720P | DiffSynth LoRA format |
+| [VBVR-Wan2.2-diffsynth](https://huggingface.co/Video-Reason/VBVR-Wan2.2-diffsynth) | Wan2.2-I2V-A14B | DiffSynth LoRA format |
+| [VBVR-LTX2.3-diffsynth](https://huggingface.co/Video-Reason/VBVR-LTX2.3-diffsynth) | LTX-Video-2.3 | DiffSynth LoRA format |
+
+## Release Information
+VBVR-Wan2.2 is trained from Wan2.2-I2V-A14B without architectural modifications, as the goal of VBVR-Wan2.2 is to *investigate data scaling behavior* and provide a *strong baseline model* for the video reasoning research community. Leveraging the VBVR-Dataset, which constitutes one of the largest video reasoning datasets to date, VBVR-Wan2.2 achieved highest score on VBVR-Bench.
+
+In this release, we present 
+[**VBVR-Wan2.2**](https://huggingface.co/Video-Reason/VBVR-Wan2.2), 
+[**VBVR-Dataset**](https://huggingface.co/datasets/Video-Reason/VBVR-Dataset),
+[**VBVR-Bench-Data**](https://huggingface.co/datasets/Video-Reason/VBVR-Bench-Data) and 
+[**VBVR-Bench-Leaderboard**](https://huggingface.co/spaces/Video-Reason/VBVR-Bench-Leaderboard).
+
 <table>
     <tr>
       <th>Model</th>
@@ -104,6 +126,11 @@ The model was presented in the paper [A Very Big Video Reasoning Suite](https://
     <tr style="background:#F2F0EF;font-weight:700;text-align:center;">
       <td colspan="14"><em>Proprietary Models</em></td>
     </tr>
+    <tr>
+      <td><u>Seedance 2.0</u></td>
+      <td><u>0.544</u></td><td><strong>0.570</strong></td><td>0.593</td><td><u>0.498</u></td><td><strong>0.618</strong></td><td><u>0.514</u></td><td><strong>0.602</strong></td>
+      <td><u>0.517</u></td><td><strong>0.643</strong></td><td>0.398</td><td><u>0.492</u></td><td>0.427</td><td><strong>0.556</strong></td>
+    </tr>
     <tr>
       <td>Runway Gen-4 Turbo</td>
       <td>0.403</td><td>0.392</td><td>0.396</td><td>0.409</td><td>0.429</td><td>0.341</td><td>0.363</td>
@@ -111,50 +138,50 @@ The model was presented in the paper [A Very Big Video Reasoning Suite](https://
     </tr>
     <tr>
       <td><strong>Sora 2</strong></td>
-      <td><strong>0.546</strong></td><td><strong>0.569</strong></td><td><u>0.602</u></td>
-      <td><u>0.477</u></td><td><strong>0.581</strong></td><td><strong>0.572</strong></td>
-      <td><strong>0.597</strong></td><td><strong>0.523</strong></td>
+      <td><strong>0.546</strong></td><td><u>0.569</u></td><td><u>0.602</u></td>
+      <td>0.477</td><td><u>0.581</u></td><td><strong>0.572</strong></td>
+      <td><u>0.597</u></td><td><strong>0.523</strong></td>
       <td><u>0.546</u></td><td><strong>0.472</strong></td><td><strong>0.525</strong></td>
-      <td><strong>0.462</strong></td><td><strong>0.546</strong></td>
+      <td><strong>0.462</strong></td><td><u>0.546</u></td>
     </tr>
     <tr>
       <td>Kling 2.6</td>
-      <td>0.369</td><td>0.408</td><td>0.465</td><td>0.323</td><td>0.375</td><td>0.347</td><td><u>0.519</u></td>
+      <td>0.369</td><td>0.408</td><td>0.465</td><td>0.323</td><td>0.375</td><td>0.347</td><td>0.519</td>
       <td>0.330</td><td>0.528</td><td>0.135</td><td>0.272</td><td>0.356</td><td>0.359</td>
     </tr>
     <tr>
-      <td><u>Veo 3.1</u></td>
-      <td><u>0.480</u></td><td><u>0.531</u></td><td><strong>0.611</strong></td>
-      <td><strong>0.503</strong></td><td><u>0.520</u></td><td><u>0.444</u></td>
-      <td>0.510</td><td><u>0.429</u></td>
-      <td><strong>0.577</strong></td><td>0.277</td><td><u>0.420</u></td>
-      <td><u>0.441</u></td><td><u>0.404</u></td>
+      <td>Veo 3.1</td>
+      <td>0.480</td><td>0.531</td><td><strong>0.611</strong></td>
+      <td><strong>0.503</strong></td><td>0.520</td><td>0.444</td>
+      <td>0.510</td><td>0.429</td>
+      <td><u>0.577</u></td><td>0.277</td><td>0.420</td>
+      <td><u>0.441</u></td><td>0.404</td>
     </tr>
     <tr style="background:#F2F0EF;font-weight:700;text-align:center;">
       <td colspan="14"><em>Data Scaling Strong Baseline</em></td>
     </tr>
+    <tr>
+      <td><strong>VBVR-LTX2.3</strong></td>
+      <td>0.516</td><td>0.580</td><td>0.608</td><td>0.631</td><td>0.529</td><td>0.454</td><td>0.680</td>
+      <td>0.453</td><td>0.608</td><td>0.577</td><td><u>0.409</u></td><td>0.414</td><td><u>0.388</u></td>
+    </tr>
+    <tr>
+      <td><strong>VBVR-Wan2.1</strong></td>
+      <td><u>0.592</u></td><td><u>0.724</u></td><td><u>0.705</u></td><td><u>0.710</u></td><td><u>0.727</u></td><td><u>0.719</u></td><td><u>0.784</u></td>
+      <td><u>0.461</u></td><td><u>0.674</u></td><td><strong>0.592</strong></td><td>0.387</td><td><u>0.461</u></td><td>0.387</td>
+    </tr>
     <tr>
       <td><strong>VBVR-Wan2.2</strong></td>
       <td><strong>0.685</strong></td><td><strong>0.760</strong></td><td><strong>0.724</strong></td>
       <td><strong>0.750</strong></td><td><strong>0.782</strong></td><td><strong>0.745</strong></td>
       <td><strong>0.833</strong></td><td><strong>0.610</strong></td>
-      <td><strong>0.768</strong></td><td><strong>0.572</strong></td><td><strong>0.547</strong></td>
+      <td><strong>0.768</strong></td><td><u>0.572</u></td><td><strong>0.547</strong></td>
       <td><strong>0.618</strong></td><td><strong>0.615</strong></td>
     </tr>
   </tbody>
 </table>
 
-## Release Information
-VBVR-Wan2.2 is trained from Wan2.2-I2V-A14B without architectural modifications, as the goal of VBVR-Wan2.2 is to *investigate data scaling behavior* and provide a *strong baseline model* for the video reasoning research community. Leveraging the VBVR-Dataset, which constitutes one of the largest video reasoning datasets to date, VBVR-Wan2.2 achieved highest score on VBVR-Bench.
-
-In this release, we present 
-[**VBVR-Wan2.2**](https://huggingface.co/Video-Reason/VBVR-Wan2.2), 
-[**VBVR-Dataset**](https://huggingface.co/datasets/Video-Reason/VBVR-Dataset),
-[**VBVR-Bench-Data**](https://huggingface.co/datasets/Video-Reason/VBVR-Bench-Data) and 
-[**VBVR-Bench-Leaderboard**](https://huggingface.co/spaces/Video-Reason/VBVR-Bench-Leaderboard).
-
-
-## 🛠️ QuickStart
+## QuickStart
 
 ### Installation
 
@@ -176,7 +203,7 @@ python example.py \
   --model_path ./VBVR-Wan2.2
 ```
 
-## 🖊️ Citation
+## Citation
 
 ```bibtex
 @article{vbvr2026,
@@ -201,9 +228,3 @@ python example.py \
   url     = {https://arxiv.org/abs/2602.20159}
 }
 ```
-
-## License
-
-Apache License  
-Version 2.0, January 2004  
-http://www.apache.org/licenses/