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LICENSE.md

@@ -0,0 +1,50 @@
+# VBVR-Pro License
+
+Copyright © 2026 Hippocampal, Inc. All rights reserved where not otherwise licensed below.
+
+VBVR-Pro uses separate licenses for data and code. These license grants apply only to materials for which Hippocampal, Inc. or the VBVR-Pro contributors have the authority to grant the applicable rights.
+
+## Data and Benchmark Materials — CC BY-NC 4.0
+
+VBVR-Pro data and benchmark materials are licensed under the [Creative Commons Attribution-NonCommercial 4.0 International License](https://creativecommons.org/licenses/by-nc/4.0/) (CC BY-NC 4.0).
+
+The data and benchmark materials include, where applicable:
+
+- task videos;
+- first-frame images;
+- task prompts;
+- annotations;
+- taxonomy labels; and
+- benchmark metadata.
+
+You may share and adapt these materials for non-commercial purposes, provided that appropriate attribution is given. The CC BY-NC 4.0 license does not grant permission for commercial use of the data or benchmark materials.
+
+When using these materials in a publication, benchmark report, presentation, or other public work, please cite the official VBVR-Pro paper and identify the materials as licensed under CC BY-NC 4.0.
+
+Suggested attribution:
+
+> VBVR-Pro, © 2026 Hippocampal, Inc., licensed under CC BY-NC 4.0.
+
+For commercial data-licensing inquiries, contact Hippocampal, Inc. at hokin@hippocampal.xyz.
+
+## Code — Apache License 2.0
+
+VBVR-Pro source code, scripts, configuration files, task-specific scoring software, and other software authored for this project are licensed under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0) (Apache-2.0).
+
+Unlike the data license above, the Apache License 2.0 permits commercial use of the licensed code, subject to its terms and conditions.
+
+Copyright © 2026 Hippocampal, Inc.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this code except in compliance with the License. You may obtain a copy of the License at:
+
+<https://www.apache.org/licenses/LICENSE-2.0>
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.
+
+## Model Weights and Third-Party Materials
+
+Model weights and checkpoints are governed by the license stated in the applicable model card and by any relevant upstream terms. Third-party code, models, datasets, and other materials retain their original licenses and notices. Nothing in this file overrides those terms.
+
+## Disclaimer
+
+VBVR-Pro is provided on an "AS IS" basis, without warranties or conditions of any kind, subject to the disclaimers and limitations in the applicable license.

README.md

@@ -25,7 +25,7 @@ tags:
 <a href="https://github.com/Video-Reason/VBVR-Pro" target="_blank">
     <img alt="Code" src="https://img.shields.io/badge/Training_Inferenceing-VBVR_Pro-100000?style=flat-square&logo=github&logoColor=white" height="20" />
 </a>
-<a href="https://huggingface.co/papers/2602.20159" target="_blank">
+<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://huggingface.co/datasets/Video-Reason/VBVR-Pro-SFT-Video" target="_blank">
@@ -41,7 +41,7 @@ tags:
 ## 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/2602.20159).
+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%;">
@@ -268,15 +268,18 @@ python example.py \
 
 ## License
 
-This model is released under the Apache License 2.0, following the base BAGEL model. BAGEL is fine-tuned from Qwen2.5-7B-Instruct and SigLIP and uses the FLUX.1-schnell autoencoder; consult the upstream repositories for their notices.
+This model is released under the Apache License 2.0. BAGEL is fine-tuned from Qwen2.5-7B-Instruct and SigLIP and uses the FLUX.1-schnell autoencoder; consult the upstream repositories for their notices.
 
 ## Citation
 
 ```bibtex
-@article{deng2025bagel,
-  title   = {Emerging Properties in Unified Multimodal Pretraining},
-  author  = {Deng, Chaorui and Zhu, Deyao and Li, Kunchang and Gou, Chenhui and Li, Feng and Wang, Zeyu and Zhong, Shu and Yu, Weihao and Nie, Xiaonan and Song, Ziang and Shi, Guang and Fan, Haoqi},
-  journal = {arXiv preprint arXiv:2505.14683},
-  year    = {2025}
+@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}, 
 }
 ```