Video-Reason
/

VBVR-Pro-FLUX2-dev

@@ -24,7 +24,7 @@ datasets:
 <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">
     <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">
@@ -40,7 +40,7 @@ datasets:
 ## 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).
 ## Models Zoo
 <table border="1" cellspacing="0" cellpadding="4" style="border-collapse: collapse; width: 100%;">

 <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/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">
 ## 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%;">