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OmniVerifier

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[ICLR 2026 Oral & ICML 2026] Generative Universal Verifier as Multimodal Meta-Reasoner

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<div align="center">
<h1 style="font-size: 0.8em; margin: 0.4em 0;">Generative Universal Verifier as Multimodal <br>Meta-Reasoner</h1></div>

### Introduction

We introduce **Generative Universal Verifier**, a novel concept and plugin designed for next-generation multimodal reasoning in vision-language models and unified multimodal models, providing the fundamental capability of reflection and refinement on visual outcomes during the reasoning and generation process.

- **ViVerBench**: A comprehensive benchmark spanning 16 categories of critical tasks for evaluating visual outcomes in multimodal reasoning. 
- **OmniVerifier**: Trained on large-scale visual verification data, the first omni-capable generative verifier trained for universal visual verification and achieves notable gains on ViVerBench(+8.3). 
- **OmniVerifier-TTS**: A sequential test-time scaling paradigm that leverages the universal verifier to bridge image generation and editing within unified models, enhancing the upper bound of generative ability through iterative fine-grained optimization.
- **OmniVerifier-M1**: A generalist multimodal meta-verifier that leverages symbolic meta-verification and decoupled RL training to achieve robust visual verification, fine-grained error localization, and state-of-the-art performance on ViVerBench.

OmniVerifier advances both reliable reflection during generation and scalable test-time refinement, marking a step toward more trustworthy and controllable next-generation reasoning systems.

### New Updates
**[2026.05]** [OmniVerifier-M1](https://arxiv.org/abs/2605.28805) is accepted by ICML 2026.

**[2026.05]** We release [OmniVerifier-M1](https://arxiv.org/abs/2605.28805), advancing multimodal verifiers through symbolic meta-verification.

**[2026.02]** [OmniVerifier](https://arxiv.org/abs/2510.13804) is accepted by ICLR 2026 **(Oral Paper, Top 1%)**.

**[2025.11]** Inference code of two automated pipelines for visual verifier data construction is released.

**[2025.10]** Inference code of Sequential OmniVerifier-TTS (based on Qwen-Image) is released.

**[2025.10]** Evaluation code of ViVerBench is released.

**[2025.10]** Training code of OmniVerifier is released.

### Installation

```bash
git clone https://github.com/Cominclip/OmniVerifier.git
cd OmniVerifier
pip install -e .
```

### Quick Start: Generated Image Verification

Use the following command to test **OmniVerifier-7B** on a generated image:

```shell
python inference.py
```

Please modify `image_path` and `prompt` to your own settings.

The model will output both an **answer** and an **explanation**, indicating whether the image is strictly aligned with the given prompt.

### Part1: ViVerBench Evaluation

We provide two evaluation approaches: **rule-based** and **model-based**. As a first step, store the model outputs in a JSON file such as `your_model.json`.

For rule-based evaluation:

```shell
python viverbench_eval_rule_based.py --model_response your_model.json
```

For model-based evaluation, we use GPT-4.1 as the judge model:

```shell
python viverbench_eval_model_based.py --model_response your_model.json
```

### Part2: OmniVerifier RL Training

We apply DAPO to directly train Qwen2.5VL-7B without cold start:

```bash
bash examples/qwen2_5_vl_7b_dapo.sh
```

After training, you should merge the checkpoint in Hugging Face format:

```bash
python3 scripts/model_merger.py --local_dir checkpoints/omniverifier/exp_name/global_step_1/actor
```

### Part3: OmniVerifier-TTS

We provide the code for sequential Omniverifier-TTS using Qwen-Image. You should first generate the step0 image and use this script for iteratively self-refine:

```shell
python sequential_omniverifier_tts.py
```

### Part4: OmniVerifier-M1 RL Training

##### Decoupled Training

```bash
bash examples/M1_decoupled_training.sh
```

##### Joint Training

```bash
bash examples/M1_joint_training.sh
```

## Citation

```
@article{zhang2025generative,
  title={Generative Universal Verifier as Multimodal Meta-Reasoner},
  author={Zhang, Xinchen and Zhang, Xiaoying and Wu, Youbin and Cao, Yanbin and Zhang, Renrui and Chu, Ruihang and Yang, Ling and Yang, Yujiu},
  journal={arXiv preprint arXiv:2510.13804},
  year={2025}
}
@article{zhang2026omniverifier,
  title={OmniVerifier-M1: Multimodal Meta-Verifier with Explicit Structured Recalibration},
  author={Zhang, Xinchen and Liu, Bowei and Liu, Jiale and Shi, Chufan and Zhang, Yizhen and Liu, Junhong and Zhang, Youliang and Li, Zhiheng and Yang, Yujiu and Yang, Ling},
  journal={arXiv preprint arXiv:2605.28805},
  year={2026}
}
```

## Acknowledgements

OmniVerifier is built upon several solid works. Thanks to [EasyR1](https://github.com/hiyouga/EasyR1) and [veRL](https://github.com/volcengine/verl) for their wonderful work and codebase!

OmniVerifier

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