---
base_model:
- Qwen/Qwen2.5-VL-7B-Instruct
datasets:
- Gnonymous/Web-CogDataset
language:
- en
- zh
license: apache-2.0
pipeline_tag: image-text-to-text
---

# Web-CogReasoner

[**Web-CogReasoner**](https://huggingface.co/papers/2508.01858) is a knowledge-driven multimodal agent designed for cognitive reasoning in web environments. It introduces a paradigm shift by systematically building agent capabilities through a two-stage training process: knowledge content learning (Factual, Conceptual) and cognitive processes (Procedural).

- **Paper:** [Web-CogReasoner: Towards Knowledge-Induced Cognitive Reasoning for Web Agents](https://huggingface.co/papers/2508.01858)
- **Project Page:** [https://eohan.me/Web-CogReasoner](https://eohan.me/Web-CogReasoner)
- **Repository:** [https://github.com/Gnonymous/Web-CogReasoner](https://github.com/Gnonymous/Web-CogReasoner)

Web-CogReasoner is trained using the [Web-CogDataset](https://huggingface.co/datasets/Gnonymous/Web-CogDataset) and employs a novel knowledge-driven Chain-of-Thought (CoT) reasoning framework to generalize to unseen web tasks.

## Performance

Web-CogReasoner demonstrates significant superiority over existing models across various benchmarks:

| Benchmark | Score |
| :--- | :---: |
| Web-CogBench | 84.4 |
| VisualWebBench | 86.3 |
| WebVoyager | 30.2% |
| Online Multimodal-Mind2Web (Cross-Tasks) | 17.0% |
| Online Multimodal-Mind2Web (Cross-Webs) | 10.1% |

## Citation

If you find this work helpful, please cite the following paper:

```bibtex
@article{guo2025web,
  title={Web-CogReasoner: Towards Knowledge-Induced Cognitive Reasoning for Web Agents},
  author={Guo, Yuhan and Guo, Cong and Sun, Aiwen and He, Hongliang and Yang, Xinyu and Lu, Yue and Zhang, Yingji and Guo, Xuntao and Zhang, Dong and Liu, Jianzhuang and others},
  journal={arXiv preprint arXiv:2508.01858},
  year={2025}
}
```