LongVT-Source

This repository contains the source video and image files for the LongVT project.

Overview

LongVT is an end-to-end agentic framework that enables "Thinking with Long Videos" via interleaved Multimodal Chain-of-Tool-Thought. This dataset provides the raw media files referenced by the training annotations in LongVT-Parquet.

Dataset Structure

The source files are organized by dataset type and stored as zip archives:

Training Data

Source	Description	Files
longvideoreason	Long video reasoning data	66 zips
videor1	Video-R1 COT data	13 zips
longvideoreflection	Long video reflection data	27 zips
selftrace	Self-distilled iMCoTT traces	6 zips
tvg	Temporal video grounding data	2 zips
geminicot	Gemini-distilled COT data	2 zips
llavacot	LLaVA COT data	1 zip
openvlthinker	OpenVLThinker data	1 zip
wemath	WeMath data	1 zip
selfqa	Self-curated QA for RL	1 zip
rl_val	RL validation data	1 zip

Evaluation Data

Source	Description	Files
videosiaheval	VideoSIAH-Eval benchmark videos	12 zips

Download

Install huggingface_hub

pip install huggingface_hub

Download all source files

huggingface-cli download longvideotool/LongVT-Source --repo-type dataset --local-dir ./source

Or download specific files

huggingface-cli download longvideotool/LongVT-Source longvideoreason_1.zip --repo-type dataset --local-dir ./source## Usage

After downloading, extract the zip files to obtain the source media:

cd source unzip "*.zip"The extracted paths will match those referenced in the LongVT-Parquet annotations.

Related Resources

• 📄 Paper: arXiv:2511.20785
• 🌐 Project Page: LongVT Website
• 💻 Code: GitHub Repository
• 📊 Annotations: LongVT-Parquet
• 🤗 Models: LongVT Collection

Citation

If you find LongVT useful for your research and applications, please cite using this BibTeX:

@misc{yang2025longvtincentivizingthinkinglong,
      title={LongVT: Incentivizing "Thinking with Long Videos" via Native Tool Calling}, 
      author={Zuhao Yang and Sudong Wang and Kaichen Zhang and Keming Wu and Sicong Leng and Yifan Zhang and Chengwei Qin and Shijian Lu and Xingxuan Li and Lidong Bing},
      year={2025},
      eprint={2511.20785},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2511.20785}, 
}

License

This dataset is released under the Apache 2.0 License.