README.md

---
license: apache-2.0
task_categories:
  - video-text-to-text
  - visual-question-answering
language:
  - en
tags:
  - video
  - long-video
  - reasoning
  - tool-calling
  - multimodal
size_categories:
  - 100K<n<1M
viewer: false
---

# ParaVT-Source

<div align="center">

[![Paper](https://img.shields.io/badge/Paper-000000?style=for-the-badge&logo=arxiv&logoColor=white)](https://arxiv.org/abs/2605.20342)
[![Website](https://img.shields.io/badge/Website-000000?style=for-the-badge&logo=google-chrome&logoColor=white)](https://evolvinglmms-lab.github.io/ParaVT/)
[![Code](https://img.shields.io/badge/Code-000000?style=for-the-badge&logo=github&logoColor=white)](https://github.com/EvolvingLMMs-Lab/ParaVT)
[![Data](https://img.shields.io/badge/Data-0040A1?style=for-the-badge&logo=huggingface&logoColor=ffffff)](https://huggingface.co/datasets/ParaVT/ParaVT-Parquet)
[![Models](https://img.shields.io/badge/Models-5EDDD2?style=for-the-badge&logo=huggingface&logoColor=ffffff)](https://huggingface.co/ParaVT/ParaVT-8B)
[![Daily Paper](https://img.shields.io/badge/🚀_Daily_Paper-FF9D00?style=for-the-badge)](https://huggingface.co/papers/2605.20342)

</div>

Source media archives for the [ParaVT](https://github.com/EvolvingLMMs-Lab/ParaVT) training corpus. Pair this repository with the annotations in [`ParaVT/ParaVT-Parquet`](https://huggingface.co/datasets/ParaVT/ParaVT-Parquet).

## Overview

ParaVT is a multi-agent agentic framework for long-video understanding, post-trained with **PARA-GRPO** (Parseability-Anchored and Ratio-gAted GRPO). This dataset bundles the raw video files referenced by every row in `ParaVT-Parquet`, packaged as per-source zip archives.

## Layout

Files are grouped by sentinel bucket. Each archive's members are stored under their full sentinel-form path, so extracting every zip into a single root produces a unified tree that [`paravt.data.materialize`](https://github.com/EvolvingLMMs-Lab/ParaVT/blob/main/paravt/data/materialize.py) can re-link in one call.

| Bucket | Contents | Archive convention |
|---|---|---|
| `longvt_source/<src>/` | LongVT shared training clips (`videor1_*`, `longvideoreason_*`, `geminicot_*`, `tvg_*`, `selftrace_*`) | `<src>_<idx>.zip`, mirroring the [`longvideotool/LongVT-Source`](https://huggingface.co/datasets/longvideotool/LongVT-Source) naming |
| `longvt_source/videor1_<N>/...` *(images)* | Auxiliary image files (`.png` / `.jpg`) referenced by the multi-modal interleaved rows in `paravt_sft_videor1_50k.parquet` | `videor1_<N>_images.zip` (one per `videor1_<N>` bucket) |
| `museg/charades/` | Charades-STA clips used by the `charades` SFT split and the `charades_tvg` RL split | `charades_<idx>.zip` |
| `museg/et_instruct_164k/` | MuSeG `et_instruct_164k` clips used by the `museg` SFT split | `et_instruct_<idx>.zip` |
| `selfqa/` | Self-curated open-ended QA clips (HACS- and Ego4D-derived UUIDs / YouTube IDs) used by the `hacs` and `ego4d_naq` RL splits | `selfqa.zip` (single archive, ~3 GB) |

Each archive is sized to stay below 10 GB on disk so that LFS pointer + Cloudflare CDN serving stays well-behaved. The `<src>_<idx>.zip` and `videor1_<N>_images.zip` archives that share a `videor1_<N>` prefix unzip into disjoint subdirectories of the same `videor1_<N>/` tree (videos under `CLEVRER/`, `LLaVA-Video-178K/`, … and images under `Chart/`, `Math/`, `Knowledge/`, `OCR/`, …), so they don't overwrite each other.

## Usage

```bash
# 1. Download every archive (use --include for a subset; see below).
huggingface-cli download ParaVT/ParaVT-Source --repo-type dataset --local-dir ./paravt-source

# 2. Extract every zip into the same root. Each zip's arcname carries the
#    full sentinel path (e.g. "longvt_source/videor1_7/Math/...png"), so the
#    extraction target must be the top-level root, NOT the per-zip directory.
( cd ./paravt-source && find . -name "*.zip" -exec unzip -q -o -d . {} \; )

# 3. Re-link absolute paths inside the parquets (one shot; see ParaVT/ParaVT-Parquet).
python -m paravt.data.materialize \
    --root        ./paravt-source \
    --parquet-dir ./paravt-parquet \
    --output-dir  ./paravt-parquet-materialized

# Selective: pull only the buckets you need (e.g. Charades grounding only).
# Materialize will warn on missing files but produce valid output for the
# subset that is present.
huggingface-cli download ParaVT/ParaVT-Source \
    --repo-type dataset --local-dir ./paravt-source \
    --include "museg/charades.zip"
```

After step 2 the directory tree under `./paravt-source/` looks like:

```
paravt-source/
├── longvt_source/
│   ├── videor1_<N>/             # mp4 from videor1_<N>.zip + png/jpg from videor1_<N>_images.zip
│   │   ├── CLEVRER/, LLaVA-Video-178K/, NeXT-QA/, ...   # videos
│   │   └── Chart/, Math/, Knowledge/, OCR/, ...         # images
│   ├── longvideoreason_<N>/     # from longvideoreason_<N>_part{1,2}.zip
│   ├── geminicot_<N>/, tvg_<N>/, selftrace_<N>/, ...
├── museg/
│   ├── charades/                # mp4 from museg/charades.zip
│   └── et_instruct_164k/        # mp4 from museg/et_instruct.zip
└── selfqa/                      # mp4 from selfqa/selfqa.zip
```

The materialized parquets then point at `file://<absolute-path-to-paravt-source>/<sentinel-path>` for every row.

## Citation

```bibtex
@misc{yang2026paravt,
  title={{ParaVT}: Taming the Tool Prior Paradox for Parallel Tool Use in Agentic Video Reinforcement Learning},
  author={Zuhao Yang and Kaichen Zhang and Sudong Wang and Keming Wu and Zhongyu Yang and Bo Li and Xiaojuan Qi and Shijian Lu and Xingxuan Li and Lidong Bing},
  year={2026},
  eprint={2605.20342},
  archivePrefix={arXiv},
  primaryClass={cs.CV}
}
```

## Acknowledgements

The `longvt_source/` archives reuse subsets of the [LongVT](https://github.com/EvolvingLMMs-Lab/LongVT) training media released at [`longvideotool/LongVT-Source`](https://huggingface.co/datasets/longvideotool/LongVT-Source); the MuSeG, Charades-STA, HACS, and Ego4D source clips are attributed to their respective original publications and used under their original licenses.