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ParaVT/ParaVT-Source

ParaVT-Source Source media archives for the ParaVT training corpus. Pair this repository with the annotations in 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… See the full description on the dataset page: https://huggingface.co/datasets/ParaVT/ParaVT-Source.

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ParaVT-Source

![Paper](https://arxiv.org/abs/2605.20342) ![Website](https://evolvinglmms-lab.github.io/ParaVT/) ![Code](https://github.com/EvolvingLMMs-Lab/ParaVT) ![Data](https://huggingface.co/datasets/ParaVT/ParaVT-Parquet) ![Models](https://huggingface.co/ParaVT/ParaVT-8B) ![Daily Paper](https://huggingface.co/papers/2605.20342)

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Source media archives for the ParaVT training corpus. Pair this repository with the annotations in `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` 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` 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

@article{yang2026paravt,
  title={ParaVT: Taming the Tool Prior Paradox for Parallel Tool Use in Agentic Video Reinforcement Learning},
  author={Yang, Zuhao and Zhang, Kaichen and Wang, Sudong and Wu, Keming and Yang, Zhongyu and Li, Bo and Qi, Xiaojuan and Lu, Shijian and Li, Xingxuan and Bing, Lidong},
  journal={arXiv preprint arXiv:2605.20342},
  year={2026}
}

Acknowledgements

The longvt_source/ archives reuse subsets of the LongVT training media released at `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.