| --- |
| language: |
| - en |
| size_categories: |
| - n<1K |
| task_categories: |
| - question-answering |
| pretty_name: VDR-EE — Video Deep Research Benchmark |
| tags: |
| - video-question-answering |
| - video-deep-research |
| - entity-centric |
| - event-centric |
| - benchmark |
| - adavdr |
| - arxiv:2608.25559 |
| configs: |
| - config_name: default |
| data_files: |
| - split: test |
| path: data/*.parquet |
| --- |
| |
| # VDR-EE: Entity- and Event-Centric Video Deep Research |
|
|
| VDR-EE is the evaluation benchmark introduced in |
| [AdaVDR: Adaptive Tool Use and Reflection for Video Deep Research](https://arxiv.org/abs/2608.25559). |
| Its 250 manually verified questions connect entities or events in videos to facts |
| from external sources, testing joint video understanding and knowledge retrieval. |
|
|
| [Paper (PDF)](https://arxiv.org/pdf/2608.25559) · |
| [GitHub](https://github.com/Accio-Lab/AdaVDR) · |
| [Project page](https://accio-lab.github.io/AdaVDR/) |
|
|
| This repository provides one `test` split in Parquet format, containing questions, |
| reference answers, category labels, video source links, and external evidence URLs. |
| Video files are not included. The original question order is preserved. |
|
|
| ## What does VDR-EE contain? |
|
|
| The benchmark covers two complementary forms of video deep research: |
|
|
| - **Entity-centric questions (153):** identify a target entity or connect multiple |
| entities appearing in a video, then retrieve the external facts needed to answer |
| the question. The categories are Single Entity and Multiple Entity. |
| - **Event-centric questions (97):** recognize and reason about an event shown in a |
| video, then connect it to external knowledge. The categories are Short Event, |
| Medium Event, and Long Event, based on the target event's duration. |
|
|
| Each released record includes a question, reference answer, stable identifier, |
| domain and question-type labels, the source video URL and identifier, an optional |
| video title, and a list of external evidence URLs. The seven domains are Culture, |
| Entertainment, Industrial, News, Scene understanding, Science, and Sports. |
|
|
| ## Video access and copyright |
|
|
| Due to copyright considerations, this repository distributes annotations and |
| source links only, rather than the original video files. Video rights remain |
| with their respective copyright holders. |
|
|
| To obtain the videos, download them from the original sources listed in the |
| `youtube_url` column, subject to the source platform's terms and applicable |
| permissions. For assistance with video access, please contact |
| [zhangxt119@gmail.com](mailto:zhangxt119@gmail.com). |
|
|
| Providing a source link does not grant a license to redistribute the associated |
| video. Source availability may change over time. |
|
|
| ## Dataset statistics |
|
|
|  |
|
|
| *Original figure from the [AdaVDR repository](https://github.com/Accio-Lab/AdaVDR#vdr-ee-benchmark). |
| The figure's event-subcategory counts differ from the labels in this Parquet |
| release; use the Parquet labels when computing category-specific metrics.* |
|
|
| ## Representative questions |
|
|
|  |
|
|
| *Illustrative benchmark examples from the |
| [AdaVDR project](https://github.com/Accio-Lab/AdaVDR). The figure shows the five |
| question categories and how video observations are combined with external facts.* |
|
|
| ## Columns |
|
|
| | Column | Type | Description | |
| | --- | --- | --- | |
| | `uid` | string | Stable UUIDv5 derived from the original question ID. | |
| | `question` | string | Question text, preserved from the source. | |
| | `answer` | string | Reference answer, preserved from the source. | |
| | `domain` | string | Culture, Entertainment, Industrial, News, Scene understanding, Science, or Sports. | |
| | `type` | string | Event or Entity. | |
| | `finegrained_type` | string | Long Event, Medium Event, Short Event, Single Entity, or Multiple Entity. | |
| | `youtube_url` | nullable string | Canonical source video URL; null if the source cannot be established. | |
| | `video_id` | string | Original video identifier, preserved as supplied; some identifiers are cache hashes. | |
| | `video_title` | nullable string | Title supplied by the source; missing titles remain null. | |
| | `evidence_sources` | list of strings | External evidence URLs, preserved as a native list. | |
|
|
| The domain and both type fields remain separate columns for filtering. Local |
| video paths and internal review records are excluded from this export. |
|
|
| ## Load |
|
|
| ```python |
| from datasets import load_dataset |
| |
| dataset = load_dataset("Accio-Lab/VDR-EE-Benchmark", split="test") |
| |
| example = dataset[0] |
| print(example["question"]) |
| print(example["youtube_url"]) |
| ``` |
|
|
| For a downloaded local copy: |
|
|
| ```python |
| dataset = load_dataset("parquet", data_files={"test": "data/*.parquet"}, split="test") |
| ``` |
|
|
| ## Evaluation |
|
|
| Use `question` as the query, retrieve the associated video using `youtube_url`, |
| and compare the resulting answer with `answer`. Report overall performance and |
| breakdowns by `type`, `finegrained_type`, and `domain` as appropriate. Reference |
| answers and `evidence_sources` support evaluation; they should not be supplied to |
| an agent as part of its question input. |
|
|
| **Evaluation code and running instructions:** |
| [Accio-Lab/AdaVDR on GitHub](https://github.com/Accio-Lab/AdaVDR). |
| The project's evaluation code is released through GitHub separately from this |
| Hugging Face dataset. Follow that repository for the evaluation implementation, |
| environment setup, and execution instructions as they become available. |
|
|
| As of October 20, 2026, the GitHub README still lists the benchmark and evaluation |
| code release as pending. This dataset card therefore links to the project without |
| assuming that runnable evaluation scripts are already public. |
|
|
| ## Benchmark results |
|
|
| The following accuracy scores (%) are reported by the |
| [AdaVDR project](https://github.com/Accio-Lab/AdaVDR#results), using GPT-5.4 as the |
| semantic answer judge in the agentic setting. They are published project results, |
| not a new evaluation run for this dataset upload. |
|
|
| | Model | VDR-EE Entity | VDR-EE Event | VDR-EE Overall | VideoDR Overall | |
| | --- | ---: | ---: | ---: | ---: | |
| | Qwen3.5-9B | 42.48 | 36.08 | 40.00 | 37.00 | |
| | AdaVDR-9B-SFT | 47.06 | 49.48 | 48.00 | 52.00 | |
| | **AdaVDR-9B** | **50.33** | **62.89** | **55.20** | **66.00** | |
|
|
| Compared with the agentic Qwen3.5-9B baseline, AdaVDR-9B improves overall accuracy |
| by 15.20 percentage points on VDR-EE and 29.00 points on VideoDR. |
|
|
| ### Complete reported results |
|
|
|  |
|
|
| *Original results table from the |
| [AdaVDR repository](https://github.com/Accio-Lab/AdaVDR#results), including other |
| baselines and question-subtype breakdowns. Event-subtype results retain the |
| published grouping; do not recompute their aggregates using this release's |
| different event-subcategory counts.* |
|
|
| ## Citation |
|
|
| If you use VDR-EE in your research, please cite the AdaVDR paper: |
|
|
| ```bibtex |
| @article{zhang2026adavdr, |
| title={AdaVDR: Adaptive Tool Use and Reflection for Video Deep Research}, |
| author={Zhang, Xintong and Fan, Xiaomeng and Yan, Shilin and He, Ekko and Liu, Zicheng and Zou, Zijian and Zhang, Guannan and Wu, Yuwei and Gao, Zhi and Xue, Hongwei}, |
| journal={arXiv preprint arXiv:2608.25559}, |
| year={2026} |
| } |
| ``` |
|
|
| ## Contact |
|
|
| Questions about the benchmark or video access: |
| [zhangxt119@gmail.com](mailto:zhangxt119@gmail.com). |
|
|
