README.md

---
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

![VDR-EE domain distribution and benchmark statistics from the AdaVDR GitHub repository](assets/VDR_EE_domainsstats.png)

*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

![VDR-EE examples covering short, medium, and long events, single entities, and multiple entities](assets/VDR_EE_cases.png)

*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

![Complete AdaVDR benchmark results with direct and agentic settings and entity/event breakdowns](assets/main_results.png)

*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).