Release VDR-EE benchmark: 250 QA records, dataset card and project figures

README.md

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+---
+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).

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