Datasets:

HJOK
/

TempCore

---
license: cc-by-nc-sa-4.0
language:
- en
pretty_name: TempCore
size_categories:
- 1K<n<10K
task_categories:
- video-text-to-text
- visual-question-answering
tags:
- video-qa
- temporal-reasoning
- benchmark
extra_gated_prompt: >-
  TempCore redistributes videos from MVBench, NExT-QA, MLVU and LongVideoBench.
  TempCore does not own these videos; all rights and copyright belong to their
  original creators and platforms. Each video stays under the license and terms
  of its source benchmark, and copyright holders may ask for a video to be
  removed. By requesting access you agree to the following terms.
extra_gated_fields:
  Name: text
  Affiliation: text
  I will use this dataset for non-commercial research only: checkbox
  I agree to the licenses and terms of the source benchmarks (MVBench, NExT-QA, MLVU, LongVideoBench, EgoSchema, Video-MME): checkbox
  I agree with the CC-BY-NC-SA data license of this dataset: checkbox
configs:
- config_name: mvbench
  data_files:
  - split: test
    path: data/mvbench.jsonl
- config_name: nextqa
  data_files:
  - split: test
    path: data/nextqa.jsonl
- config_name: egoschema
  data_files:
  - split: test
    path: data/egoschema.jsonl
- config_name: mlvu
  data_files:
  - split: test
    path: data/mlvu.jsonl
- config_name: longvideobench
  data_files:
  - split: test
    path: data/longvideobench.jsonl
- config_name: videomme
  data_files:
  - split: test
    path: data/videomme.jsonl
---
# TempCore

Data for the EMNLP 2026 paper
*TempCore: Are Video QA Benchmarks Temporally Grounded?*
([arXiv:2509.01167](https://arxiv.org/abs/2509.01167)).

Most Video QA questions can be answered no matter which frames a VLM sees. We
measure this per sample with **Frame Selection Sensitivity (FSS)** — the change
in accuracy when the most relevant frames (SigLIP MaxProb) are swapped for the
least relevant ones (MinProb), averaged over eight VLMs — and combine it with a
**Language Independence Score (LIS)** from no-frame predictions. **TempCore** is
the subset of each benchmark that is both vision-dependent (LIS > 0.5) and
frame-sensitive (|FSS| > 0.15).

This repository holds the TempCore questions with their videos, in one schema
across the six source benchmarks.

| Config | Source benchmark | TempCore questions | Videos | Videos in this repo |
|--------|------------------|------------------:|-------:|:--------------------|
| `mvbench` | MVBench | 209 | 203 | yes |
| `nextqa` | NExT-QA (MC test) | 629 | 421 | yes |
| `mlvu` | MLVU (dev, MC) | 673 | 488 | yes |
| `longvideobench` | LongVideoBench (val) | 261 | 219 | yes |
| `egoschema` | EgoSchema (500-question subset) | 96 | 96 | no — fetch from source |
| `videomme` | Video-MME | 692 | 511 | no — fetch from source |

EgoSchema videos come from Ego4D, whose license does not allow redistribution,
and Video-MME may not be redistributed without the authors' approval. Their rows
are included; `scripts/fetch_videos.py` pulls the needed videos from the
upstream Hub repositories into the same layout (see below).

## Usage

```python
from datasets import load_dataset
ds = load_dataset("HJOK/TempCore", "mlvu", split="test")
row = ds[0]
row["question"], row["options"], row["answer"], row["video_path"]
```

`video_path` is relative to the repository root. Download the videos for one
benchmark (or `videos/*` for all hosted ones):

```bash
hf download HJOK/TempCore --repo-type dataset --local-dir TempCore \
    --include "data/*" "scripts/*" "videos/mlvu/*"
```

EgoSchema and Video-MME videos (accept the upstream terms first; only the needed
members are read from the upstream archives, not the whole archives):

```bash
cd TempCore
pip install huggingface_hub requests
python scripts/fetch_videos.py --benchmark egoschema videomme --out .
```

`SHA256SUMS` lists the checksum of every hosted video.

## Evaluate with lmms-eval

TempCore is a task group in [lmms-eval](https://github.com/EvolvingLMMs-Lab/lmms-eval)
([task README](https://github.com/EvolvingLMMs-Lab/lmms-eval/tree/main/lmms_eval/tasks/tempcore)):

```bash
python -m lmms_eval --model qwen3_vl --model_args pretrained=Qwen/Qwen3-VL-8B-Instruct \
    --tasks tempcore --batch_size 1
```

`tempcore` runs `tempcore_mvbench`, `tempcore_nextqa`, `tempcore_egoschema`,
`tempcore_mlvu`, `tempcore_longvideobench` and `tempcore_videomme`; each can be
run on its own. Each task reuses the matching lmms-eval benchmark task
(prompt, video loading, scoring) and keeps only the TempCore questions, so it
downloads the videos from the original benchmark repositories rather than from
this one. Video tasks need `uv sync --extra video`.

## Fields

| Field | Description |
|-------|-------------|
| `sample_id` | Stable id within the source benchmark (MVBench `<task>/<index_in_task>`, NExT-QA `<video>/<row>`, EgoSchema `<q_uid>`, MLVU `<json_stem>/<index_in_file>`, LongVideoBench `<id>`, Video-MME `<question_id>`) |
| `benchmark` | Config name |
| `video_path` | Video file, relative to the repository root |
| `video_hosted` | Whether the video is stored in this repository |
| `video_source` | Upstream location: `repo_id`, `files` (archive or file names), `format` (`zip` / `tar` / `file`), `member` |
| `question` | Question text, as in the source benchmark |
| `options` | Answer options, with any leading `A.`-style letters removed |
| `answer` / `answer_index` | Correct option as a letter (A–E) and a 0-based index |
| `fss`, `lis` | Frame Selection Sensitivity and Language Independence Score |
| `fss_class` | `key` (FSS > 0.15), `antikey` (FSS < −0.15) or `nonkey` |
| `category` | `temporally_grounded` (key) or `visual_bias` (antikey); every TempCore row is one of these two |
| `tempcore` | Always `true` here |
| `n_models_fss`, `n_models_lis` | Number of models behind each score (8) |
| `per_model` | Per-model correctness (0/1) under MaxProb, MinProb and no-frame input |
| `meta` | JSON string with the remaining source fields (e.g. MVBench `task`, `start`/`end`; MLVU `question_type`, `duration`; LongVideoBench categories and subtitle file name; Video-MME `duration`, `domain`, `task_type`) |

Scores come from eight VLMs: SmolVLM2-256M/500M/2.2B, LLaVA-OneVision-0.5B/7B,
VideoLLaMA3-2B, Qwen3-VL-8B and InternVL3.5-8B, with K=32 frames scored by
SigLIP2-so400m on question and answer text.

`annotations/` holds the scores for **every** sample of the six benchmarks (not
only TempCore), so thresholds can be changed without re-running models, and
`annotations/human_eval/` the 100 human temporal-necessity ratings from the
paper. These files carry ids and text hashes only.

## Video sources and processing

| Config | Upstream repository | Upstream license / terms |
|--------|---------------------|--------------------------|
| `mvbench` | [`OpenGVLab/MVBench`](https://huggingface.co/datasets/OpenGVLab/MVBench) | MIT; videos for academic research only, and some sources carry their own agreements |
| `nextqa` | [`lmms-lab/NExTQA`](https://huggingface.co/datasets/lmms-lab/NExTQA) (videos from VidOR) | NExT-QA: MIT, cite and attach the source |
| `mlvu` | [`sy1998/MLVU`](https://huggingface.co/datasets/sy1998/MLVU) (files identical by SHA-256 to [`MLVU/MVLU`](https://huggingface.co/datasets/MLVU/MVLU)) | CC-BY-NC-SA 4.0, academic research only |
| `longvideobench` | [`longvideobench/LongVideoBench`](https://huggingface.co/datasets/longvideobench/LongVideoBench) | CC-BY-NC-SA 4.0, non-commercial only |
| `egoschema` | [`lmms-lab/egoschema`](https://huggingface.co/datasets/lmms-lab/egoschema) | Ego4D license |
| `videomme` | [`lmms-lab/Video-MME`](https://huggingface.co/datasets/lmms-lab/Video-MME) | Academic research only; no redistribution without approval |

Videos are stored byte-for-byte as extracted from the upstream files, with one
exception: the 23 MVBench `episodic_reasoning` questions use TVQA clips that
MVBench distributes as 3 fps JPEG frames. Those are encoded to H.264 mp4 at
3 fps (CRF 18), keeping one video frame per source frame. MVBench `start`/`end`
clip bounds are kept in `meta`; the paper's scores use the whole video file.

## License

The questions, annotations and scores are released under CC-BY-NC-SA 4.0. The
videos remain the property of their creators and are covered by the terms of
their source benchmark listed above. If you own a video in this dataset and want
it removed, open a discussion on this repository.

## Citation

```bibtex
@inproceedings{ok2026tempcore,
  title     = {TempCore: Are Video QA Benchmarks Temporally Grounded?},
  author    = {Ok, Hyunjong and Lee, Jaeho},
  booktitle = {Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing},
  year      = {2026}
}
```

Please also cite the source benchmarks you use.

	---
	license: cc-by-nc-sa-4.0
	language:
	- en
	pretty_name: TempCore
	size_categories:
	- 1K<n<10K
	task_categories:
	- video-text-to-text
	- visual-question-answering
	tags:
	- video-qa
	- temporal-reasoning
	- benchmark
	extra_gated_prompt: >-
	TempCore redistributes videos from MVBench, NExT-QA, MLVU and LongVideoBench.
	TempCore does not own these videos; all rights and copyright belong to their
	original creators and platforms. Each video stays under the license and terms
	of its source benchmark, and copyright holders may ask for a video to be
	removed. By requesting access you agree to the following terms.
	extra_gated_fields:
	Name: text
	Affiliation: text
	I will use this dataset for non-commercial research only: checkbox
	I agree to the licenses and terms of the source benchmarks (MVBench, NExT-QA, MLVU, LongVideoBench, EgoSchema, Video-MME): checkbox
	I agree with the CC-BY-NC-SA data license of this dataset: checkbox
	configs:
	- config_name: mvbench
	data_files:
	- split: test
	path: data/mvbench.jsonl
	- config_name: nextqa
	data_files:
	- split: test
	path: data/nextqa.jsonl
	- config_name: egoschema
	data_files:
	- split: test
	path: data/egoschema.jsonl
	- config_name: mlvu
	data_files:
	- split: test
	path: data/mlvu.jsonl
	- config_name: longvideobench
	data_files:
	- split: test
	path: data/longvideobench.jsonl
	- config_name: videomme
	data_files:
	- split: test
	path: data/videomme.jsonl
	---

	# TempCore

	Data for the EMNLP 2026 paper
	TempCore: Are Video QA Benchmarks Temporally Grounded?
	([arXiv:2509.01167](https://arxiv.org/abs/2509.01167)).

	Most Video QA questions can be answered no matter which frames a VLM sees. We
	measure this per sample with Frame Selection Sensitivity (FSS) — the change
	in accuracy when the most relevant frames (SigLIP MaxProb) are swapped for the
	least relevant ones (MinProb), averaged over eight VLMs — and combine it with a
	Language Independence Score (LIS) from no-frame predictions. TempCore is
	the subset of each benchmark that is both vision-dependent (LIS > 0.5) and
	frame-sensitive (\|FSS\| > 0.15).

	This repository holds the TempCore questions with their videos, in one schema
	across the six source benchmarks.

	\| Config \| Source benchmark \| TempCore questions \| Videos \| Videos in this repo \|
	\|--------\|------------------\|------------------:\|-------:\|:--------------------\|
	\| `mvbench` \| MVBench \| 209 \| 203 \| yes \|
	\| `nextqa` \| NExT-QA (MC test) \| 629 \| 421 \| yes \|
	\| `mlvu` \| MLVU (dev, MC) \| 673 \| 488 \| yes \|
	\| `longvideobench` \| LongVideoBench (val) \| 261 \| 219 \| yes \|
	\| `egoschema` \| EgoSchema (500-question subset) \| 96 \| 96 \| no — fetch from source \|
	\| `videomme` \| Video-MME \| 692 \| 511 \| no — fetch from source \|

	EgoSchema videos come from Ego4D, whose license does not allow redistribution,
	and Video-MME may not be redistributed without the authors' approval. Their rows
	are included; `scripts/fetch_videos.py` pulls the needed videos from the
	upstream Hub repositories into the same layout (see below).

	## Usage

	```python
	from datasets import load_dataset

	ds = load_dataset("HJOK/TempCore", "mlvu", split="test")
	row = ds[0]
	row["question"], row["options"], row["answer"], row["video_path"]
	```

	`video_path` is relative to the repository root. Download the videos for one
	benchmark (or `videos/*` for all hosted ones):

	```bash
	hf download HJOK/TempCore --repo-type dataset --local-dir TempCore \
	--include "data/" "scripts/" "videos/mlvu/*"
	```

	EgoSchema and Video-MME videos (accept the upstream terms first; only the needed
	members are read from the upstream archives, not the whole archives):

	```bash
	cd TempCore
	pip install huggingface_hub requests
	python scripts/fetch_videos.py --benchmark egoschema videomme --out .
	```

	`SHA256SUMS` lists the checksum of every hosted video.

	## Evaluate with lmms-eval

	TempCore is a task group in [lmms-eval](https://github.com/EvolvingLMMs-Lab/lmms-eval)
	([task README](https://github.com/EvolvingLMMs-Lab/lmms-eval/tree/main/lmms_eval/tasks/tempcore)):

	```bash
	python -m lmms_eval --model qwen3_vl --model_args pretrained=Qwen/Qwen3-VL-8B-Instruct \
	--tasks tempcore --batch_size 1
	```

	`tempcore` runs `tempcore_mvbench`, `tempcore_nextqa`, `tempcore_egoschema`,
	`tempcore_mlvu`, `tempcore_longvideobench` and `tempcore_videomme`; each can be
	run on its own. Each task reuses the matching lmms-eval benchmark task
	(prompt, video loading, scoring) and keeps only the TempCore questions, so it
	downloads the videos from the original benchmark repositories rather than from
	this one. Video tasks need `uv sync --extra video`.

	## Fields

	\| Field \| Description \|
	\|-------\|-------------\|
	\| `sample_id` \| Stable id within the source benchmark (MVBench `<task>/<index_in_task>`, NExT-QA `<video>/<row>`, EgoSchema `<q_uid>`, MLVU `<json_stem>/<index_in_file>`, LongVideoBench `<id>`, Video-MME `<question_id>`) \|
	\| `benchmark` \| Config name \|
	\| `video_path` \| Video file, relative to the repository root \|
	\| `video_hosted` \| Whether the video is stored in this repository \|
	\| `video_source` \| Upstream location: `repo_id`, `files` (archive or file names), `format` (`zip` / `tar` / `file`), `member` \|
	\| `question` \| Question text, as in the source benchmark \|
	\| `options` \| Answer options, with any leading `A.`-style letters removed \|
	\| `answer` / `answer_index` \| Correct option as a letter (A–E) and a 0-based index \|
	\| `fss`, `lis` \| Frame Selection Sensitivity and Language Independence Score \|
	\| `fss_class` \| `key` (FSS > 0.15), `antikey` (FSS < −0.15) or `nonkey` \|
	\| `category` \| `temporally_grounded` (key) or `visual_bias` (antikey); every TempCore row is one of these two \|
	\| `tempcore` \| Always `true` here \|
	\| `n_models_fss`, `n_models_lis` \| Number of models behind each score (8) \|
	\| `per_model` \| Per-model correctness (0/1) under MaxProb, MinProb and no-frame input \|
	\| `meta` \| JSON string with the remaining source fields (e.g. MVBench `task`, `start`/`end`; MLVU `question_type`, `duration`; LongVideoBench categories and subtitle file name; Video-MME `duration`, `domain`, `task_type`) \|

	Scores come from eight VLMs: SmolVLM2-256M/500M/2.2B, LLaVA-OneVision-0.5B/7B,
	VideoLLaMA3-2B, Qwen3-VL-8B and InternVL3.5-8B, with K=32 frames scored by
	SigLIP2-so400m on question and answer text.

	`annotations/` holds the scores for every sample of the six benchmarks (not
	only TempCore), so thresholds can be changed without re-running models, and
	`annotations/human_eval/` the 100 human temporal-necessity ratings from the
	paper. These files carry ids and text hashes only.

	## Video sources and processing

	\| Config \| Upstream repository \| Upstream license / terms \|
	\|--------\|---------------------\|--------------------------\|
	\| `mvbench` \| [`OpenGVLab/MVBench`](https://huggingface.co/datasets/OpenGVLab/MVBench) \| MIT; videos for academic research only, and some sources carry their own agreements \|
	\| `nextqa` \| [`lmms-lab/NExTQA`](https://huggingface.co/datasets/lmms-lab/NExTQA) (videos from VidOR) \| NExT-QA: MIT, cite and attach the source \|
	\| `mlvu` \| [`sy1998/MLVU`](https://huggingface.co/datasets/sy1998/MLVU) (files identical by SHA-256 to [`MLVU/MVLU`](https://huggingface.co/datasets/MLVU/MVLU)) \| CC-BY-NC-SA 4.0, academic research only \|
	\| `longvideobench` \| [`longvideobench/LongVideoBench`](https://huggingface.co/datasets/longvideobench/LongVideoBench) \| CC-BY-NC-SA 4.0, non-commercial only \|
	\| `egoschema` \| [`lmms-lab/egoschema`](https://huggingface.co/datasets/lmms-lab/egoschema) \| Ego4D license \|
	\| `videomme` \| [`lmms-lab/Video-MME`](https://huggingface.co/datasets/lmms-lab/Video-MME) \| Academic research only; no redistribution without approval \|

	Videos are stored byte-for-byte as extracted from the upstream files, with one
	exception: the 23 MVBench `episodic_reasoning` questions use TVQA clips that
	MVBench distributes as 3 fps JPEG frames. Those are encoded to H.264 mp4 at
	3 fps (CRF 18), keeping one video frame per source frame. MVBench `start`/`end`
	clip bounds are kept in `meta`; the paper's scores use the whole video file.

	## License

	The questions, annotations and scores are released under CC-BY-NC-SA 4.0. The
	videos remain the property of their creators and are covered by the terms of
	their source benchmark listed above. If you own a video in this dataset and want
	it removed, open a discussion on this repository.

	## Citation

	```bibtex
	@inproceedings{ok2026tempcore,
	title = {TempCore: Are Video QA Benchmarks Temporally Grounded?},
	author = {Ok, Hyunjong and Lee, Jaeho},
	booktitle = {Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing},
	year = {2026}
	}
	```

	Please also cite the source benchmarks you use.