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.

TempCore

Data for the EMNLP 2026 paper TempCore: Are Video QA Benchmarks Temporally Grounded? (arXiv: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

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):

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):

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

SHA256SUMS lists the checksum of every hosted 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`	MIT; videos for academic research only, and some sources carry their own agreements
`nextqa`	`lmms-lab/NExTQA` (videos from VidOR)	NExT-QA: MIT, cite and attach the source
`mlvu`	`sy1998/MLVU` (files identical by SHA-256 to `MLVU/MVLU`)	CC-BY-NC-SA 4.0, academic research only
`longvideobench`	`longvideobench/LongVideoBench`	CC-BY-NC-SA 4.0, non-commercial only
`egoschema`	`lmms-lab/egoschema`	Ego4D license
`videomme`	`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

@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.

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Paper for HJOK/TempCore

TempCore: Are Video QA Benchmarks Temporally Grounded?

Paper • 2509.01167 • Published Sep 20