| --- |
| 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 |
| --- |
| |
| <h1 align="center">TempCore: Are Video QA Benchmarks Temporally Grounded?</h1> |
|
|
| <p align="center"> |
| <a href="https://arxiv.org/abs/2509.01167">📖 arXiv</a> • |
| <a href="https://huggingface.co/papers/2509.01167">🤗 Paper</a> • |
| <a href="https://github.com/EvolvingLMMs-Lab/lmms-eval/tree/main/lmms_eval/tasks/tempcore">🧪 lmms-eval task</a> • |
| EMNLP 2026 |
| </p> |
|
|
| **TempCore** is the temporally sensitive subset of six Video QA benchmarks: |
| **2,560 questions** that need the video *and* whose answer depends on which |
| frames a model sees. |
|
|
| ## 🚀 Evaluate with lmms-eval |
|
|
| TempCore is built into [lmms-eval](https://github.com/EvolvingLMMs-Lab/lmms-eval). |
| This is the recommended way to evaluate on it: no access request to this |
| repository is needed, since each task fetches its videos from the source |
| benchmark. |
|
|
| ```bash |
| git clone https://github.com/EvolvingLMMs-Lab/lmms-eval && cd lmms-eval |
| uv sync --extra video --extra qwen |
| |
| uv run python -m lmms_eval --model qwen3_vl \ |
| --model_args pretrained=Qwen/Qwen3-VL-8B-Instruct \ |
| --tasks tempcore --batch_size 1 |
| ``` |
|
|
| | lmms-eval task | Source task | Questions | |
| |----------------|-------------|----------:| |
| | `tempcore` | all six below | 2,560 | |
| | `tempcore_mvbench` | `mvbench_*` (18 subtasks) | 209 | |
| | `tempcore_nextqa` | `nextqa_mc_test` | 629 | |
| | `tempcore_egoschema` | `egoschema_subset` | 96 | |
| | `tempcore_mlvu` | `mlvu_dev` | 673 | |
| | `tempcore_longvideobench` | `longvideobench_val_v` | 261 | |
| | `tempcore_videomme` | `videomme` | 692 | |
|
|
| Each task reuses the matching lmms-eval benchmark task (prompt, frame sampling, |
| scoring) and keeps only the TempCore questions, so a TempCore score is directly |
| comparable with the full-benchmark score of the same task. |
|
|
| ## 📌 Overview |
|
|
| <!-- overview figure: drop assets/tempcore_overview.png here --> |
| |
| 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). |
| |
| | Config | Source benchmark | TempCore questions | Videos | Videos in this repo | |
| |--------|------------------|-------------------:|-------:|:--------------------| |
| | `mvbench` | MVBench | 209 | 203 | ✅ | |
| | `nextqa` | NExT-QA (MC test) | 629 | 421 | ✅ | |
| | `egoschema` | EgoSchema (500-question subset) | 96 | 96 | ❌ fetch from source | |
| | `mlvu` | MLVU (dev, MC) | 673 | 488 | ✅ | |
| | `longvideobench` | LongVideoBench (val) | 261 | 219 | ✅ | |
| | `videomme` | Video-MME | 692 | 511 | ❌ fetch from source | |
| |
| *Temporally grounded*: FSS > 0.15, the most relevant frames help. |
| *Visual bias*: FSS < −0.15, the frames an encoder ranks as most relevant mislead |
| the models. |
| |
| ## 📦 Using this repository |
| |
| This repository holds the TempCore questions in one schema across the six |
| benchmarks, the videos of four of them, and per-sample scores. Request access |
| above to download it. |
| |
| ```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 videos come from Ego4D, whose license does not allow redistribution, |
| and Video-MME may not be redistributed without the authors' approval. |
| `scripts/fetch_videos.py` pulls the needed videos from the upstream Hub |
| repositories into the same layout (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. |
|
|
| ## 🗂️ 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. |
|
|
