| --- |
| license: cc-by-nc-sa-4.0 |
| language: |
| - en |
| pretty_name: TRACE-Bench |
| size_categories: |
| - 1K<n<10K |
| task_categories: |
| - video-text-to-text |
| - question-answering |
| tags: |
| - affective-computing |
| - emotion |
| - video-understanding |
| - multimodal |
| - benchmark |
| configs: |
| - config_name: task_1_grounded_affect_recognition |
| data_files: |
| - split: test |
| path: data/task_1_grounded_affect_recognition.jsonl |
| - config_name: task_2_regulation_decoding |
| data_files: |
| - split: test |
| path: data/task_2_regulation_decoding.jsonl |
| - config_name: task_3_affective_cause_reasoning |
| data_files: |
| - split: test |
| path: data/task_3_affective_cause_reasoning.jsonl |
| - config_name: task_4_affective_effect_reasoning |
| data_files: |
| - split: test |
| path: data/task_4_affective_effect_reasoning.jsonl |
| - config_name: task_5_full_chain_reconstruction |
| data_files: |
| - split: test |
| path: data/task_5_full_chain_reconstruction.jsonl |
| --- |
| |
| # TRACE-Bench |
|
|
| TRACE-Bench evaluates how models trace an emotion in a social video, from its causes to its consequences. Each item asks a model to watch a clip and fill a structured answer about one affective episode: what the subject feels, how it shows, whether the display is regulated, what caused the feeling, and what it changes in the people around. |
|
|
| Paper: [arXiv:2610.11410](https://arxiv.org/abs/2610.11410). |
|
|
| This repository holds the questions, the reference answers and the task instructions. The video clips and their subtitles are in the gated repository [county/TRACE-Bench-media](https://huggingface.co/datasets/county/TRACE-Bench-media). |
|
|
| ## Tasks |
|
|
| | Config | Task | |
| |---|---| |
| | `task_1_grounded_affect_recognition` | Task 1. Grounded Affect Recognition | |
| | `task_2_regulation_decoding` | Task 2. Regulation Decoding | |
| | `task_3_affective_cause_reasoning` | Task 3. Affective Cause Reasoning | |
| | `task_4_affective_effect_reasoning` | Task 4. Affective Effect Reasoning | |
| | `task_5_full_chain_reconstruction` | Task 5. Full Chain Reconstruction | |
|
|
| Every task is a single `test` split. |
|
|
| ## Files |
|
|
| ``` |
| data/ |
| task_1_grounded_affect_recognition.jsonl |
| task_2_regulation_decoding.jsonl |
| task_3_affective_cause_reasoning.jsonl |
| task_4_affective_effect_reasoning.jsonl |
| task_5_full_chain_reconstruction.jsonl |
| videos.csv |
| instructions/ |
| task_1_grounded_affect_recognition.md |
| ... |
| task_5_full_chain_reconstruction.md |
| ``` |
|
|
| Each line of a task file is one item. |
|
|
| | Field | Meaning | |
| |---|---| |
| | `unique_id` | item id, `<video_id>_T<task>_<chain>_<k>` | |
| | `video_id` | the clip the item is about, a row of `videos.csv` | |
| | `task_id`, `task_name` | task number and name | |
| | `chain_id` | the affective chain inside the clip that the item belongs to | |
| | `known_fields` | the fields given in the question, as a JSON object | |
| | `question` | the prompt as a list of lines: task statement, focal context, known fields, answer template | |
| | `answer` | the reference answer, with the fields of the answer template that are populated | |
|
|
| `videos.csv` lists every clip with the collection it comes from: `video_id`, `source_collection`, `has_subtitle`. |
|
|
| `instructions/` holds one task instruction per task. It defines the fields of that task and their admissible values. |
|
|
| ## Loading |
|
|
| ```python |
| from datasets import load_dataset |
| |
| from huggingface_hub import hf_hub_download |
| |
| task = "task_1_grounded_affect_recognition" |
| items = load_dataset("county/TRACE-Bench", task, split="test") |
| instruction = open(hf_hub_download("county/TRACE-Bench", f"instructions/{task}.md", repo_type="dataset")).read() |
| prompt = instruction.strip() + "\n\n" + "\n".join(items[0]["question"]) |
| ``` |
|
|
| The prompt refers to the clip `items[0]["video_id"]`. A model receives the clip, its subtitles if it reads text, and the prompt, which is the task instruction followed by the question, and returns a JSON object that follows the answer template. |
|
|
| ## Evaluation |
|
|
| Categorical fields are compared directly with the reference, and free-text fields are rated by a judge model, as described in the paper. |
|
|
| ## License |
|
|
| The questions and the reference answers are released under [CC BY-NC-SA 4.0](LICENSE). The video clips are not part of this repository. They come from public collections and remain the property of their copyright holders, and they are available for non-commercial research under the terms of [county/TRACE-Bench-media](https://huggingface.co/datasets/county/TRACE-Bench-media). |
|
|
| ## Citation |
|
|
| ```bibtex |
| @article{li2026trace, |
| title = {Cognition-Oriented Emotion Tracing from Causes to Consequences in Real-World Social Scenes}, |
| author = {Li, Hao and Zhang, Jinye and Li, Bobo and Lee, Mong-Li and Hsu, Wynne and Wang, Zheng and Fei, Hao and Zhang, Min}, |
| journal = {arXiv preprint arXiv:2610.11410}, |
| year = {2026}, |
| eprint = {2610.11410}, |
| archivePrefix = {arXiv}, |
| primaryClass = {cs.AI} |
| } |
| ``` |
|
|
| ## Contact |
|
|
| sc.lihao@whu.edu.cn |
|
|
