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.
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.
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
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. 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.
Citation
@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}
}