World-Representation-Lab/World-Embedding-Regression
World Embedding Regression World Embedding Regression is the physical-property regression dataset from the World Embedding Benchmark. It contains 5,000 simulation videos across 10 physics families, with 500 examples in each dataset configuration. Usage from datasets import load_dataset dataset = load_dataset( "World-Representation-Lab/World-Embedding-Regression", "pendulum", split="test", ) Fields family: physics family. id: unique… See the full description on the dataset page: https://huggingface.co/datasets/World-Representation-Lab/World-Embedding-Regression.
World Embedding Regression
World Embedding Regression is the physical-property regression dataset from the World Embedding Benchmark. It contains 5,000 simulation videos across 10 physics families, with 500 examples in each dataset configuration.
Usage
from datasets import load_dataset
dataset = load_dataset(
"World-Representation-Lab/World-Embedding-Regression",
"pendulum",
split="test",
)Fields
family: physics family.id: unique example identifier.video: simulation video.regression_attribute: physical quantity to predict.regression_value: continuous target value.
The fixed test and scaling splits used by the benchmark are distributed with the evaluation code.
Citation
@misc{liu2026worldembeddingbenchmark,
title = {World Embedding Benchmark},
author = {Yiqi Liu and Ruifeng Yuan and Yang Wang and Long Li and Fengyu Cai and Hou Pong Chan and Jialin Yu and Hao Zhang and Chenghua Lin and Chenghao Xiao},
year = {2026},
eprint = {2610.03632},
archivePrefix = {arXiv},
primaryClass = {cs.CV},
url = {https://arxiv.org/abs/2610.03632}
}