World-Representation-Lab/World-Embedding-Fluid-Retrieval
World Embedding Fluid Retrieval World Embedding Fluid Retrieval is the fluid mechanics retrieval split of the World Embedding Benchmark. It contains 700 simulation videos from 7 physics families. The family shards are loaded together as the default configuration. Usage from datasets import load_dataset dataset = load_dataset( "World-Representation-Lab/World-Embedding-Fluid-Retrieval", split="test", ) Fields query_id: unique text-query… See the full description on the dataset page: https://huggingface.co/datasets/World-Representation-Lab/World-Embedding-Fluid-Retrieval.
World Embedding Fluid Retrieval
World Embedding Fluid Retrieval is the fluid mechanics retrieval split of the World Embedding Benchmark. It contains 700 simulation videos from 7 physics families. The family shards are loaded together as the default configuration.
Usage
from datasets import load_dataset
dataset = load_dataset(
"World-Representation-Lab/World-Embedding-Fluid-Retrieval",
split="test",
)Fields
query_id: unique text-query identifier.case_id: simulation case identifier.raw_text: structured physical description.parsed_text: natural-language retrieval caption.video: simulation video.
The benchmark uses parsed_text for text-video retrieval.
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}
}