World-Representation-Lab/World-Embedding-Solid-Retrieval
World Embedding Solid Retrieval World Embedding Solid Retrieval is the solid mechanics retrieval split of the World Embedding Benchmark. It contains 2,700 simulation videos from 27 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-Solid-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-Solid-Retrieval.
World Embedding Solid Retrieval
World Embedding Solid Retrieval is the solid mechanics retrieval split of the World Embedding Benchmark. It contains 2,700 simulation videos from 27 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-Solid-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}
}