pretty_name: SciCode-Verified
license: apache-2.0
language:
- en
annotations_creators:
- expert-generated
source_datasets:
- extended
task_categories:
- text-generation
size_categories:
- n<1K
tags:
- code
- science
- benchmark
- code-generation
- scientific-coding
- llm-evaluation
configs:
- config_name: default
data_files:
- split: test
path: data/problems_test.jsonl
SciCode-Verified
SciCode-Verified is the corrected, human-verified release of the SciCode scientific-code-generation benchmark. A problem-by-problem audit identified 263 defects in the 65-problem SciCode test split and corrected every confirmable defect. The released evaluation set contains 64 main problems and 287 scored subproblems; one original problem is excluded because its specification does not determine a unique, verifiable answer.
- Paper: SciCode-Verified: How Benchmark Defects Underestimated the Scientific-Coding Ability of Language Models
- Code, evaluation harness, and audit trail: github.com/flyingwagner/scicode-verified
- Original benchmark: SciCode
Files
| File | Purpose |
|---|---|
data/problems_test.jsonl |
Corrected prompts and problem specifications, one main problem per line |
test_data_cleaned.h5 |
Corrected frozen grading targets used by the evaluation harness |
manifest.json |
Release version, problem order, and MD5 checksums |
LICENSE |
Apache License 2.0 inherited from the upstream benchmark |
Release v2 checksums:
5c604d8dbf52642bd94e13b92c8f52eb data/problems_test.jsonl
2b41a7df40ddc23ce651ec05b8ecb6f8 test_data_cleaned.h5
Load the problem specifications
from datasets import load_dataset
dataset = load_dataset("shhu2001/SciCode-Verified", split="test")
print(dataset[0])
Download the grading targets
from huggingface_hub import hf_hub_download
h5_path = hf_hub_download(
repo_id="shhu2001/SciCode-Verified",
repo_type="dataset",
filename="test_data_cleaned.h5",
)
The full generation, grading, multi-environment evaluation, and integrity-check workflow is documented in the GitHub repository. Model outputs and evaluation logs are intentionally not included in this dataset repository.
License and attribution
SciCode-Verified is derived from SciCode and redistributed under the Apache License 2.0. Please cite both the original SciCode benchmark and SciCode-Verified.
@article{hu2026scicodeverified,
title = {{SciCode-Verified}: How Benchmark Defects Underestimated the Scientific-Coding Ability of Language Models},
author = {Hu, Sihan and Huang, Lyuhan and Deng, Youjin and Chen, Kun},
year = {2026},
eprint = {2608.04975},
archivePrefix = {arXiv},
primaryClass = {cs.SE},
url = {https://arxiv.org/abs/2608.04975}
}
@article{tian2024scicode,
title = {{SciCode}: A Research Coding Benchmark Curated by Scientists},
author = {Tian, Minyang and Gao, Luyu and Zhang, Shizhuo Dylan and Chen, Xinan and others},
year = {2024},
eprint = {2407.13168},
archivePrefix = {arXiv},
primaryClass = {cs.AI},
url = {https://arxiv.org/abs/2407.13168}
}