SciEval ToolKit

A unified evaluation toolkit and leaderboard for rigorously assessing the scientific intelligence of large language and vision–language models across the full research workflow.

[![Tutorial](https://img.shields.io/badge/Tutorial-SciEval-b8dcff?style=for-the-badge&logo=google-chrome&logoColor=white)](https://scievalkit-docs.readthedocs.io/en/latest) [![Leaderboard](https://img.shields.io/badge/LEADERBOARD-Scieval-f6e58d?style=for-the-badge&logo=huggingface)](https://opencompass.org.cn/Intern-Discovery-Eval/rank) [![Report](https://img.shields.io/badge/REPORT-Technical-f4c2d7?style=for-the-badge&logo=arxiv)](https://arxiv.org/abs/2512.22334) [![GitHub](https://img.shields.io/badge/GitHub-Repository-c7b9e2?style=for-the-badge&logo=github&logoColor=white)](https://github.com/InternScience/SciEvalKit)

Welcome to the official repository of SciEval!

Why SciEval? **SciEval** is an open‑source evaluation framework and leaderboard aimed at measuring the **scientific intelligence** of large language and vision–language models. Although modern frontier models often achieve *~90* on general‑purpose benchmarks, their performance drops sharply on rigorous, domain‑specific scientific tasks—revealing a persistent **general‑versus‑scientific gap** that motivates the need for SciEval. Its design is shaped by following core ideas: - **Beyond general‑

Agents

SciEvalKit