HumanStudy-Bench
Health Warn
- License — License: MIT
- Description — Repository has a description
- Active repo — Last push 0 days ago
- Low visibility — Only 5 GitHub stars
Code Warn
- process.env — Environment variable access in co_website/app/api/contribute/upload/route.ts
Permissions Pass
- Permissions — No dangerous permissions requested
This tool provides a standardized testbed and execution engine for replaying human-subject psychological experiments with AI agents. It is designed to help researchers evaluate how well LLMs simulate human behavior in social science research.
Security Assessment
The overall risk is Low. The automated scan found no hardcoded secrets, no dangerous system permissions, and no evidence of arbitrary shell command execution. The only flagged item is an environment variable access (`process.env`) within a website API route, which is a standard and secure practice for handling configuration keys (like database URLs) server-side. The tool does not appear to request sensitive local data or make unauthorized external network requests.
Quality Assessment
The project is very new and has low community visibility, currently sitting at only 5 GitHub stars. However, it is actively maintained, with repository activity as recent as today. It uses a permissive MIT license, making it freely available for both personal and commercial use. The repository is well-organized, includes comprehensive documentation, and features a structured workflow for community contributions.
Verdict
Safe to use.
HumanStudy-Bench: Community Edition — Standardized human study replays for AI agent evaluation
HumanStudy-Bench: Community Edition
Open community-driven expansion of the HumanStudy-Bench benchmark
LLMs are increasingly used to simulate human participants in social science research, but existing evaluations conflate base model capabilities with agent design choices, making it unclear whether results reflect the model or the configuration.
Overview
HumanStudy-Bench treats participant simulation as an agent design problem and provides a standardized testbed — combining an Execution Engine that reconstructs full experimental protocols from published studies and a Benchmark with standardized evaluation metrics — for replaying human-subject experiments end-to-end with alignment evaluation at the level of scientific inference.
How to Contribute a Study
1. Fork and clone
git clone https://github.com/<your-github-id>/HumanStudy-Bench.git
cd HumanStudy-Bench
git checkout -b contrib-<yourgithubid>-013
2. Create your study folder
Add a new directory under studies/ with the required folders:
studies/<yourgithubid>_013/
├── index.json
├── source/
├── scripts/
└── README.md
See the docs below for what goes inside each folder and the exact schemas:
| # | Guide | Description |
|---|---|---|
| 1 | What Should I Submit? | Overview of contribution, required folders and files |
| 2 | How to Extract Data from a Paper | Paper hierarchy, AI extraction prompt, walkthrough example |
| 3 | How to Build Your Study Files | Schemas, code examples, and contracts for each file |
| 4 | How to Submit Your Study | Fork, verify, push, and open a PR |
3. Verify locally
bash scripts/verify_study.sh <yourgithubid>_013
4. Commit and push
git add studies/<yourgithubid>_013/
git commit -m "Add study: <Your Study Title>"
git push origin contrib-<yourgithubid>-013
5. Open a Pull Request
Open a PR on GitHub targeting the main branch. Maintainers assign final study_XXX numbering by merge order. CI runs validation automatically; confirmation is by human review.
You can also submit a study via web upload at hs-bench.clawder.ai/contribute.
Existing Studies
The 12 foundational studies (cognition, strategic interaction, social psychology) serve as reference examples. Browse them on the website or locally under studies/.
Citation
If you use HumanStudy-Bench, please cite:
@misc{liu2026humanstudybenchaiagentdesign,
title={HumanStudy-Bench: Towards AI Agent Design for Participant Simulation},
author={Xuan Liu and Haoyang Shang and Zizhang Liu and Xinyan Liu and Yunze Xiao and Yiwen Tu and Haojian Jin},
year={2026},
eprint={2602.00685},
archivePrefix={arXiv},
primaryClass={cs.AI},
url={https://arxiv.org/abs/2602.00685},
}
License
MIT License. See LICENSE for details.
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