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16 changes: 16 additions & 0 deletions .gitignore
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.env
.env.local

# Local pipeline settings may contain API keys
config/settings.yaml
config/settings.yml
config/settings.json

# Generated pipeline outputs and simulation runs
generation_pipeline/outputs/*
!generation_pipeline/outputs/.gitkeep
generation_pipeline/old_outputs/
generation_pipeline/**/__pycache__/
runs/
runs_*/
experiments/
experiments_*/
.cache/stage5/

# OS
Thumbs.db

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148 changes: 101 additions & 47 deletions README.md
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@@ -1,82 +1,136 @@
<div align="center">
<img src="docs/img/new-HS-bench_logo.png" alt="HumanStudy-Bench Logo" width="300">

<h1>HumanStudy-Bench: Community Edition</h1>
<p><em>Open community-driven expansion of the HumanStudy-Bench benchmark</em></p>

<a href="https://arxiv.org/abs/2602.00685"><img src="https://img.shields.io/badge/arXiv-Paper-b31b1b?logo=arxiv&logoColor=white" alt="Read the Paper" /></a>
<a href="https://opensource.org/licenses/MIT"><img src="https://img.shields.io/badge/License-MIT-yellow.svg" alt="License: MIT" /></a>
<a href="https://github.com/HumanStudy-Hub/HumanStudy-Bench/pulls"><img src="https://img.shields.io/badge/contributions-welcome-brightgreen.svg?logo=opensourceinitiative&logoColor=white" alt="Contributions Welcome" /></a>
<a href="https://www.hs-bench.clawder.ai"><img src="https://img.shields.io/badge/docs-website-blue?logo=readthedocs&logoColor=white" alt="Docs" /></a>
<h1>HumanStudy-Bench</h1>
<p><em>A benchmark for evaluating AI agents through runnable human studies</em></p>

<a href="https://arxiv.org/abs/2602.00685"><img src="https://img.shields.io/badge/arXiv-Paper-b31b1b?logo=arxiv&logoColor=white" alt="Read the Paper"></a>
<a href="https://opensource.org/licenses/MIT"><img src="https://img.shields.io/badge/License-MIT-yellow.svg" alt="MIT License"></a>
<a href="https://github.com/HumanStudy-Hub/HumanStudy-Bench/pulls"><img src="https://img.shields.io/badge/contributions-welcome-brightgreen.svg" alt="Contributions welcome"></a>
</div>

---

> 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.
HumanStudy-Bench is a standardized testbed for replaying human-subject
experiments with AI agents. It combines:

- an execution engine that reconstructs published experimental protocols;
- a growing collection of runnable social-science studies; and
- evaluation at the level of behavior, effects, and scientific findings.

The 12 foundational studies cover cognition, strategic interaction, and social
psychology. Community studies live alongside them under [`studies/`](studies/).

## <img src="https://api.iconify.design/lucide/book-open.svg?color=%230891b2" width="20" height="20" /> Overview
## Add a study

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.
There are two contribution paths.

## <img src="https://api.iconify.design/lucide/git-pull-request.svg?color=%230891b2" width="20" height="20" /> How to Contribute a Study
| Path | Best for | Current status |
|---|---|---|
| **Build Study** | Researchers starting from a paper PDF and optional open materials | **Private beta.** HumanStudy-Hub runs this repository's pipeline, persists review decisions, and produces a ZIP or GitHub pull request. |
| **Direct pull request** | Contributors who want full control over study files | **Available now.** Build the folder locally, run validation, and open a PR. |

The intended Build Study workflow is:

```text
paper PDF + optional OSF/open materials
-> evidence and study extraction
-> researcher review at uncertain decisions
-> runnable HumanStudy-Bench folder
-> download ZIP and/or save to GitHub
```

### 1. Fork and clone
Build Study is currently operated through the private HumanStudy-Hub web
application. The direct contribution documentation remains supported for
researchers who prefer to author or inspect every file themselves. The manual
entry point is the [direct pull request guide](docs/submit_study.md).

### Direct pull request

1. Fork this repository and clone your fork.

```bash
git clone https://github.com/<your-github-id>/HumanStudy-Bench.git
cd HumanStudy-Bench
git checkout -b contrib-<yourgithubid>-013
git checkout -b contrib-<your-github-id>-<study-name>
```

### 2. Create your study folder

Add a new directory under `studies/` with the required folders:
2. Add a study folder.

```text
studies/<contributor-id>_<study-id>/
index.json
README.md
source/
scripts/
```
studies/<yourgithubid>_013/
├── index.json
├── source/
├── scripts/
└── README.md

3. Validate it locally.

```bash
bash scripts/verify_study.sh <contributor-id>_<study-id>
```

See the docs below for what goes inside each folder and the exact schemas:
4. Commit the study, push your branch, and open a pull request targeting
`main`. CI validates the package and contributor attribution; maintainers
complete the human review and assign the final study ID.

| # | Guide | Description |
|---|-------|-------------|
| 1 | [What Should I Submit?](https://www.hs-bench.clawder.ai/docs/what_to_submit) | Overview of contribution, required folders and files |
| 2 | [How to Extract Data from a Paper](https://www.hs-bench.clawder.ai/docs/extract_from_paper) | Paper hierarchy, AI extraction prompt, walkthrough example |
| 3 | [How to Build Your Study Files](https://www.hs-bench.clawder.ai/docs/build_study_files) | Schemas, code examples, and contracts for each file |
| 4 | [How to Submit Your Study](https://www.hs-bench.clawder.ai/docs/submit_study) | Fork, verify, push, and open a PR |
Contributor guides:

### 3. Verify locally
- [What should I submit?](docs/what_to_submit.md)
- [How to extract data from a paper](docs/extract_from_paper.md)
- [How to build study files](docs/build_study_files.md)
- [How to submit a study](docs/submit_study.md)

```bash
bash scripts/verify_study.sh <yourgithubid>_013
```
## Generation pipeline

### 4. Commit and push
This repository includes a human-in-the-loop pipeline for drafting a study
folder from a paper PDF and optional OSF or supplementary sources.

```bash
git add studies/<yourgithubid>_013/
git commit -m "Add study: <Your Study Title>"
git push origin contrib-<yourgithubid>-013
```
1. Inventory empirical units and comparison groups.
2. Extract findings, effects, samples, and statistics.
3. Assemble and audit participant-facing materials.
4. Write a package under `studies/<study_id>/`.
5. Run a simulation only after the package passes readiness review.

### 5. Open a Pull Request
Install the generation dependencies:

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.
```bash
python -m pip install -e ".[llm,pdf]"
cp config/settings.example.yaml config/settings.yaml
```

You can also submit a study via **web upload** at [hs-bench.clawder.ai/contribute](https://www.hs-bench.clawder.ai/contribute).
Do not commit `config/settings.yaml`.

## <img src="https://api.iconify.design/lucide/flask-conical.svg?color=%230891b2" width="20" height="20" /> Existing Studies
Run a stage with:

The 12 foundational studies (cognition, strategic interaction, social psychology) serve as reference examples. Browse them on the [website](https://www.hs-bench.clawder.ai/contribute#studies) or locally under `studies/`.
```bash
python generation_pipeline/run.py \
--settings config/settings.yaml \
--stage 1 \
--pdf /absolute/path/to/paper.pdf \
--provider openai \
--model gpt-5-mini
```

## <img src="https://api.iconify.design/lucide/quote.svg?color=%230891b2" width="20" height="20" /> Citation
Generated evidence and review artifacts are written under
`generation_pipeline/outputs/<paper_id>/`. Pipeline-generated packages remain
blocked from simulation until `source/audit.json` marks them ready. The pipeline
entry point is [`generation_pipeline/run.py`](generation_pipeline/run.py); run
it with `--help` for the full stage and review options.

## Repository structure

```text
studies/ Runnable benchmark studies
src/ Execution and evaluation code
generation_pipeline/ PDF/material extraction and package generation
scripts/ Study validation and index utilities
docs/ Manual contribution guides
tests/ Pipeline and benchmark tests
```

If you use HumanStudy-Bench, please cite:
## Citation

```bibtex
@misc{liu2026humanstudybenchaiagentdesign,
Expand All @@ -90,6 +144,6 @@ If you use HumanStudy-Bench, please cite:
}
```

## <img src="https://api.iconify.design/lucide/scale.svg?color=%230891b2" width="20" height="20" /> License
## License

MIT License. See [LICENSE](LICENSE) for details.
MIT License. See [LICENSE](LICENSE).
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