README.md

---
license: cc-by-4.0
language:
  - en
tags:
  - benchmark
  - reproducibility
  - computational-materials-science
  - agents
pretty_name: AutoMat
size_categories:
  - n<1K
extra_gated_prompt: >-
  AutoMat is a benchmark of reproducibility claims from computational
  materials-science papers. Access is gated to keep the leaderboard meaningful
  and to respect upstream paper licensing. Please briefly describe how you
  intend to use the dataset.
extra_gated_fields:
  Name: text
  Affiliation: text
  Intended use: text
  I agree to use this dataset solely for non-commercial research: checkbox
extra_gated_button_content: Request access
configs:
  - config_name: default
    data_files:
      - split: claims
        path: manifest.parquet
---

# AutoMat

AutoMat is a benchmark of reproducibility claims drawn from the computational
materials-science literature. Each claim packages a published scientific
statement together with the inputs, papers, and reference outputs an autonomous
agent (or human) would need to attempt a faithful reproduction.

## Layout

Each claim lives under `claims/AUTOMAT-XXXX_<author>/` with three subtrees:

| Path | Contents |
| ---- | -------- |
| `meta/provenance.json` | Authoritative metadata (claim ID, paper, DOI, author). |
| `meta/claim.md` | Human-readable claim statement and reproduction instructions. |
| `agent_view/` | What an agent is allowed to see: `claim.txt`, the paper PDF, input data, custom code. |
| `reference/` | Ground-truth reproduction: expected outputs and a reference implementation. |


## Coverage

A small number of claims are withheld from the current release at their
authors' request, because the underlying paper is still pending official
publication. These will be added in a later revision once the corresponding
papers are public. The published `manifest.parquet` lists exactly the claims
included in this release, so any consumer that drives off the manifest stays
in sync automatically.


## Using the dataset

This is a gated dataset, so you must be logged into your Hugging Face account
(and have been granted access) before downloading. Verify with:

```bash
hf auth whoami
```

If that reports you are not logged in, run:

```bash
hf auth login
```

Once you are logged in, you can download the dataset:

```python
from huggingface_hub import snapshot_download
import pandas as pd

local = snapshot_download(repo_id="jhu-clsp/AutoMat", repo_type="dataset")
manifest = pd.read_parquet(f"{local}/manifest.parquet")
print(manifest.head())
```

To fetch a single claim only:

```python
from huggingface_hub import snapshot_download
snapshot_download(
    repo_id="jhu-clsp/AutoMat",
    repo_type="dataset",
    allow_patterns=["manifest.parquet", "claims/AUTOMAT-0003*"],
)
```

The companion harness at https://github.com/JHU-CLSP/AutoMat consumes this
layout directly.

## Evaluation protocol

For each claim, an agent receives the contents of `agent_view/` and the
free-form claim text. A successful reproduction is one whose outputs match the
artifacts in `reference/expected/` (per the comparison rules described in the
harness `harness/evaluators/`). Reference implementations under
`reference/impl/` are provided for context and to debug failed runs — they are
not intended as the canonical reproduction path.

## Licensing notes

- The metadata, claim statements, and reference outputs in this dataset are
  released under CC-BY-4.0.
- The paper PDFs included under `agent_view/paper/` remain the property of
  their original publishers. Their inclusion here is for reproducibility
  research under the gated-access terms above; consult each paper's
  publisher for redistribution rights.

## Citation

```
@article{huang2026automat,
  title     = {Can Coding Agents Reproduce Findings in
                Computational Materials Science?},
  author    = {Huang, Ziyang and Cao, Yi and
                Shargh, Ali K. and Luo, Jing and
                Mei, Ruidong and Zaki, Mohd and
                Liu, Zhan and Bunstine, Wyatt and
                Jurayj, William and Goswami, Somdatta and
                McQueen, Tyrel and Shields, Michael and
                El-Awady, Jaafar and Clancy, Paulette and
                Van Durme, Benjamin and Andrews, Nicholas and 
                Walden, William and Khashabi, Daniel},
  journal = {arXiv preprint arXiv:2605.00803},
  year    = {2026},
}
```

## Changelog

- **v1.0.0** — initial release.