---
pretty_name: ARIA Repo Benchmark
dataset_info:
  features:
  - name: dataset
    dtype: string
  - name: model_name
    dtype: string
  - name: model_links
    list: 'null'
  - name: paper_title
    dtype: string
  - name: paper_date
    dtype: timestamp[ns]
  - name: paper_url
    dtype: string
  - name: code_links
    list: string
  - name: metrics
    dtype: string
  - name: table_metrics
    list: string
  - name: prompts
    dtype: string
  - name: paper_text
    dtype: string
  - name: compute_hours
    dtype: float64
  - name: num_gpus
    dtype: int64
  - name: reasoning
    dtype: string
  - name: trainable_single_gpu_8h
    dtype: string
  - name: verified
    dtype: string
  - name: modality
    dtype: string
  - name: paper_title_drop
    dtype: string
  - name: paper_date_drop
    dtype: string
  - name: code_links_drop
    dtype: string
  - name: num_gpus_drop
    dtype: int64
  - name: dataset_link
    dtype: string
  - name: time_and_compute_verification
    dtype: string
  - name: link_to_colab_notebook
    dtype: string
  - name: run_possible
    dtype: string
  - name: notes
    dtype: string
  splits:
  - name: train
    num_bytes: 3507790
    num_examples: 58
  download_size: 1553348
  dataset_size: 3507790
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
language:
  - en
size_categories:
  - n<1K
tags:
  - aria
  - benchmark
  - ml-research
  - reproducibility
task_categories:
  - text-generation
---

# ARIA Repo Benchmark

The ARIA Repo Benchmark is part of the [ARIA benchmark suite](https://github.com/AlgorithmicResearchGroup/ARIA), a collection of closed-book benchmarks probing the ML knowledge that frontier models have internalized during training. This dataset contains 58 curated research paper implementations with metadata for evaluating whether ML experiments described in papers can be reproduced.

## Dataset Summary

- **Size**: 58 entries
- **Coverage**: Computer Vision, NLP, Time Series, Graph, Bioinformatics
- **Purpose**: Evaluate AI agents on their ability to locate, understand, and reproduce ML research experiments

Each entry links a research paper to its code repository, dataset, metrics, and compute requirements, along with verification of whether the experiment is reproducible on constrained hardware.

## Dataset Structure

### Key Fields

| Field | Type | Description |
|-------|------|-------------|
| `paper_title` | string | Title of the research paper |
| `paper_url` | string | ArXiv URL |
| `paper_date` | timestamp | Publication date |
| `paper_text` | string | Full paper text |
| `dataset` | string | Dataset used in the paper |
| `dataset_link` | string | Link to the dataset |
| `model_name` | string | Model name |
| `code_links` | list[string] | GitHub repository links |
| `metrics` | string | Performance metrics reported |
| `table_metrics` | list[string] | Detailed metrics from tables |
| `prompts` | string | Evaluation prompts |
| `modality` | string | Data modality (CV, NLP, Time Series, Graph, etc.) |

### Compute & Reproducibility Fields

| Field | Type | Description |
|-------|------|-------------|
| `compute_hours` | float64 | Estimated training compute hours |
| `num_gpus` | int64 | Number of GPUs required |
| `reasoning` | string | Reasoning about compute estimates |
| `trainable_single_gpu_8h` | string | Trainable on a single GPU in 8 hours |
| `verified` | string | Verification status |
| `time_and_compute_verification` | string | Compute verification notes |
| `link_to_colab_notebook` | string | Google Colab notebook link |
| `run_possible` | string | Whether the code runs successfully |
| `notes` | string | Additional notes |

## Usage

```python
from datasets import load_dataset

ds = load_dataset("AlgorithmicResearchGroup/aria-repo-benchmark", split="train")

for entry in ds:
    print(f"{entry['paper_title']} - {entry['modality']} - Reproducible: {entry['run_possible']}")
```

## Related Resources

- [ARIA Benchmark Suite](https://github.com/AlgorithmicResearchGroup/ARIA)
- [Algorithmic Research Group - Open Source](https://algorithmicresearchgroup.com/opensource.html)

## Citation

```bibtex
@misc{aria_repo_benchmark,
    title={ARIA Repo Benchmark},
    author={Algorithmic Research Group},
    year={2024},
    publisher={Hugging Face},
    url={https://huggingface.co/datasets/AlgorithmicResearchGroup/aria-repo-benchmark}
}
```