# {{PAPER_TITLE}}

Implementation of [{{PAPER_TITLE}}](https://arxiv.org/abs/{{ARXIV_ID}}) ({{AUTHORS}}, {{YEAR}}).

## What this implements

{{ONE_PARAGRAPH_CONTRIBUTION_STATEMENT}}

## Quick start

```bash
pip install -r requirements.txt
```

```python
from src.model import {{MODEL_CLASS}}, ModelConfig

config = ModelConfig()
model = {{MODEL_CLASS}}(config)

# Example forward pass
import torch
x = torch.randn({{EXAMPLE_INPUT_SHAPE}})
output = model(x)
print(output.shape)  # {{EXPECTED_OUTPUT_SHAPE}}
```

## File structure

```
{{PAPER_SLUG}}/
├── README.md                 # This file
├── REPRODUCTION_NOTES.md     # Ambiguity audit — what's specified vs. assumed
├── requirements.txt          # Dependencies
├── src/
│   ├── model.py              # {{MODEL_DESCRIPTION}}
│   ├── loss.py               # {{LOSS_DESCRIPTION}}
│   ├── data.py               # {{DATA_DESCRIPTION}}
│   ├── train.py              # {{TRAIN_DESCRIPTION}}
│   ├── evaluate.py           # {{EVAL_DESCRIPTION}}
│   └── utils.py              # {{UTILS_DESCRIPTION}}
├── configs/
│   └── base.yaml             # All hyperparameters — each cited or flagged
└── notebooks/
    └── walkthrough.ipynb     # Paper sections → code → sanity checks
```

## Important: Read REPRODUCTION_NOTES.md

This implementation flags every choice that the paper does not specify.
Before using this code for research, read [REPRODUCTION_NOTES.md](REPRODUCTION_NOTES.md)
to understand which implementation details are from the paper and which are our choices.

## Citation

```bibtex
{{BIBTEX_ENTRY}}
```

---

*Generated by [paper2code](https://github.com/PrathamLearnsToCode/paper2code) — citation-anchored paper implementation.*
