playbook/antigravity-awesome-skills/skills/hugging-face-paper-publisher/templates/modern.md

title	authors	date	arxiv	tags	layout
TITLE	AUTHORS	DATE		machine-learning ai	modern

{{TITLE}}

{{AUTHORS}}

{{DATE}}

[arXiv](#) · [PDF](#) · [Code](#) · [Demo](#)

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Abstract

{{ABSTRACT}}

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Introduction

Modern research requires clear, accessible communication. This template provides a clean, web-friendly format inspired by Distill and modern scientific publications.

💡 **Key Insight**: Present your main contribution upfront to engage readers immediately.

Why This Matters

Explain the significance of your work in plain language. What real-world problems does it solve?

Our Approach

Summarize your methodology at a high level before diving into details.

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Background

**Definition**: Clearly define key terms and concepts early in the paper.

Provide context necessary to understand your contribution without overwhelming readers with details.

Problem Statement

Formally state the problem you're addressing.

Challenges

What makes this problem difficult?

1. Challenge 1: Description
2. Challenge 2: Description
3. Challenge 3: Description

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Method

Present your approach with clear visual aids and intuitive explanations.

[Diagram of your architecture goes here]

Figure 1: Overview of the proposed method. Caption explains the key components.

Model Architecture

Describe your model systematically:

# Pseudocode example
class YourModel:
    def __init__(self):
        self.encoder = Encoder()
        self.decoder = Decoder()

    def forward(self, x):
        z = self.encoder(x)
        output = self.decoder(z)
        return output

Training Strategy

Explain how you train the model, including:

• Objective Function: Mathematical formulation
• Optimization: Algorithm and hyperparameters
• Regularization: Techniques to prevent overfitting

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Experiments

Setup

Component	Configuration
Dataset	Name, Size, Split
Hardware	GPU Type, RAM
Framework	PyTorch 2.0, Transformers
Training Time	Hours/Days

Results

Present results clearly with tables and visualizations.

Model	Accuracy	F1 Score	Params	Speed
Baseline	85.2%	0.84	100M	100 tok/s
Ours	92.1%	0.91	120M	95 tok/s
SOTA	90.5%	0.89	300M	60 tok/s

🔍 **Observation**: Our method achieves state-of-the-art performance with fewer parameters.

Analysis

Deep dive into what the results reveal:

1. Performance: How does your method compare?
2. Efficiency: What are the computational costs?
3. Robustness: How does it perform across different scenarios?

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Ablation Study

Systematically evaluate each component's contribution.

Configuration	Score	Δ
Full Model	92.1%	-
- Component A	89.3%	-2.8%
- Component B	90.1%	-2.0%
- Component C	91.5%	-0.6%

Conclusion: All components contribute meaningfully, with Component A being most critical.

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Discussion

What We Learned

Synthesize insights from your experiments.

Limitations

⚠️ Current Limitations:

1. Performance on domain X is limited
2. Computational requirements are high
3. Requires large training datasets

Future Directions

Where should the community go next?

• Direction 1: Description
• Direction 2: Description
• Direction 3: Description

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Related Work

Compare and contrast with existing methods.

Prior Approaches

Method	Year	Key Idea	Limitation
Method A	2020	Approach 1	Issue X
Method B	2021	Approach 2	Issue Y
Method C	2023	Approach 3	Issue Z

How We Differ

Clearly articulate what's novel about your work.

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Conclusion

We presented {{TITLE}}, which achieves:

1. ✅ Main contribution 1
2. ✅ Main contribution 2
3. ✅ Main contribution 3

Our results demonstrate [key finding], opening new directions for [future work].

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Reproducibility

Code & Data

• Code: github.com/username/repo
• Models: huggingface.co/username/model
• Datasets: huggingface.co/datasets/username/dataset
• Demo: huggingface.co/spaces/username/demo

Citation

@article{yourpaper2025,
  title={{{{TITLE}}}},
  author={{{{AUTHORS}}}},
  year={2025},
  journal={arXiv preprint}
}

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Acknowledgments

Thank funding agencies, collaborators, and computing resources that made this work possible.

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Appendix

A. Additional Results

Supplementary experiments and extended results.

B. Hyperparameters

Complete training configuration:

learning_rate: 1e-4
batch_size: 32
epochs: 100
optimizer: AdamW
scheduler: cosine
warmup_steps: 1000

C. Dataset Details

Detailed information about datasets used.

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