320 lines
6.3 KiB
Markdown
320 lines
6.3 KiB
Markdown
---
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title: {{TITLE}}
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authors: {{AUTHORS}}
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date: {{DATE}}
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arxiv:
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tags: [machine-learning, ai]
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layout: modern
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---
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<div class="header">
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# {{TITLE}}
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<div class="authors">
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{{AUTHORS}}
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</div>
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<div class="date">
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{{DATE}}
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</div>
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<div class="links">
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[arXiv](#) · [PDF](#) · [Code](#) · [Demo](#)
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</div>
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</div>
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---
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## Abstract
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<div class="abstract">
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{{ABSTRACT}}
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</div>
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---
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## Introduction
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Modern research requires clear, accessible communication. This template provides a clean, web-friendly format inspired by Distill and modern scientific publications.
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<div class="key-insight">
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💡 **Key Insight**: Present your main contribution upfront to engage readers immediately.
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</div>
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### Why This Matters
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Explain the significance of your work in plain language. What real-world problems does it solve?
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### Our Approach
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Summarize your methodology at a high level before diving into details.
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---
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## Background
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<div class="definition">
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**Definition**: Clearly define key terms and concepts early in the paper.
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</div>
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Provide context necessary to understand your contribution without overwhelming readers with details.
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### Problem Statement
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Formally state the problem you're addressing.
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### Challenges
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What makes this problem difficult?
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1. **Challenge 1**: Description
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2. **Challenge 2**: Description
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3. **Challenge 3**: Description
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---
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## Method
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Present your approach with clear visual aids and intuitive explanations.
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<div class="figure">
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```
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[Diagram of your architecture goes here]
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```
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**Figure 1**: Overview of the proposed method. Caption explains the key components.
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</div>
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### Model Architecture
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Describe your model systematically:
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```python
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# Pseudocode example
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class YourModel:
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def __init__(self):
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self.encoder = Encoder()
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self.decoder = Decoder()
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def forward(self, x):
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z = self.encoder(x)
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output = self.decoder(z)
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return output
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```
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### Training Strategy
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Explain how you train the model, including:
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- **Objective Function**: Mathematical formulation
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- **Optimization**: Algorithm and hyperparameters
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- **Regularization**: Techniques to prevent overfitting
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---
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## Experiments
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### Setup
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<div class="experiment-details">
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| Component | Configuration |
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|-----------|--------------|
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| **Dataset** | Name, Size, Split |
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| **Hardware** | GPU Type, RAM |
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| **Framework** | PyTorch 2.0, Transformers |
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| **Training Time** | Hours/Days |
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</div>
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### Results
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Present results clearly with tables and visualizations.
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<div class="results-table">
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| Model | Accuracy | F1 Score | Params | Speed |
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|-------|----------|----------|--------|-------|
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| Baseline | 85.2% | 0.84 | 100M | 100 tok/s |
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| **Ours** | **92.1%** | **0.91** | 120M | 95 tok/s |
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| SOTA | 90.5% | 0.89 | 300M | 60 tok/s |
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</div>
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<div class="insight">
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🔍 **Observation**: Our method achieves state-of-the-art performance with fewer parameters.
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</div>
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### Analysis
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Deep dive into what the results reveal:
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1. **Performance**: How does your method compare?
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2. **Efficiency**: What are the computational costs?
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3. **Robustness**: How does it perform across different scenarios?
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---
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## Ablation Study
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Systematically evaluate each component's contribution.
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<div class="ablation-results">
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| Configuration | Score | Δ |
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|---------------|-------|---|
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| Full Model | 92.1% | - |
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| - Component A | 89.3% | -2.8% |
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| - Component B | 90.1% | -2.0% |
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| - Component C | 91.5% | -0.6% |
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</div>
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**Conclusion**: All components contribute meaningfully, with Component A being most critical.
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---
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## Discussion
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### What We Learned
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Synthesize insights from your experiments.
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### Limitations
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<div class="limitations">
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⚠️ **Current Limitations**:
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1. Performance on domain X is limited
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2. Computational requirements are high
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3. Requires large training datasets
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</div>
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### Future Directions
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Where should the community go next?
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- **Direction 1**: Description
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- **Direction 2**: Description
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- **Direction 3**: Description
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---
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## Related Work
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Compare and contrast with existing methods.
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### Prior Approaches
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| Method | Year | Key Idea | Limitation |
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|--------|------|----------|------------|
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| Method A | 2020 | Approach 1 | Issue X |
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| Method B | 2021 | Approach 2 | Issue Y |
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| Method C | 2023 | Approach 3 | Issue Z |
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### How We Differ
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Clearly articulate what's novel about your work.
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---
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## Conclusion
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<div class="conclusion">
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We presented **{{TITLE}}**, which achieves:
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1. ✅ **Main contribution 1**
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2. ✅ **Main contribution 2**
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3. ✅ **Main contribution 3**
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Our results demonstrate [key finding], opening new directions for [future work].
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</div>
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---
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## Reproducibility
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<div class="reproducibility">
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### Code & Data
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- **Code**: [github.com/username/repo](#)
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- **Models**: [huggingface.co/username/model](#)
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- **Datasets**: [huggingface.co/datasets/username/dataset](#)
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- **Demo**: [huggingface.co/spaces/username/demo](#)
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### Citation
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```bibtex
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@article{yourpaper2025,
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title={{{{TITLE}}}},
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author={{{{AUTHORS}}}},
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year={2025},
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journal={arXiv preprint}
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}
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```
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</div>
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---
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## Acknowledgments
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Thank funding agencies, collaborators, and computing resources that made this work possible.
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---
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<div class="appendix">
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## Appendix
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### A. Additional Results
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Supplementary experiments and extended results.
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### B. Hyperparameters
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Complete training configuration:
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```yaml
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learning_rate: 1e-4
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batch_size: 32
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epochs: 100
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optimizer: AdamW
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scheduler: cosine
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warmup_steps: 1000
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```
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### C. Dataset Details
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Detailed information about datasets used.
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</div>
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---
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<style>
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.header { text-align: center; margin-bottom: 2em; }
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.authors { font-size: 1.2em; margin: 0.5em 0; }
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.date { color: #666; margin: 0.5em 0; }
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.links { margin-top: 1em; }
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.abstract { background: #f5f5f5; padding: 1.5em; border-radius: 8px; margin: 1em 0; }
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.key-insight, .insight { background: #e8f4f8; border-left: 4px solid #2196F3; padding: 1em; margin: 1em 0; }
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.definition { background: #fff3e0; border-left: 4px solid #ff9800; padding: 1em; margin: 1em 0; }
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.limitations { background: #ffebee; border-left: 4px solid #f44336; padding: 1em; margin: 1em 0; }
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.conclusion { background: #e8f5e9; border-left: 4px solid #4caf50; padding: 1.5em; margin: 1em 0; }
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.figure { text-align: center; margin: 2em 0; }
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.experiment-details, .results-table, .ablation-results { margin: 1em 0; }
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.reproducibility { background: #f5f5f5; padding: 1.5em; border-radius: 8px; margin: 2em 0; }
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</style>
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