---
name: paper-parsing
description: Use when analyzing ML/DL papers to ensure comprehensive extraction of all relevant information
---

# Paper Parsing Methodology

## Overview

Systematic approach to parsing ML/DL papers for replication. Emphasizes **completeness** and **openness** to avoid missing critical details.

**Announce at start:** "I'm using the paper-parsing skill to ensure comprehensive paper analysis."

## Core Philosophy

1. **Completeness over speed**: Better to extract too much than miss something
2. **Open-ended discovery**: Papers contain unique insights; don't force into templates
3. **Cross-reference**: Information appears in multiple places; cross-check
4. **Explicit uncertainty**: Mark unclear items rather than guessing

## Paper Sections Checklist

### Abstract
- [ ] Core contribution identified
- [ ] Key results/numbers extracted
- [ ] Problem domain understood

### Introduction
- [ ] Problem motivation clear
- [ ] Gap in existing work identified
- [ ] Proposed solution summarized
- [ ] Claimed contributions listed

### Related Work
- [ ] Key prior methods identified
- [ ] Differences from this work noted
- [ ] Potential baselines for comparison

### Method / Approach
- [ ] Architecture fully described
- [ ] All components identified
- [ ] Mathematical formulation complete
- [ ] Training procedure detailed
- [ ] Loss functions specified
- [ ] Hyperparameters listed

### Experiments
- [ ] Datasets listed with sizes
- [ ] Evaluation metrics defined
- [ ] Baseline comparisons noted
- [ ] Ablation studies cataloged
- [ ] Key numerical results extracted

### Appendix / Supplementary
- [ ] Additional implementation details
- [ ] Extended results
- [ ] Proofs or derivations
- [ ] Code references

## Information Extraction Patterns

### Architecture Details

Look for:
- Layer types and configurations
- Activation functions
- Normalization methods
- Attention mechanisms
- Skip connections
- Input/output dimensions

Common locations:
- Method section figures
- Architecture diagrams
- Table of hyperparameters
- Appendix implementation details

### Training Configuration

| Parameter | Typical Locations |
|-----------|-------------------|
| Learning rate | Experiments, Appendix |
| Batch size | Experiments, Appendix |
| Optimizer | Method, Appendix |
| Epochs | Experiments |
| Hardware | Experiments, Appendix |
| Training time | Experiments |

### Numerical Results

Extract from:
- Main results tables
- Comparison figures
- Ablation tables
- Training curves (approximate values)

Format as:
| Metric | Dataset | Value | Conditions |
|--------|---------|-------|------------|
| Accuracy | CIFAR-10 | 95.2% | ResNet-50 backbone |

## Common Omissions to Watch For

1. **Initialization**: Often in appendix or not mentioned
2. **Data augmentation**: May be standard but unspecified
3. **Early stopping criteria**: Often implied
4. **Evaluation protocol**: Train/val/test split details
5. **Random seeds**: Reproducibility details
6. **Software versions**: PyTorch, CUDA versions

## Quality Verification

Before completing analysis:

1. **Coverage check**: Every section reviewed?
2. **Consistency check**: Numbers match across sections?
3. **Completeness check**: Could someone implement from this?
4. **Ambiguity check**: Unclear items marked?

## Output Quality Markers

Good analysis:
- Specific numbers, not "good performance"
- Exact layer configs, not "standard ResNet"
- Explicit uncertainty markers
- Cross-references between sections

Poor analysis:
- Vague descriptions
- Missing hyperparameters
- No numerical targets
- Assumptions without noting them

## Red Flags

If you notice:
- "Implementation details in code" → Check GitHub link
- "Standard settings" → Look up the standard
- "Following [citation]" → May need to read that paper
- Inconsistent numbers → Note the discrepancy
