academic-plotting

# Academic Plotting for ML Papers Generate publication-quality figures for ML/AI conference papers. Two distinct workflows: 1. **Diagram figures** (architecture, system design, workflows, pipelines) — AI image generation via Gemini 2. **Data figures** (line charts, bar charts, scatter plots, heatmaps, ablations) — matplotlib/seaborn ## When to Use Which Workflow | Figure Type | Tool | Why | |-------------|------|-----| | Architecture / system diagram | Gemini (Workflow 1) | Complex spatial layouts with boxes, arrows, labels | | Workflow / pipeline / lifecycle | Gemini (Workflow 1) | Multi-step processes with connections | | Bar chart, line plot, scatter | matplotlib (Workflow 2) | Precise numerical data, reproducible | | Heatmap, confusion matrix | matplotlib/seaborn (Workflow 2) | Structured grid data | | Ablation table as chart | matplotlib (Workflow 2) | Grouped bars or line comparisons | | Pie / donut chart | matplotlib (Workflow 2) | Proportional data (use sparingly in ML papers) | | Training curves | matplotlib (Workflow 2) | Loss/accuracy over steps/epochs | **Rule of thumb**: If the figure has numerical axes, use matplotlib. If the figure has boxes and arrows, use Gemini. --- ## Step 0: Context Analysis & Extraction The user will typically provide one of these inputs — not a ready-made specification: | Input Type | Example | What to Extract | |-----------|---------|-----------------| | Full paper / section draft | "Here's our method section..." | System compo...