Academic Plotting for ML Papers

Generate publication-quality figures for ML/AI conference papers. Two distinct workflows:

Diagram figures (architecture, system design, workflows, pipelines) — AI image generation via Gemini
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