academic-plotting

これはレビュー時に保存された抜粋です。完全で最新の内容は外部ソースを確認してください。

---
name: academic-plotting
description: Generates publication-quality figures for ML papers from research context. Given a paper section or description, extracts system components and relationships to generate architecture diagrams via Gemini. Given experiment results or data, auto-selects chart type and generates data-driven figures via matplotlib/seaborn. Use when creating any figure for a conference paper.
version: 1.0.0
author: Orchestra Research
license: MIT
tags: [Academic Writing, Visualization, Matplotlib, Seaborn, Plotting, Figures, Diagrams, NeurIPS, ICML, ICLR, LaTeX]
dependencies: [matplotlib>=3.8.0, seaborn>=0.13.0, numpy, google-genai>=1.0.0]
---

# 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 components, their relationships, data flow |
| Description paragraph | "Our system has three layers that..." | Key entities, hierarchy, connections |
| Raw results / data table | "MMLU: 85.2, HumanEval: 72.1..." | Metrics, methods, comparison structure |
| CSV / JSON data | Experiment log files | Variables, trends, grouping dimensions |
| Vague request | "Make a figure for the overview" | Read surrounding paper context to infer content |

### Extraction Workflow

**For diagrams** (research context → architecture figure):

1. **Read the provided context** — paper section, abstract, or description paragraph
2. **Identify visual entities** — What are the main components/modules/stages?
   - Look for: nouns that represent system parts, named modules, layers, stages
   - Count them: if >8 top-level entities, consider grouping into sections
3. **Identify relationships** — How do components connect?
   - Look for: verbs describing data flow ("sends to", "queries", "feeds into")
   - Classify: data flow (solid arrow), control flow (gray), error path (dashed red)
4. **Determine layout pattern**:
   - Sequential pipeline → left-to-right flow
   - Layered architecture → horizontal bands stacked vertically
   - Hub-and-spoke → central node with radiating connections
   - Hierarchical → top-down tree
5. **Assign colors** — One accent color per logical group/layer
6. **Write every label exactly** — Extract exact terminology from the paper text

**For data charts** (results → figure):

1. **Read the provided data** — table, paragraph with numbers, CSV, or JSON
2. **Identify dimensions**:
   - What is being compared? (methods, models, configurations) → categorical axis
   - What is the metric? (accuracy, loss, latency, F1) → value axis
   - Is there a time/step dimension? → line plot
   - Are there multiple metrics? →