--- 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? → multi-panel or grouped bars 3. **Choose chart type** automatically using this priority: - Has a step/time axis → **line plot** - Comparing N methods on M benchmarks → **grouped bar chart** - Single ranking → **horizontal bar** (leaderboard) - Correlation between two continuous variables → **scatter plot** - Square matrix of values → **heatmap** - Proportional breakdown → **stacked bar** (avoid pie charts) 4. **Determine figure sizing** — Single column vs full width based on data density 5. **Highlight "our method"** — Identify which entry is the paper's contribution and give it a distinct color

### Auto-Detection Examples

**Context → Diagram**: "Our system has a Planner, Executor, and Verifier. Planner sends plans to Executor, Executor returns results to Verifier, Verifier feeds back to Planner on failure." → 3 entities, cycle layout, dashed feedback arrow → **Workflow 1 (Gemini)**

**Data → Chart**: "GPT-4: MMLU 86.4, HumanEval 67.0. Ours: 88.1, 71.2. Llama-3: 79.3, 62.1." → 3 methods × 2 benchmarks → **Workflow 2 (grouped bar)**, highlight "Ours" in coral

---

## Workflow 1: Architecture & System Diagrams (AI Image Generation)

Use Gemini 3 Pro Image Preview to generate diagrams. **Choose a visual style first** — this is the single biggest factor in whether the figure looks professional or generic.

### Visual Styles

Pick one style per paper (all figures should be consistent):

#### Style A: "Sketch / 简笔画" (Hand-Drawn)

Warm, approachable, memorable. Ideal for overview figures and system introductions. Looks like a whiteboard sketch refined by a designer.

``` VISUAL STYLE — HAND-DRAWN SKETCH: - Slightly irregular, hand-drawn line quality — lines wobble gently, not perfectly straight - Rounded, soft shapes with visible pen strokes (like drawn with a thick felt-tip marker) - Warm off-white background (#FAFAF7), NOT pure white - Fill colors are soft watercolor-like washes: muted blue (#D6E4F0), soft peach (#F5DEB3), light sage (#D4E6D4), pale lavender (#E6DFF0) - Borders are dark charcoal (#2C2C2C) with 2-3px line weight, slightly uneven - Arrows are hand-drawn with slight curves, ending in simple open arrowheads (not filled triangles) - Text uses a rounded sans-serif font (like Comic Neue or Architects Daughter feel) - Small doodle-style icons inside boxes: a tiny gear ⚙ for processing, a lightbulb 💡 for ideas, a magnifying glass 🔍 for search — rendered as simple line drawings, NOT emoji - Overall feel: a carefully drawn whiteboard diagram, clean but with personality - NO clip art, NO stock icons, NO photorealistic elements ```

#### Style B: "Modern Minimal" (Clean & Bold)

Confident, authoritative. Best for method figures where precision matters.

``` VISUAL STYLE — MODERN MINIMAL: - Ultra-clean geometric shapes with crisp edges - Bold color blocks as backgrounds for sections — NOT just accent bars, but full section fills using desaturated tones: slate blue (#E8EDF2), warm sand (#F5F0E8), cool mint (#E8F2EE) - Component boxes have ROUNDED CORNERS (12px radius), NO visible border — they float on the section background using subtle shadow (1px, 4px blur, rgba(0,0,0,0.06)) - ONE accent color per section used sparingly on key elements: Deep blue (#2563EB), Emerald (#059669), Amber (#D97706), Rose (#E11D48) - Arrows are thin (1.5px), dark gray (#6B7280), with small filled circle at source and clean arrowhead at target — NOT thick colored arrows - Typography: Inter or system sans-serif, title 600 weight, body 400 weight - Labels INSIDE boxes, not beside them - Generous whitespace — at least 24px between elements - NO decorative elements, NO icons — let the structure speak ```

#### Style C: "Illustrated Technical" (Icon-Rich)

Engaging, explanatory. Good for tutorial-style papers and figures that need to be self-explanatory.

``` VISUAL STYLE — ILLUSTRATED TECHNICAL: - Each major component has a small MEANINGFUL ICON drawn in a consistent line-art style (single color, 2px stroke, ~24x24px): brain icon for reasoning, database cylinder for storage, arrow-loop for iteration, network nodes for communication - Components sit inside soft rounded rectangles with a LEFT COLOR STRIP (4px wide) - Background is pure white, but each logical group has a very faint colored region behind it (#F8FAFC for blue group, #FFF8F0 for orange group) - Connections use CURVED bezier paths (not straight lines), colored by SOURCE component - Key data flows are THICKER (3px) than secondary flows (1px, dashed) - Small annotation badges on arrows: "×N" for repeated operations, "optional" in italics - Title labels are ABOVE each section in small caps, letter-spaced - Overall: like a well-designed API documentation diagram ```

#### Style D: "Accent Bar" (Classic Academic)

The default academic style. Safe for any venue, works well in grayscale.

``` VISUAL STYLE — CLASSIC ACCENT BAR: - Horizontal section bands stacked vertically, pale gray (#F7F7F5) fill - Thick colored LEFT ACCENT BAR (8px) distinguishes each section - Content boxes: white fill, thin #DDD border, 4px rounded corners - Section palette: Blue #4A90D9, Teal #5BA58B, Amber #D4A252, Slate #7B8794 - Sans-serif typography (Helvetica/Arial), bold titles, regular body - Colored arrows match their SOURCE section - Clean, flat, zero decoration ```

### Curated Color Palettes

**"Ocean Dusk"** (professional, calming — default recommendation): `#264653` deep teal, `#2A9D8F` teal, `#E9C46A` gold, `#F4A261` sandy orange, `#E76F51` burnt coral

**"Ink & Wash"** (for 简笔画 style): `#2C2C2C` charcoal ink, `#D6E4F0` washed blue, `#F5DEB3` washed wheat, `#D4E6D4` washed sage, `#E6DFF0` washed lavender

**"Nord"** (for modern minimal): `#2E3440` polar night, `#5E81AC` frost blue, `#A3BE8C` aurora green, `#EBCB8B` aurora yellow, `#BF616A` aurora red

**"Okabe-Ito"** (universal colorblind-safe, required for data charts): `#E69F00` orange, `#56B4E9` sky blue, `#009E73` green, `#F0E442` yellow, `#0072B2` blue, `#D55E00` vermillion, `#CC79A7` pink

### Checklist

- [ ] **Extract from context**: Read paper/description, identify entities and relationships - [ ] **Choose visual style** (A/B/C/D) — match the paper's tone and venue - [ ] **Choose color palette** — or use one consistent with existing paper figures - [ ] Obtain Gemini API key (`GEMINI_API_KEY` env var) - [ ] Write a detailed prompt: style block + layout + connections + constraints - [ ] Generate script at `figures/gen_fig_<name>.py`, run for 3 attempts - [ ] Review, select best, save as `figures/fig_<name>.png`

### Prompt Structure (6 Sections)

Every Gemini prompt must include these sections in order:

``` 1. FRAMING (5 lines): "Create a [STYLE_NAME]-style technical diagram for a [VENUE] paper. The diagram should feel [ADJECTIVES]..."

2. VISUAL STYLE (20-30 lines): Copy the full style block from above (A/B/C/D). This is the most important section — it determines the entire visual character.

3. COLOR PALETTE (10 lines): Exact hex codes for every color used.

4. LAYOUT (50-150 lines): Every component, box, section — exact text, spatial arrangement, and grouping. Be exhaustively specific.

5. CONNECTIONS (30-80 lines): Every arrow individually — source, target, style, label, routing direction.

6. CONSTRAINTS (10 lines): What NOT to include. Adapt per style — e.g., sketch style allows slight irregularity but still no clip art. ```

### Generation Script Template

```python #!/usr/bin/env python3 """Generate [FIGURE_NAME] diagram using Gemini image generation.""" import os, sys, time from google import genai

API_KEY = os.environ.get("GEMINI_API_KEY") if not API_KEY: print("ERROR: Set GEMINI_API_KEY environment variable.") print(" Get a key at: https://aistudio.google.com/apikey") sys.exit(1)

MODEL = "gemini-3-pro-image-preview" OUTPUT_DIR = os.path.dirname(os.path.abspath(__file__)) client = genai.Client(api_key=API_KEY)

PROMPT = """ [PASTE YOUR 6-SECTION PROMPT HERE] """

def generate_image(prompt_text, attempt

--- 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? → multi-panel or grouped bars 3. **Choose chart type** automatically using this priority: - Has a step/time axis → **line plot** - Comparing N methods on M benchmarks → **grouped bar chart** - Single ranking → **horizontal bar** (leaderboard) - Correlation between two continuous variables → **scatter plot** - Square matrix of values → **heatmap** - Proportional breakdown → **stacked bar** (avoid pie charts) 4. **Determine figure sizing** — Single column vs full width based on data density 5. **Highlight "our method"** — Identify which entry is the paper's contribution and give it a distinct color

### Auto-Detection Examples

**Context → Diagram**: "Our system has a Planner, Executor, and Verifier. Planner sends plans to Executor, Executor returns results to Verifier, Verifier feeds back to Planner on failure." → 3 entities, cycle layout, dashed feedback arrow → **Workflow 1 (Gemini)**

**Data → Chart**: "GPT-4: MMLU 86.4, HumanEval 67.0. Ours: 88.1, 71.2. Llama-3: 79.3, 62.1." → 3 methods × 2 benchmarks → **Workflow 2 (grouped bar)**, highlight "Ours" in coral

---

## Workflow 1: Architecture & System Diagrams (AI Image Generation)

Use Gemini 3 Pro Image Preview to generate diagrams. **Choose a visual style first** — this is the single biggest factor in whether the figure looks professional or generic.

### Visual Styles

Pick one style per paper (all figures should be consistent):

#### Style A: "Sketch / 简笔画" (Hand-Drawn)

Warm, approachable, memorable. Ideal for overview figures and system introductions. Looks like a whiteboard sketch refined by a designer.

``` VISUAL STYLE — HAND-DRAWN SKETCH: - Slightly irregular, hand-drawn line quality — lines wobble gently, not perfectly straight - Rounded, soft shapes with visible pen strokes (like drawn with a thick felt-tip marker) - Warm off-white background (#FAFAF7), NOT pure white - Fill colors are soft watercolor-like washes: muted blue (#D6E4F0), soft peach (#F5DEB3), light sage (#D4E6D4), pale lavender (#E6DFF0) - Borders are dark charcoal (#2C2C2C) with 2-3px line weight, slightly uneven - Arrows are hand-drawn with slight curves, ending in simple open arrowheads (not filled triangles) - Text uses a rounded sans-serif font (like Comic Neue or Architects Daughter feel) - Small doodle-style icons inside boxes: a tiny gear ⚙ for processing, a lightbulb 💡 for ideas, a magnifying glass 🔍 for search — rendered as simple line drawings, NOT emoji - Overall feel: a carefully drawn whiteboard diagram, clean but with personality - NO clip art, NO stock icons, NO photorealistic elements ```

#### Style B: "Modern Minimal" (Clean & Bold)

Confident, authoritative. Best for method figures where precision matters.

``` VISUAL STYLE — MODERN MINIMAL: - Ultra-clean geometric shapes with crisp edges - Bold color blocks as backgrounds for sections — NOT just accent bars, but full section fills using desaturated tones: slate blue (#E8EDF2), warm sand (#F5F0E8), cool mint (#E8F2EE) - Component boxes have ROUNDED CORNERS (12px radius), NO visible border — they float on the section background using subtle shadow (1px, 4px blur, rgba(0,0,0,0.06)) - ONE accent color per section used sparingly on key elements: Deep blue (#2563EB), Emerald (#059669), Amber (#D97706), Rose (#E11D48) - Arrows are thin (1.5px), dark gray (#6B7280), with small filled circle at source and clean arrowhead at target — NOT thick colored arrows - Typography: Inter or system sans-serif, title 600 weight, body 400 weight - Labels INSIDE boxes, not beside them - Generous whitespace — at least 24px between elements - NO decorative elements, NO icons — let the structure speak ```

#### Style C: "Illustrated Technical" (Icon-Rich)

Engaging, explanatory. Good for tutorial-style papers and figures that need to be self-explanatory.

``` VISUAL STYLE — ILLUSTRATED TECHNICAL: - Each major component has a small MEANINGFUL ICON drawn in a consistent line-art style (single color, 2px stroke, ~24x24px): brain icon for reasoning, database cylinder for storage, arrow-loop for iteration, network nodes for communication - Components sit inside soft rounded rectangles with a LEFT COLOR STRIP (4px wide) - Background is pure white, but each logical group has a very faint colored region behind it (#F8FAFC for blue group, #FFF8F0 for orange group) - Connections use CURVED bezier paths (not straight lines), colored by SOURCE component - Key data flows are THICKER (3px) than secondary flows (1px, dashed) - Small annotation badges on arrows: "×N" for repeated operations, "optional" in italics - Title labels are ABOVE each section in small caps, letter-spaced - Overall: like a well-designed API documentation diagram ```

#### Style D: "Accent Bar" (Classic Academic)

The default academic style. Safe for any venue, works well in grayscale.

``` VISUAL STYLE — CLASSIC ACCENT BAR: - Horizontal section bands stacked vertically, pale gray (#F7F7F5) fill - Thick colored LEFT ACCENT BAR (8px) distinguishes each section - Content boxes: white fill, thin #DDD border, 4px rounded corners - Section palette: Blue #4A90D9, Teal #5BA58B, Amber #D4A252, Slate #7B8794 - Sans-serif typography (Helvetica/Arial), bold titles, regular body - Colored arrows match their SOURCE section - Clean, flat, zero decoration ```

### Curated Color Palettes

**"Ocean Dusk"** (professional, calming — default recommendation): `#264653` deep teal, `#2A9D8F` teal, `#E9C46A` gold, `#F4A261` sandy orange, `#E76F51` burnt coral

**"Ink & Wash"** (for 简笔画 style): `#2C2C2C` charcoal ink, `#D6E4F0` washed blue, `#F5DEB3` washed wheat, `#D4E6D4` washed sage, `#E6DFF0` washed lavender

**"Nord"** (for modern minimal): `#2E3440` polar night, `#5E81AC` frost blue, `#A3BE8C` aurora green, `#EBCB8B` aurora yellow, `#BF616A` aurora red

**"Okabe-Ito"** (universal colorblind-safe, required for data charts): `#E69F00` orange, `#56B4E9` sky blue, `#009E73` green, `#F0E442` yellow, `#0072B2` blue, `#D55E00` vermillion, `#CC79A7` pink

### Checklist

- [ ] **Extract from context**: Read paper/description, identify entities and relationships - [ ] **Choose visual style** (A/B/C/D) — match the paper's tone and venue - [ ] **Choose color palette** — or use one consistent with existing paper figures - [ ] Obtain Gemini API key (`GEMINI_API_KEY` env var) - [ ] Write a detailed prompt: style block + layout + connections + constraints - [ ] Generate script at `figures/gen_fig_<name>.py`, run for 3 attempts - [ ] Review, select best, save as `figures/fig_<name>.png`

### Prompt Structure (6 Sections)

Every Gemini prompt must include these sections in order:

``` 1. FRAMING (5 lines): "Create a [STYLE_NAME]-style technical diagram for a [VENUE] paper. The diagram should feel [ADJECTIVES]..."

2. VISUAL STYLE (20-30 lines): Copy the full style block from above (A/B/C/D). This is the most important section — it determines the entire visual character.

3. COLOR PALETTE (10 lines): Exact hex codes for every color used.

4. LAYOUT (50-150 lines): Every component, box, section — exact text, spatial arrangement, and grouping. Be exhaustively specific.

5. CONNECTIONS (30-80 lines): Every arrow individually — source, target, style, label, routing direction.

6. CONSTRAINTS (10 lines): What NOT to include. Adapt per style — e.g., sketch style allows slight irregularity but still no clip art. ```

### Generation Script Template

```python #!/usr/bin/env python3 """Generate [FIGURE_NAME] diagram using Gemini image generation.""" import os, sys, time from google import genai

API_KEY = os.environ.get("GEMINI_API_KEY") if not API_KEY: print("ERROR: Set GEMINI_API_KEY environment variable.") print(" Get a key at: https://aistudio.google.com/apikey") sys.exit(1)

MODEL = "gemini-3-pro-image-preview" OUTPUT_DIR = os.path.dirname(os.path.abspath(__file__)) client = genai.Client(api_key=API_KEY)

PROMPT = """ [PASTE YOUR 6-SECTION PROMPT HERE] """

def generate_image(prompt_text, attempt

--- 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? → multi-panel or grouped bars 3. **Choose chart type** automatically using this priority: - Has a step/time axis → **line plot** - Comparing N methods on M benchmarks → **grouped bar chart** - Single ranking → **horizontal bar** (leaderboard) - Correlation between two continuous variables → **scatter plot** - Square matrix of values → **heatmap** - Proportional breakdown → **stacked bar** (avoid pie charts) 4. **Determine figure sizing** — Single column vs full width based on data density 5. **Highlight "our method"** — Identify which entry is the paper's contribution and give it a distinct color

### Auto-Detection Examples

**Context → Diagram**: "Our system has a Planner, Executor, and Verifier. Planner sends plans to Executor, Executor returns results to Verifier, Verifier feeds back to Planner on failure." → 3 entities, cycle layout, dashed feedback arrow → **Workflow 1 (Gemini)**

**Data → Chart**: "GPT-4: MMLU 86.4, HumanEval 67.0. Ours: 88.1, 71.2. Llama-3: 79.3, 62.1." → 3 methods × 2 benchmarks → **Workflow 2 (grouped bar)**, highlight "Ours" in coral

---

## Workflow 1: Architecture & System Diagrams (AI Image Generation)

Use Gemini 3 Pro Image Preview to generate diagrams. **Choose a visual style first** — this is the single biggest factor in whether the figure looks professional or generic.

### Visual Styles

Pick one style per paper (all figures should be consistent):

#### Style A: "Sketch / 简笔画" (Hand-Drawn)

Warm, approachable, memorable. Ideal for overview figures and system introductions. Looks like a whiteboard sketch refined by a designer.

``` VISUAL STYLE — HAND-DRAWN SKETCH: - Slightly irregular, hand-drawn line quality — lines wobble gently, not perfectly straight - Rounded, soft shapes with visible pen strokes (like drawn with a thick felt-tip marker) - Warm off-white background (#FAFAF7), NOT pure white - Fill colors are soft watercolor-like washes: muted blue (#D6E4F0), soft peach (#F5DEB3), light sage (#D4E6D4), pale lavender (#E6DFF0) - Borders are dark charcoal (#2C2C2C) with 2-3px line weight, slightly uneven - Arrows are hand-drawn with slight curves, ending in simple open arrowheads (not filled triangles) - Text uses a rounded sans-serif font (like Comic Neue or Architects Daughter feel) - Small doodle-style icons inside boxes: a tiny gear ⚙ for processing, a lightbulb 💡 for ideas, a magnifying glass 🔍 for search — rendered as simple line drawings, NOT emoji - Overall feel: a carefully drawn whiteboard diagram, clean but with personality - NO clip art, NO stock icons, NO photorealistic elements ```

#### Style B: "Modern Minimal" (Clean & Bold)

Confident, authoritative. Best for method figures where precision matters.

``` VISUAL STYLE — MODERN MINIMAL: - Ultra-clean geometric shapes with crisp edges - Bold color blocks as backgrounds for sections — NOT just accent bars, but full section fills using desaturated tones: slate blue (#E8EDF2), warm sand (#F5F0E8), cool mint (#E8F2EE) - Component boxes have ROUNDED CORNERS (12px radius), NO visible border — they float on the section background using subtle shadow (1px, 4px blur, rgba(0,0,0,0.06)) - ONE accent color per section used sparingly on key elements: Deep blue (#2563EB), Emerald (#059669), Amber (#D97706), Rose (#E11D48) - Arrows are thin (1.5px), dark gray (#6B7280), with small filled circle at source and clean arrowhead at target — NOT thick colored arrows - Typography: Inter or system sans-serif, title 600 weight, body 400 weight - Labels INSIDE boxes, not beside them - Generous whitespace — at least 24px between elements - NO decorative elements, NO icons — let the structure speak ```

#### Style C: "Illustrated Technical" (Icon-Rich)

Engaging, explanatory. Good for tutorial-style papers and figures that need to be self-explanatory.

``` VISUAL STYLE — ILLUSTRATED TECHNICAL: - Each major component has a small MEANINGFUL ICON drawn in a consistent line-art style (single color, 2px stroke, ~24x24px): brain icon for reasoning, database cylinder for storage, arrow-loop for iteration, network nodes for communication - Components sit inside soft rounded rectangles with a LEFT COLOR STRIP (4px wide) - Background is pure white, but each logical group has a very faint colored region behind it (#F8FAFC for blue group, #FFF8F0 for orange group) - Connections use CURVED bezier paths (not straight lines), colored by SOURCE component - Key data flows are THICKER (3px) than secondary flows (1px, dashed) - Small annotation badges on arrows: "×N" for repeated operations, "optional" in italics - Title labels are ABOVE each section in small caps, letter-spaced - Overall: like a well-designed API documentation diagram ```

#### Style D: "Accent Bar" (Classic Academic)

The default academic style. Safe for any venue, works well in grayscale.

``` VISUAL STYLE — CLASSIC ACCENT BAR: - Horizontal section bands stacked vertically, pale gray (#F7F7F5) fill - Thick colored LEFT ACCENT BAR (8px) distinguishes each section - Content boxes: white fill, thin #DDD border, 4px rounded corners - Section palette: Blue #4A90D9, Teal #5BA58B, Amber #D4A252, Slate #7B8794 - Sans-serif typography (Helvetica/Arial), bold titles, regular body - Colored arrows match their SOURCE section - Clean, flat, zero decoration ```

### Curated Color Palettes

**"Ocean Dusk"** (professional, calming — default recommendation): `#264653` deep teal, `#2A9D8F` teal, `#E9C46A` gold, `#F4A261` sandy orange, `#E76F51` burnt coral

**"Ink & Wash"** (for 简笔画 style): `#2C2C2C` charcoal ink, `#D6E4F0` washed blue, `#F5DEB3` washed wheat, `#D4E6D4` washed sage, `#E6DFF0` washed lavender

**"Nord"** (for modern minimal): `#2E3440` polar night, `#5E81AC` frost blue, `#A3BE8C` aurora green, `#EBCB8B` aurora yellow, `#BF616A` aurora red

**"Okabe-Ito"** (universal colorblind-safe, required for data charts): `#E69F00` orange, `#56B4E9` sky blue, `#009E73` green, `#F0E442` yellow, `#0072B2` blue, `#D55E00` vermillion, `#CC79A7` pink

### Checklist

- [ ] **Extract from context**: Read paper/description, identify entities and relationships - [ ] **Choose visual style** (A/B/C/D) — match the paper's tone and venue - [ ] **Choose color palette** — or use one consistent with existing paper figures - [ ] Obtain Gemini API key (`GEMINI_API_KEY` env var) - [ ] Write a detailed prompt: style block + layout + connections + constraints - [ ] Generate script at `figures/gen_fig_<name>.py`, run for 3 attempts - [ ] Review, select best, save as `figures/fig_<name>.png`

### Prompt Structure (6 Sections)

Every Gemini prompt must include these sections in order:

``` 1. FRAMING (5 lines): "Create a [STYLE_NAME]-style technical diagram for a [VENUE] paper. The diagram should feel [ADJECTIVES]..."

2. VISUAL STYLE (20-30 lines): Copy the full style block from above (A/B/C/D). This is the most important section — it determines the entire visual character.

3. COLOR PALETTE (10 lines): Exact hex codes for every color used.

4. LAYOUT (50-150 lines): Every component, box, section — exact text, spatial arrangement, and grouping. Be exhaustively specific.

5. CONNECTIONS (30-80 lines): Every arrow individually — source, target, style, label, routing direction.

6. CONSTRAINTS (10 lines): What NOT to include. Adapt per style — e.g., sketch style allows slight irregularity but still no clip art. ```

### Generation Script Template

```python #!/usr/bin/env python3 """Generate [FIGURE_NAME] diagram using Gemini image generation.""" import os, sys, time from google import genai

API_KEY = os.environ.get("GEMINI_API_KEY") if not API_KEY: print("ERROR: Set GEMINI_API_KEY environment variable.") print(" Get a key at: https://aistudio.google.com/apikey") sys.exit(1)

MODEL = "gemini-3-pro-image-preview" OUTPUT_DIR = os.path.dirname(os.path.abspath(__file__)) client = genai.Client(api_key=API_KEY)

PROMPT = """ [PASTE YOUR 6-SECTION PROMPT HERE] """

def generate_image(prompt_text, attempt

--- 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? → multi-panel or grouped bars 3. **Choose chart type** automatically using this priority: - Has a step/time axis → **line plot** - Comparing N methods on M benchmarks → **grouped bar chart** - Single ranking → **horizontal bar** (leaderboard) - Correlation between two continuous variables → **scatter plot** - Square matrix of values → **heatmap** - Proportional breakdown → **stacked bar** (avoid pie charts) 4. **Determine figure sizing** — Single column vs full width based on data density 5. **Highlight "our method"** — Identify which entry is the paper's contribution and give it a distinct color

### Auto-Detection Examples

**Context → Diagram**: "Our system has a Planner, Executor, and Verifier. Planner sends plans to Executor, Executor returns results to Verifier, Verifier feeds back to Planner on failure." → 3 entities, cycle layout, dashed feedback arrow → **Workflow 1 (Gemini)**

**Data → Chart**: "GPT-4: MMLU 86.4, HumanEval 67.0. Ours: 88.1, 71.2. Llama-3: 79.3, 62.1." → 3 methods × 2 benchmarks → **Workflow 2 (grouped bar)**, highlight "Ours" in coral

---

## Workflow 1: Architecture & System Diagrams (AI Image Generation)

Use Gemini 3 Pro Image Preview to generate diagrams. **Choose a visual style first** — this is the single biggest factor in whether the figure looks professional or generic.

### Visual Styles

Pick one style per paper (all figures should be consistent):

#### Style A: "Sketch / 简笔画" (Hand-Drawn)

Warm, approachable, memorable. Ideal for overview figures and system introductions. Looks like a whiteboard sketch refined by a designer.

``` VISUAL STYLE — HAND-DRAWN SKETCH: - Slightly irregular, hand-drawn line quality — lines wobble gently, not perfectly straight - Rounded, soft shapes with visible pen strokes (like drawn with a thick felt-tip marker) - Warm off-white background (#FAFAF7), NOT pure white - Fill colors are soft watercolor-like washes: muted blue (#D6E4F0), soft peach (#F5DEB3), light sage (#D4E6D4), pale lavender (#E6DFF0) - Borders are dark charcoal (#2C2C2C) with 2-3px line weight, slightly uneven - Arrows are hand-drawn with slight curves, ending in simple open arrowheads (not filled triangles) - Text uses a rounded sans-serif font (like Comic Neue or Architects Daughter feel) - Small doodle-style icons inside boxes: a tiny gear ⚙ for processing, a lightbulb 💡 for ideas, a magnifying glass 🔍 for search — rendered as simple line drawings, NOT emoji - Overall feel: a carefully drawn whiteboard diagram, clean but with personality - NO clip art, NO stock icons, NO photorealistic elements ```

#### Style B: "Modern Minimal" (Clean & Bold)

Confident, authoritative. Best for method figures where precision matters.

``` VISUAL STYLE — MODERN MINIMAL: - Ultra-clean geometric shapes with crisp edges - Bold color blocks as backgrounds for sections — NOT just accent bars, but full section fills using desaturated tones: slate blue (#E8EDF2), warm sand (#F5F0E8), cool mint (#E8F2EE) - Component boxes have ROUNDED CORNERS (12px radius), NO visible border — they float on the section background using subtle shadow (1px, 4px blur, rgba(0,0,0,0.06)) - ONE accent color per section used sparingly on key elements: Deep blue (#2563EB), Emerald (#059669), Amber (#D97706), Rose (#E11D48) - Arrows are thin (1.5px), dark gray (#6B7280), with small filled circle at source and clean arrowhead at target — NOT thick colored arrows - Typography: Inter or system sans-serif, title 600 weight, body 400 weight - Labels INSIDE boxes, not beside them - Generous whitespace — at least 24px between elements - NO decorative elements, NO icons — let the structure speak ```

#### Style C: "Illustrated Technical" (Icon-Rich)

Engaging, explanatory. Good for tutorial-style papers and figures that need to be self-explanatory.

``` VISUAL STYLE — ILLUSTRATED TECHNICAL: - Each major component has a small MEANINGFUL ICON drawn in a consistent line-art style (single color, 2px stroke, ~24x24px): brain icon for reasoning, database cylinder for storage, arrow-loop for iteration, network nodes for communication - Components sit inside soft rounded rectangles with a LEFT COLOR STRIP (4px wide) - Background is pure white, but each logical group has a very faint colored region behind it (#F8FAFC for blue group, #FFF8F0 for orange group) - Connections use CURVED bezier paths (not straight lines), colored by SOURCE component - Key data flows are THICKER (3px) than secondary flows (1px, dashed) - Small annotation badges on arrows: "×N" for repeated operations, "optional" in italics - Title labels are ABOVE each section in small caps, letter-spaced - Overall: like a well-designed API documentation diagram ```

#### Style D: "Accent Bar" (Classic Academic)

The default academic style. Safe for any venue, works well in grayscale.

``` VISUAL STYLE — CLASSIC ACCENT BAR: - Horizontal section bands stacked vertically, pale gray (#F7F7F5) fill - Thick colored LEFT ACCENT BAR (8px) distinguishes each section - Content boxes: white fill, thin #DDD border, 4px rounded corners - Section palette: Blue #4A90D9, Teal #5BA58B, Amber #D4A252, Slate #7B8794 - Sans-serif typography (Helvetica/Arial), bold titles, regular body - Colored arrows match their SOURCE section - Clean, flat, zero decoration ```

### Curated Color Palettes

**"Ocean Dusk"** (professional, calming — default recommendation): `#264653` deep teal, `#2A9D8F` teal, `#E9C46A` gold, `#F4A261` sandy orange, `#E76F51` burnt coral

**"Ink & Wash"** (for 简笔画 style): `#2C2C2C` charcoal ink, `#D6E4F0` washed blue, `#F5DEB3` washed wheat, `#D4E6D4` washed sage, `#E6DFF0` washed lavender

**"Nord"** (for modern minimal): `#2E3440` polar night, `#5E81AC` frost blue, `#A3BE8C` aurora green, `#EBCB8B` aurora yellow, `#BF616A` aurora red

**"Okabe-Ito"** (universal colorblind-safe, required for data charts): `#E69F00` orange, `#56B4E9` sky blue, `#009E73` green, `#F0E442` yellow, `#0072B2` blue, `#D55E00` vermillion, `#CC79A7` pink

### Checklist

- [ ] **Extract from context**: Read paper/description, identify entities and relationships - [ ] **Choose visual style** (A/B/C/D) — match the paper's tone and venue - [ ] **Choose color palette** — or use one consistent with existing paper figures - [ ] Obtain Gemini API key (`GEMINI_API_KEY` env var) - [ ] Write a detailed prompt: style block + layout + connections + constraints - [ ] Generate script at `figures/gen_fig_<name>.py`, run for 3 attempts - [ ] Review, select best, save as `figures/fig_<name>.png`

### Prompt Structure (6 Sections)

Every Gemini prompt must include these sections in order:

``` 1. FRAMING (5 lines): "Create a [STYLE_NAME]-style technical diagram for a [VENUE] paper. The diagram should feel [ADJECTIVES]..."

2. VISUAL STYLE (20-30 lines): Copy the full style block from above (A/B/C/D). This is the most important section — it determines the entire visual character.

3. COLOR PALETTE (10 lines): Exact hex codes for every color used.

4. LAYOUT (50-150 lines): Every component, box, section — exact text, spatial arrangement, and grouping. Be exhaustively specific.

5. CONNECTIONS (30-80 lines): Every arrow individually — source, target, style, label, routing direction.

6. CONSTRAINTS (10 lines): What NOT to include. Adapt per style — e.g., sketch style allows slight irregularity but still no clip art. ```

### Generation Script Template

```python #!/usr/bin/env python3 """Generate [FIGURE_NAME] diagram using Gemini image generation.""" import os, sys, time from google import genai

API_KEY = os.environ.get("GEMINI_API_KEY") if not API_KEY: print("ERROR: Set GEMINI_API_KEY environment variable.") print(" Get a key at: https://aistudio.google.com/apikey") sys.exit(1)

MODEL = "gemini-3-pro-image-preview" OUTPUT_DIR = os.path.dirname(os.path.abspath(__file__)) client = genai.Client(api_key=API_KEY)

PROMPT = """ [PASTE YOUR 6-SECTION PROMPT HERE] """

def generate_image(prompt_text, attempt