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_num):
    print(f"\n{'='*60}\nAttempt {attempt_num}\n{'='*60}")
    try:
        response = client.models.generate_content(
            model=MODEL,
            contents=prompt_text,
            config=genai.types.GenerateContentConfig(
                response_modalities=["IMAGE", "TEXT"],
            ),
        )
        output_path = os.path.join(OUTPUT_DIR, f"fig_NAME_attempt{attempt_num}.png")
        for part in response.candidates[0].content.parts:
            if part.inline_data:
                with open(output_path, "wb") as f:
                    f.write(part.inline_data.data)
                print(f"Saved: {output_path} ({os.path.getsize(output_path):,} bytes)")
                return output_path
            elif part.text:
                print(f"Text: {part.text[:300]}")
        print("WARNING: No image in response")
        return None
    except Exception as e:
        print(f"ERROR: {e}")
        return None

def main():
    results = []
    for i in range(1, 4):
        if i > 1:
            time.sleep(2)
        path = generate_image(PROMPT, i)
        if path:
            results.append(path)
    if not results:
        print("All attempts failed!")
        sys.exit(1)
    print(f"\nGenerated {len(results)} attempts. Review and pick the best.")

if __name__ == "__main__":
    main()

Key Rules

• Always 3 attempts — quality varies significantly between runs
• Style block is mandatory — without it, Gemini defaults to generic corporate look
• Never hardcode API keys — use os.environ.get("GEMINI_API_KEY")
• Save generation scripts — reproducibility is critical
• Specify every label exactly — Gemini may misspell or rearrange text

Full prompt examples per style: See references/diagram-generation.md

---

Workflow 2: Data-Driven Charts (matplotlib/seaborn)

For any figure with numerical data, axes, or quantitative comparisons.

Checklist

• Extract from context: Parse results/data, identify methods, metrics, and comparison structure
• Auto-select chart type based on data dimensions (see decision guide below)
• Prepare data (CSV, dict, or inline arrays)
• Apply publication styling (fonts, colors, sizes)
• Highlight "our method" with a distinct color
• Export as both PDF (vector) and PNG (300 DPI)
• Verify LaTeX font compatibility
• Save script at figures/gen_fig_<name>.py

Chart Type Decision Guide

Data Pattern	Best Chart	Notes
Trend over time/steps	Line plot	Training curves, scaling laws
Comparing categories	Grouped bar chart	Model comparisons, ablations
Distribution	Violin / box plot	Score distributions across methods
Correlation	Scatter plot	Embedding analysis, metric correlation
Grid of values	Heatmap	Attention maps, confusion matrices
Part of whole	Stacked bar (not pie)	Prefer stacked bar over pie in ML papers
Many methods, one metric	Horizontal bar	Leaderboard-style comparisons

Publication Styling Template

import matplotlib.pyplot as plt
import numpy as np

# --- Publication defaults (polished, not generic) ---
plt.rcParams.update({
    "font.family": "serif", "font.serif": ["Times New Roman", "DejaVu Serif"],
    "font.size": 10, "axes.titlesize": 11, "axes.titleweight": "bold",
    "axes.labelsize": 10, "legend.fontsize": 8.5, "legend.frameon": False,
    "figure.dpi": 300, "savefig.dpi": 300, "savefig.bbox": "tight",
    "axes.spines.top": False, "axes.spines.right": False,
    "axes.grid": True, "grid.alpha": 0.15, "grid.linestyle": "-",
    "lines.linewidth": 1.8, "lines.markersize": 5,
})

# --- "Ocean Dusk" palette (professional, distinctive, colorblind-safe) ---
COLORS = ["#264653", "#2A9D8F", "#E9C46A", "#F4A261", "#E76F51",
          "#0072B2", "#56B4E9", "#8C8C8C"]
OUR_COLOR = "#E76F51"       # coral — warm, stands out
BASELINE_COLOR = "#B0BEC5"  # cool gray — recedes
FIG_SINGLE, FIG_FULL = (3.25, 2.5), (6.75, 2.8)

Common Chart Patterns

Line plot (training curves) — with markers and confidence bands:

fig, ax = plt.subplots(figsize=FIG_SINGLE)
markers = ["o", "s", "^", "D", "v"]
for i, (method, (mean, std)) in enumerate(results.items()):
    color = OUR_COLOR if method == "Ours" else COLORS[i]
    ax.plot(steps, mean, label=method, color=color,
            marker=markers[i % 5], markevery=max(1, len(steps)//8),
            markersize=4, zorder=3)
    ax.fill_between(steps, mean - std, mean + std, color=color, alpha=0.12)
ax.set_xlabel("Training Steps")
ax.set_ylabel("Accuracy (%)")
ax.legend(loc="lower right")
fig.savefig("figures/fig_training.pdf")
fig.savefig("figures/fig_training.png", dpi=300)

Grouped bar chart (ablation) — with value labels:

fig, ax = plt.subplots(figsize=FIG_FULL)
x = np.arange(len(categories))
n = len(methods)
width = 0.7 / n
for i, (method, scores) in enumerate(methods.items()):
    color = OUR_COLOR if method == "Ours" else COLORS[i]
    offset = (i - n / 2 + 0.5) * width
    bars = ax.bar(x + offset, scores, width * 0.9, label=method, color=color,
                  edgecolor="white", linewidth=0.5)
    for bar, s in zip(bars, scores):
        ax.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 0.3,
                f"{s:.1f}", ha="center", va="bottom", fontsize=7, color="#444")
ax.set_xticks(x)
ax.set_xticklabels(categories)
ax.set_ylabel("Score")
ax.legend(ncol=min(n, 4))
fig.savefig("figures/fig_ablation.pdf")

Heatmap — with diverging colormap and clean borders:

import seaborn as sns
fig, ax = plt.subplots(figsize=(4, 3.5))
sns.heatmap(matrix, annot=True, fmt=".2f", cmap="YlOrRd", ax=ax,
            cbar_kws={"shrink": 0.75, "aspect": 20},
            linewidths=1.5, linecolor="white",
            annot_kws={"size": 8, "weight": "medium"})
ax.set_xlabel("Predicted")
ax.set_ylabel("Actual")
fig.savefig("figures/fig_confusion.pdf")

Horizontal bar (leaderboard) — with "our method" highlight:

fig, ax = plt.subplots(figsize=FIG_SINGLE)
y_pos = np.arange(len(models))
colors = [BASELINE_COLOR] * len(models)
colors[our_idx] = OUR_COLOR
bars = ax.barh(y_pos, scores, color=colors, height=0.55,
               edgecolor="white", linewidth=0.5)
ax.set_yticks(y_pos)
ax.set_yticklabels(models)
ax.set_xlabel("Accuracy (%)")
ax.invert_yaxis()
for bar, s in zip(bars, scores):
    ax.text(bar.get_width() + 0.3, bar.get_y() + bar.get_height()/2,
            f"{s:.1f}", va="center", fontsize=8, color="#444")
fig.savefig("figures/fig_leaderboard.pdf")

Full pattern library (scaling laws, violin plots, multi-panel, radar): See references/data-visualization.md

---

Publication Style Quick Reference

Venue	Single Col	Full Width	Font
NeurIPS	5.5 in	5.5 in	Times
ICML	3.25 in	6.75 in	Times
ICLR	5.5 in	5.5 in	Times
ACL	3.3 in	6.8 in	Times
AAAI	3.3 in	7.0 in	Times

Always export PDF for vector quality. PNG only for AI-generated diagrams.

Venue-specific details, LaTeX integration, font matching, accessibility checklist: See references/style-guide.md

---

Common Issues

Issue	Solution
Fonts look wrong in LaTeX	Export PDF, set text.usetex=True, or use font.family=serif
Figure too large for column	Check venue width limits, use figsize in inches
Colors indistinguishable in print	Use colorblind-safe palette + different line styles/markers
Gemini misspells labels	Spell out every label exactly in prompt, add "SPELL EXACTLY" constraint
Gemini ignores style	Add more negative constraints, be more specific about hex colors
Blurry figures in PDF	Export as PDF (vector), not PNG; or use 300+ DPI for PNG
Legend overlaps data	Use bbox_to_anchor, loc="upper left", or external legend
Too many tick labels	Use ax.xaxis.set_major_locator(MaxNLocator(5))

When to Use vs Alternatives

Need	This Skill	Alternative
Architecture diagrams	Gemini generation	TikZ (manual), draw.io (interactive), Mermaid (simple)
Data charts	matplotlib/seaborn	Plotly (interactive), R/ggplot2 (statistics-heavy)
Full paper writing	Use with ml-paper-writing	—
Poster figures	Larger fonts, wider	latex-posters skill
Presentation figures	Larger text, fewer details	PowerPoint/Keynote export

---

Quick Reference: File Naming Convention

figures/
├── gen_fig_<name>.py      # Generation script (always save for reproducibility)
├── fig_<name>.pdf         # Final vector output (for LaTeX)
├── fig_<name>.png         # Raster output (300 DPI, for AI-generated or fallback)
└── fig_<name>_attempt*.png # Gemini attempts (keep for comparison)