paper-illustration skill

Paper Illustration: Multi-Stage Codex-Supervised Figure Generation

Generate publication-quality illustrations using a multi-stage workflow with Codex as the STRICT supervisor/reviewer.

Core Design Philosophy

┌──────────────────────────────────────────────────────────────────────────┐
│                    MULTI-STAGE ITERATIVE WORKFLOW                        │
├──────────────────────────────────────────────────────────────────────────┤
│                                                                          │
│   User Request                                                           │
│       │                                                                  │
│       ▼                                                                  │
│   ┌─────────────┐                                                        │
│   │   Codex    │ ◄─── Step 1: Parse request, create initial prompt     │
│   │  (Planner)  │                                                        │
│   └──────┬──────┘                                                        │
│          │                                                               │
│          ▼                                                               │
│   ┌─────────────┐                                                        │
│   │   Gemini    │ ◄─── Step 2: Optimize layout description               │
│   │ (gemini-3-pro)│      - Refine component p

Constants

• IMAGEMODEL = gemini-3-pro-image-preview** — Paperbanana (Nano Banana Pro) for image rendering
• REASONINGMODEL = gemini-3-pro-preview** — Gemini for layout optimization and style checking
• MAXITERATIONS = 5** — Maximum refinement rounds
• TARGETSCORE = 9** — Minimum acceptable score (1-10) — RAISED FOR QUALITY
• OUTPUTDIR = figures/aigenerated/ — Output directory
• APIKEYENV = GEMINIAPIKEY — Environment variable

CVPR/ICLR/NeurIPS Top-Tier Conference Style Guide

What "CVPR Style" Actually Means:

Visual Standards

• Clean white background — No decorative patterns or gradients (unless subtle)
• Sans-serif fonts — Arial, Helvetica, or Computer Modern; minimum 14pt
• Subtle color palette — Not rainbow colors; use 3-5 coordinated colors
• Print-friendly — Must be readable in grayscale (many reviewers print papers)
• Professional borders — Thin (2-3px), solid colors, not flashy

Layout Standards

• Horizontal flow — Left-to-right is the standard for pipelines
• Clear grouping — Use subtle background boxes to group related modules
• Consistent sizing — Similar components should have similar sizes
• Balanced whitespace — Not cramped, not sparse

Arrow Standards (MOST CRITICAL)

• Thick strokes — 4-6px minimum (thin arrows disappear when printed)
• Clear arrowheads — Large, filled triangular heads
• Dark colors — Black or dark gray (#333333); avoid colored arrows
• Labeled — Every arrow should indicate what data flows through it
• No crossings — Reorganize layout to avoid arrow crossings
• CORRECT DIRECTION — Arrows must point to the RIGHT target!

Visual Appeal (科研风格 - Professional Academic Style)

目标：既不保守也不花哨，找到平衡点

✅ 应该有的视觉元素：

• Subtle gradient fills — 淡雅的渐变填充（同色系从浅到深），不是炫彩
• Rounded corners — 圆角矩形（6-10px radius），现代感但不夸张
• Clear visual hierarchy — 通过大小、颜色深浅区分层次
• Consistent color coding — 统一的配色方案（3-4种主色）
• Internal structure — 大模块内部显示子组件（如Encoder内部的layer结构）
• Professional typography — 清晰的标签，适当的字号层次

✅ 配色建议（学术专业）：

• Inputs: 柔和的绿色系 (#10B981 / #34D399)
• Encoders: 专业的蓝色系 (#2563EB / #3B82F6)
• Fusion: 优雅的紫色系 (#7C3AED / #8B5CF6)
• Outputs: 温暖的橙色系 (#EA580C / #F97316)
• Arrows: 黑色或深灰 (#333333 / #1F2937)
• Background: 纯白 (#FFFFFF)，不要花纹

❌ 要避免的过度装饰：

• ❌ Rainbow color schemes (彩虹配色)
• ❌ Heavy drop shadows (重阴影效果)
• ❌ 3D effects / perspective (3D透视)
• ❌ Excessive gradients (夸张的多色渐变)
• ❌ Clip art / cartoon icons (卡通图标)
• ❌ Decorative patterns in background (背景花纹)
• ❌ Glowing effects (发光效果)
• ❌ Too many small icons (过多小图标)

✓ 理想的视觉效果：

• 一眼看上去专业、清晰
• 有适度的视觉吸引力，但不抢眼
• 符合CVPR/NeurIPS论文的审美标准
• 打印友好（灰度模式下也能清晰辨认）
• 像精心设计的学术图表，而不是PPT模板

What to AVOID (CRITICAL)

• ❌ Rainbow color schemes (too many colors)
• ❌ Thin, hairline arrows (arrows must be THICK)
• ❌ Unlabeled connections
• ❌ Plain boring rectangles (add some visual interest)
• ❌ Over-decorated with shadows/glows/icons (too flashy)
• ❌ Small text that's unreadable when printed
• ❌ WRONG arrow directions — This is UNACCEPTABLE!

Scope

Not for: Statistical plots (use /paper-figure), photo-realistic images

Workflow: MUST EXECUTE ALL STEPS

Step 0: Pre-flight Check

# Check API key
if [ -z "$GEMINI_API_KEY" ]; then
    echo "ERROR: GEMINI_API_KEY not set"
    echo "Get your key from: https://aistudio.google.com/app/apikey"
    echo "Set it: export GEMINI_API_KEY='your-key'"
    exit 1
fi

# Create output directory
mkdir -p figures/ai_generated

Step 1: Codex Plans the Figure (YOU ARE HERE)

CRITICAL: Codex must first analyze the user's request and create a detailed prompt.

Parse the input: $ARGUMENTS

Codex's task:

1. Understand what figure the user wants
2. Identify all components, connections, data flow
3. Create a detailed, structured prompt for Gemini
4. Include style requirements AND visual appeal requirements

Prompt Template for Codex to generate:

Create a PROFESSIONAL, VISUALLY APPEALING publication-quality academic diagram following CVPR/ICLR/NeurIPS standards.

## Visual Style: 科研风格 (Academic Professional Style)
### 目标：平衡 — 既不保守也不花哨

#### DO (应该有):
- **Subtle gradients** — 同色系淡雅渐变（如 #2563EB → #3B82F6），不是多色炫彩
- **Rounded corners** — 圆角矩形（6-10px），现代感
- **Clear visual hierarchy** — 通过大小、深浅区分层次
- **Internal structure** — 大模块内显示子组件结构
- **Consistent color coding** — 统一的3-4色方案
- **Professional polish** — 精致但不夸张

#### DON'T (不要有):
- ❌ Rainbow/multi-color gradients (彩虹渐变)
- ❌ Heavy drop shadows (重阴影)
- ❌ 3D effects / perspective (3D效果)
- ❌ Glowing effects (发光效果)
- ❌ Excessive decorative icons (过多装饰图标)
- ❌ Plain boring rectangles (完全平淡的方块)

#### 理想效果：
像顶会论文中精心设计的架构图 — 专业、清晰、有适度的视觉吸引力

## Figure Type
[Architecture Diagram / Pipeline / Comparison / etc.]

## Components to Include (BE SPECIFIC ABOUT CONTENT)
1. [Component 1]:
   - Label: "[exact text]"
   - Sub-label: "[smaller text below]"
   - Position: [left/center/right, top/middle/bottom]
   - Style: [border color, fill, internal structure]
2. [Component 2]: ...

## Layout
- Direction: [left-to-right / top-to-bottom]
- Spacing: [tight / normal / loose]
- Grouping: [how components

Step 2: Gemini Layout Optimization (gemini-3-pro)

Codex sends the initial prompt to Gemini (gemini-3-pro) for layout optimization.

#!/bin/bash
# Step 2: Optimize layout using Gemini gemini-3-pro
# This step refines component positioning and spacing

set -e

OUTPUT_DIR="figures/ai_generated"
mkdir -p "$OUTPUT_DIR"

API_KEY="${GEMINI_API_KEY}"
URL="https://generativelanguage.googleapis.com/v1beta/models/gemini-3-pro-preview:generateContent?key=$API_KEY"

# The initial prompt from Codex
INITIAL_PROMPT='[Codex fills in the detailed prompt here]'

# Layout optimization request
LAYOUT_REQUEST="You are an expert in academic figure layout design for CVPR/NeurIPS papers.

Analyze this figure request and provide an OPTIMIZED LAYOUT DESCRIPTION:

$INITIAL_PROMPT

Provide:
1. **Optimized Component Positions**: Exact positions (left/center/right, top/middle/bottom) for each component
2. **Spacing Recommendations**: Specific spacing between components
3. **Grouping Strategy**: Which components should be visually grouped together
4. **Arrow Routing**: Optimal paths for arrows to avoid crossings
5. **Visual Hierarchy**: Size recommendations for main vs sub-components

Output a DETAILED layout specification that will be used for rendering."

# Build JSON payload
python3 << PYTHON
import json
payload = {
    "contents": [{"part

Step 3: Gemini Style Verification (gemini-3-pro)

Codex sends the optimized layout to Gemini for CVPR/NeurIPS style verification.

#!/bin/bash
# Step 3: Verify and enhance style compliance using Gemini gemini-3-pro

API_KEY="${GEMINI_API_KEY}"
URL="https://generativelanguage.googleapis.com/v1beta/models/gemini-3-pro-preview:generateContent?key=$API_KEY"

# Read layout from previous step
LAYOUT=$(cat figures/ai_generated/layout_description.txt)

# Style verification request
STYLE_REQUEST="You are a CVPR/NeurIPS paper figure reviewer specializing in visual standards.

Review and ENHANCE this figure specification for top-tier conference compliance:

$LAYOUT

Ensure compliance with:
1. **Color Palette**: Use professional academic colors (green for inputs, blue for encoders, purple for fusion, orange for outputs)
2. **Arrow Standards**: Thick (5-6px), black/dark gray, clear arrowheads, all labeled
3. **Font Standards**: Sans-serif, minimum 14pt, readable in print
4. **Visual Appeal (科研风格)**:
   - ✅ Subtle same-color gradients, rounded corners (6-10px), internal structure visible
   - ❌ NO heavy shadows, NO glowing effects, NO rainbow gradients

Output an ENHANCED figure specification with explicit style instructions for rendering."

# Build JSON payload
python3 << PYTHON
import json
payload = {
    "contents": [{"p

Step 4: Paperbanana Image Rendering (gemini-3-pro-image-preview)

Codex sends the optimized, style-verified specification to Paperbanana for rendering.

#!/bin/bash
# Step 4: Render image using Paperbanana (gemini-3-pro-image-preview)
# Internal codename: Nano Banana Pro
# Use DIRECT connection (no proxy) - proxy causes SSL errors

set -e

OUTPUT_DIR="figures/ai_generated"
mkdir -p "$OUTPUT_DIR"

API_KEY="${GEMINI_API_KEY}"
URL="https://generativelanguage.googleapis.com/v1beta/models/gemini-3-pro-image-preview:generateContent?key=$API_KEY"

# Read the style-enhanced specification from previous step
STYLE_SPEC=$(cat figures/ai_generated/style_spec.txt)

# Add rendering instructions
RENDER_PROMPT="Render a publication-quality academic diagram based on this specification:

$STYLE_SPEC

RENDERING REQUIREMENTS:
- Output a clean, professional diagram suitable for CVPR/NeurIPS submission
- Use vector-quality rendering with sharp edges and clear text
- Ensure all elements are properly aligned and spaced
- The diagram should be immediately understandable at a glance"

# Build JSON payload using Python for proper escaping
python3 << PYTHON
import json
payload = {
    "contents": [{"parts": [{"text": '''$RENDER_PROMPT'''}]}],
    "generationConfig": {"responseModalities": ["TEXT", "IMAGE"]}
}
with open("/tmp/gemini_request.json", "w") as f:
 

Step 5: Codex STRICT Visual Review & Scoring (MANDATORY)

Codex MUST read the generated image and perform a STRICT review:

1. Visual Analysis: What does the image show in detail?
2. Strengths: What's good about it?
3. STRICT Verification: Check EVERY item below
4. Score: Rate 1-10 (10 = perfect) — BE STRICT!

STRICT Review Template:

## Codex's STRICT Review of Figure v{N}

### What I See
[Describe the generated image in DETAIL - every block, every arrow]

### Strengths
- [Strength 1]
- [Strength 2]

### ═══════════════════════════════════════════════════════════════
### STRICT VERIFICATION CHECKLIST (ALL must pass for score ≥ 9)
### ═══════════════════════════════════════════════════════════════

#### A. Arrow Correctness Verification (CRITICAL - any failure = score ≤ 6)
Check EACH arrow:
- [ ] Arrow 1: [Source] → [Target] — Does it point to the CORRECT target?
- [ ] Arrow 2: [Source] → [Target] — Does it point to the CORRECT target?
- [ ] Arrow 3: [Source] → [Target] — Does it point to the CORRECT target?
- [ ] Arrow 4: [Source] → [Target] — Does it point to the CORRECT target?
- [ ] Arrow 5: [Source] → [Target] — Does it point to the CORRECT target?
- [ ] Arrow 6: [Source] → [Target] — Does it point to the CORRECT target?

#### B. Block Content Verification (any failure = score ≤ 7)
Check EACH block:
- [ ] Block 1 "[Name]": Has correct label? Has sub-label? Content correct?
- [ ] Block 2 "[Name]": Has correct label? Has sub-label? Content correct?
- [ ] Block 3 "[Name]": Has correct label? Has sub-label? Con