rohitg00 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 positioning │
│ │ Layout │ - Optimize spacing and grouping │
│ └──────┬──────┘ │
│ │ │
│ ▼ │
│ ┌─────────────┐ │
│ │ Gemini │ ◄─── Step 3: CVPR/NeurIPS style verification │
│ │ (gemini-3-pro)│ - Check color palette compliance │
│ │ Style │ - Verify arrow and font standards │
│ └──────┬──────┘ │
│ │ │
│ ▼ │
│ ┌─────────────┐ │
│ │ Paperbanana │ ◄─── Step 4: Render final image │
│ │ (gemini-3- │ - High-quality image generation │
│ │ pro-image) │ - Internal codename: Nano Banana Pro │
│ └──────┬──────┘ │
│ │ │
│ ▼ │
│ ┌─────────────┐ │
│ │ Codex │ ◄─── Step 5: STRICT visual review + SCORE (1-10) │
│ │ (Reviewer) │ - Verify EVERY arrow direction │
│ │ STRICT! │ - Verify EVERY block content │
│ └──────┬──────┘ - Verify aesthetics & visual appeal │
│ │ │
│ ▼ │
│ Score ≥ 9? ──YES──► Accept & Output │
│ │ │
│ NO │
│ │ │
│ ▼ │
│ Generate SPECIFIC improvement feedback ──► Loop back to Step 2 │
│ │
└──────────────────────────────────────────────────────────────────────────┘
Constants
- IMAGE_MODEL =
gemini-3-pro-image-preview— Paperbanana (Nano Banana Pro) for image rendering - REASONING_MODEL =
gemini-3-pro-preview— Gemini for layout optimization and style checking - MAX_ITERATIONS = 5 — Maximum refinement rounds
- TARGET_SCORE = 9 — Minimum acceptable score (1-10) — RAISED FOR QUALITY
- OUTPUT_DIR =
figures/ai_generated/— Output directory - API_KEY_ENV =
GEMINI_API_KEY— 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
| Figure Type | Quality | Examples |
|---|---|---|
| Architecture diagrams | Excellent | Model architecture, pipeline, encoder-decoder |
| Method illustrations | Excellent | Conceptual diagrams, algorithm flowcharts |
| Conceptual figures | Good | Comparison diagrams, taxonomy trees |
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:
- Understand what figure the user wants
- Identify all components, connections, data flow
- Create a detailed, structured prompt for Gemini
- 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 should be grouped]
## Connections (BE EXPLICIT ABOUT DIRECTION)
EXACT arrow specifications:
1. [Component A] → [Component B]: Arrow goes FROM A TO B, label it "[data type]"
2. [Component C] → [Component D]: Arrow goes FROM C TO D, label it "[data type]"
...
VERIFY: Each arrow must point to the CORRECT target!
## Style Requirements (CVPR/ICLR/NeurIPS Standard)
### Visual Style
- Color palette: Professional academic colors
- Inputs: Green (#10B981)
- Encoders: Blue (#2563EB)
- Fusion modules: Purple (#7C3AED)
- Outputs: Orange (#EA580C)
- Font: Sans-serif (Arial/Helvetica), minimum 14pt, bold for labels
- Background: Clean white, no patterns
- Blocks: Rounded rectangles (8-12px radius), subtle gradient fill, colored border (2-3px)
- Subtle shadows for depth effect
- Print-friendly (must work in grayscale)
### CRITICAL: Arrow & Data Flow Requirements
1. **ALL arrows must be VERY THICK** - minimum 5-6px stroke width
2. **ALL arrows must have CLEAR arrowheads** - large, visible triangular heads
3. **ALL arrows must be BLACK or DARK GRAY** - not colored
4. **Label EVERY arrow** with what data flows through it
5. **VERIFY arrow direction** - each arrow MUST point to the correct target
6. **No ambiguous connections** - every arrow should have a clear source and destination
### Logic Clarity Requirements
1. **Data flow must be immediately obvious** - viewer should understand the pipeline in 5 seconds
2. **No crossing arrows** - reorganize layout to avoid arrow crossings
3. **Consistent direction** - maintain left-to-right or top-to-bottom flow throughout
4. **Group related components** - use subtle background boxes or spacing to group modules
5. **Clear hierarchy** - main components larger, sub-components smaller
## Additional Requirements
[Any specific requirements from user]
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:
$INITIAL_PROMPT
Provide:
1. **Optimized Component Positions**: Exact positions (left/center/right, top/middle/bottom) for each component
2. **Spacing Recommendations**: Specific spacing between components
3. **Grouping Strategy**: Which components should be visually grouped together
4. **Arrow Routing**: Optimal paths for arrows to avoid crossings
5. **Visual Hierarchy**: Size recommendations for main vs sub-components
Output a DETAILED layout specification that will be used for rendering."
# Build JSON payload
python3 << PYTHON
import json
payload = {
"contents": [{"parts": [{"text": '''$LAYOUT_REQUEST'''}]}]
}
with open("/tmp/gemini_layout_request.json", "w") as f:
json.dump(payload, f, indent=2)
print("Layout request created")
PYTHON
# Call Gemini gemini-3-pro-preview for layout optimization (DIRECT connection, no proxy)
RESPONSE=$(curl -s --max-time 90 \
-X POST "$URL" \
-H 'Content-Type: application/json' \
-d @/tmp/gemini_layout_request.json)
# Extract layout description
LAYOUT_DESCRIPTION=$(echo "$RESPONSE" | python3 -c "
import sys, json
data = json.load(sys.stdin)
try:
print(data['candidates'][0]['content']['parts'][0]['text'])
except:
print('Error extracting layout')
")
echo "=== Layout Optimization Complete ==="
echo "$LAYOUT_DESCRIPTION"
echo "$LAYOUT_DESCRIPTION" > "$OUTPUT_DIR/layout_description.txt"
Step 3: Gemini Style Verification (gemini-3-pro)
Codex sends the optimized layout to Gemini for CVPR/NeurIPS style verification.
#!/bin/bash
# Step 3: Verify and enhance style compliance using Gemini gemini-3-pro
API_KEY="${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:
$LAYOUT
Ensure compliance with:
1. **Color Palette**: Use professional academic colors (green for inputs, blue for encoders, purple for fusion, orange for outputs)
2. **Arrow Standards**: Thick (5-6px), black/dark gray, clear arrowheads, all labeled
3. **Font Standards**: Sans-serif, minimum 14pt, readable in print
4. **Visual Appeal (科研风格)**:
- ✅ Subtle same-color gradients, rounded corners (6-10px), internal structure visible
- ❌ NO heavy shadows, NO glowing effects, NO rainbow gradients
Output an ENHANCED figure specification with explicit style instructions for rendering."
# Build JSON payload
python3 << PYTHON
import json
payload = {
"contents": [{"parts": [{"text": '''$STYLE_REQUEST'''}]}]
}
with open("/tmp/gemini_style_request.json", "w") as f:
json.dump(payload, f, indent=2)
print("Style request created")
PYTHON
# Call Gemini gemini-3-pro-preview for style verification (DIRECT connection, no proxy)
RESPONSE=$(curl -s --max-time 90 \
-X POST "$URL" \
-H 'Content-Type: application/json' \
-d @/tmp/gemini_style_request.json)
# Extract style-enhanced specification
STYLE_SPEC=$(echo "$RESPONSE" | python3 -c "
import sys, json
data = json.load(sys.stdin)
try:
print(data['candidates'][0]['content']['parts'][0]['text'])
except:
print('Error extracting style spec')
")
echo "=== Style Verification Complete ==="
echo "$STYLE_SPEC"
echo "$STYLE_SPEC" > "figures/ai_generated/style_spec.txt"
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:
json.dump(payload, f, indent=2)
print("JSON payload created")
PYTHON
# Call Paperbanana API WITHOUT proxy (direct connection works better)
RESPONSE=$(curl -s --max-time 180 \
-X POST "$URL" \
-H 'Content-Type: application/json' \
-d @/tmp/gemini_request.json)
# Check for error
if echo "$RESPONSE" | grep -q '"error"'; then
echo "API Error:"
echo "$RESPONSE" | python3 -m json.tool 2>/dev/null || echo "$RESPONSE"
exit 1
fi
# Extract and save image
echo "$RESPONSE" | python3 << 'PYTHON'
import sys, json, base64
from pathlib import Path
output_dir = Path("figures/ai_generated")
data = json.load(sys.stdin)
try:
parts = data['candidates'][0]['content']['parts']
iteration = 1 # Codex increments this each iteration
for part in parts:
if 'text' in part:
print(f"\n[Paperbanana]: {part['text'][:200]}...")
elif 'inlineData' in part:
img_data = base64.b64decode(part['inlineData']['data'])
img_path = output_dir / f"figure_v{iteration}.png"
with open(img_path, "wb") as f:
f.write(img_data)
print(f"\n✅ Image saved: {img_path}")
print(f" Size: {len(img_data)/1024:.1f} KB")
except Exception as e:
print(f"Parse error: {e}")
print(f"Raw response: {str(data)[:500]}")
PYTHON
Step 5: Codex STRICT Visual Review & Scoring (MANDATORY)
Codex MUST read the generated image and perform a STRICT review:
- Visual Analysis: What does the image show in detail?
- Strengths: What's good about it?
- STRICT Verification: Check EVERY item below
- 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? Content correct?
- [ ] Block 4 "[Name]": Has correct label? Has sub-label? Content correct?
- [ ] Block 5 "[Name]": Has correct label? Has sub-label? Content correct?
- [ ] Block 6 "[Name]": Has correct label? Has sub-label? Content correct?
- [ ] Block 7 "[Name]": Has correct label? Has sub-label? Content correct?
#### C. Arrow Visibility (any failure = score ≤ 7)
- [ ] ALL arrows are THICK (≥5px visible stroke)
- [ ] ALL arrows have CLEAR arrowheads (large triangular heads)
- [ ] ALL arrows are BLACK or DARK GRAY (not light colors)
- [ ] NO arrows are too thin or invisible
#### D. Arrow Labels (any failure = score ≤ 7)
- [ ] EVERY arrow has a text label
- [ ] Labels are readable (not too small)
- [ ] Labels correctly describe the data flowing
#### E. Visual Appeal (科研风格 - Balanced Academic Style) (any failure = score ≤ 8)
- [ ] **有适度视觉吸引力** — 有subtle渐变或圆角，但不夸张
- [ ] **不是平淡方块** — 有一定设计感
- [ ] **不过度装饰** — 没有重阴影、发光效果、彩虹配色
- [ ] **专业学术风格** — 像CVPR论文中的图表，不是PPT模板
- [ ] **Internal structure visible** — 大模块内部显示子组件结构
- [ ] **Color palette: 3-4种协调色** — 不是彩虹，也不是纯黑白
#### E2. Visual Appeal - RED FLAGS (immediate score ≤ 7 if found)
- [ ] **NO heavy drop shadows** (重阴影 = too flashy)
- [ ] **NO glowing effects** (发光效果 = too flashy)
- [ ] **NO rainbow gradients** (彩虹渐变 = unprofessional)
- [ ] **NO excessive decorative icons** (过多装饰图标 = distracting)
#### F. Layout & Flow (any failure = score ≤ 7)
- [ ] Clean horizontal left-to-right flow
- [ ] No arrow crossings
- [ ] Data flow traceable in 5 seconds
- [ ] Balanced spacing (not cramped, not sparse)
#### G. Style Compliance
- [ ] CVPR/NeurIPS professional style
- [ ] Color palette appropriate (not rainbow)
- [ ] Font readable
- [ ] Print-friendly (grayscale test)
### ═══════════════════════════════════════════════════════════════
### Issues Found (BE SPECIFIC)
1. [Issue 1]: [EXACTLY what is wrong] → [How to fix]
2. [Issue 2]: [EXACTLY what is wrong] → [How to fix]
3. [Issue 3]: [EXACTLY what is wrong] → [How to fix]
### Score: X/10
### STRICT Score Breakdown Guide:
- **10**: Perfect. No issues. Publication-ready masterpiece. 视觉风格完美平衡。
- **9**: Excellent. Minor issues that don't affect understanding. 可以直接使用。
- **8**: Good but has noticeable issues. 视觉上太平淡或太花哨都需要改进。
- **7**: Usable but has clear problems. 箭头或内容有问题。
- **6**: Has arrow direction errors (箭头指向错误) OR missing major components.
- **1-5**: Major issues. Unacceptable.
### Visual Style Scoring (视觉风格评分):
- **太花哨 (Too flashy)**: 重阴影、发光效果、彩虹配色 → score ≤ 7
- **太平淡 (Too plain)**: 纯黑白方块、无任何视觉设计 → score ≤ 8
- **恰到好处 (Balanced)**: 适度渐变、圆角、清晰层次 → score 9-10
### Verdict
[ ] ACCEPT (score ≥ 9 AND all critical checks pass)
[ ] REFINE (score < 9 OR any critical check fails)
**If REFINE: List the EXACT issues that must be fixed**
Step 6: Decision Point
IF score >= 9 AND all critical checks pass:
→ Accept figure, generate LaTeX snippet, DONE
ELSE IF iteration < MAX_ITERATIONS:
→ Generate SPECIFIC improvement prompt based on EXACT issues
→ Go to Step 2 (Gemini Layout) with refined prompt
ELSE:
→ Max iterations reached, show best version
→ Ask user if they want to continue or accept
Step 7: Generate Improvement Prompt (for refinement)
Codex generates TARGETED improvement prompt with EXACT issues:
Refine this academic diagram. This is iteration {N}.
## ═══════════════════════════════════════════════════════════════
## CRITICAL: Fix These EXACT Issues (from previous review)
## ═══════════════════════════════════════════════════════════════
### Arrow Direction Errors (MUST FIX):
1. [EXACT issue]: Arrow from [A] to [B] is pointing to wrong target. It should point to [C] instead.
2. [EXACT issue]: ...
### Missing Arrow Labels (MUST FIX):
1. Arrow from [A] to [B] is missing label "[data type]"
2. ...
### Block Content Issues (MUST FIX):
1. Block "[Name]" has wrong label. Should be "[correct label]"
2. ...
### Visual Appeal Issues (SHOULD FIX):
1. Blocks are too plain. Add [gradients/shadows/internal structure]
2. ...
## Keep These Good Elements:
- [What to preserve from previous version]
## Generate the improved figure with ALL issues fixed.
Step 8: Final Output
When figure is accepted (score ≥ 9):
% === AI-Generated Figure ===
\begin{figure*}[t]
\centering
\includegraphics[width=0.95\textwidth]{figures/ai_generated/figure_final.png}
\caption{[Caption based on user's original request].}
\label{fig:[label]}
\end{figure*}
Key Rules (MUST FOLLOW - STRICT)
- NEVER skip the review step — Always read and STRICTLY score the image
- NEVER accept score < 9 — Keep refining until excellence
- VERIFY EVERY ARROW DIRECTION — Wrong direction = automatic fail (score ≤ 6)
- VERIFY EVERY BLOCK CONTENT — Wrong content = automatic fail (score ≤ 7)
- BE SPECIFIC in feedback — "Arrow from A to B points to wrong target C" not "arrow is wrong"
- SAVE all iterations — Keep version history for comparison
- Codex is the STRICT boss — Accept only excellence, not "good enough"
- ARROW CORRECTNESS IS NON-NEGOTIABLE — Any wrong arrow direction = reject
- VISUAL APPEAL MATTERS — Plain boring figures = score ≤ 8
- Target score is 9 — Not 8, not "good enough"
- USE MULTI-STAGE WORKFLOW — Codex → Gemini Layout → Gemini Style → Paperbanana → Codex Review
- USE CORRECT MODELS — gemini-3-pro for reasoning, gemini-3-pro-image-preview for rendering
Output Structure
figures/ai_generated/
├── layout_description.txt # Step 2: Gemini layout optimization output
├── style_spec.txt # Step 3: Gemini style verification output
├── figure_v1.png # Iteration 1 (Paperbanana render)
├── figure_v2.png # Iteration 2
├── figure_v3.png # Iteration 3
├── figure_final.png # Accepted version (copy of best, score ≥ 9)
├── latex_include.tex # LaTeX snippet
└── review_log.json # All review scores and STRICT feedback
Model Summary
| Stage | Model | Purpose |
|---|---|---|
| Step 1 | Codex | Parse request, create initial prompt |
| Step 2 | gemini-3-pro | Layout optimization (positioning, spacing, grouping) |
| Step 3 | gemini-3-pro | CVPR/NeurIPS style verification |
| Step 4 | gemini-3-pro-image-preview (Paperbanana) | High-quality image rendering |
| Step 5 | Codex | STRICT visual review and scoring |