// <Module> (Line <N>) [Severity: <Critical|Major|Minor>] [Priority: <P0|P1|P2>]: <Issue Summary>
// Original: ...
// Revised: ...
// Rationale: ...
// ⚠️ [To Be Verified]: <Mark when evidence/data is needed>

// ERROR [Severity: Critical] [Priority: P0]: <Brief Error>
// Cause: <Missing tool or invalid path>
// Suggestion: <Install tool/verify path/retry command>

undefined

**Compilation Speed Advantages**:
- Typst compilation speed is usually in milliseconds (vs LaTeX's seconds)
- Incremental compilation: Only recompile modified parts
- Suitable for real-time preview and rapid iteration

**Chinese Support**:
```typst
// Example of Chinese font configuration
#set text(
  font: ("Source Han Serif", "Noto Serif CJK SC"),
  lang: "zh",
  region: "cn"
)

// Page settings
#set page(
  paper: "a4",  // or "us-letter"
  margin: (x: 2.5cm, y: 2.5cm)
)

// Text settings
#set text(
  font: "Times New Roman",
  size: 11pt,
  lang: "en"
)

// Paragraph settings
#set par(
  justify: true,
  leading: 0.65em,
  first-line-indent: 1.5em
)

// Heading settings
#set heading(numbering: "1.1")

// GRAMMAR (Line 23) [Severity: Major] [Priority: P1]: Missing article
// Original: We propose method for...
// Revised: We propose a method for...
// Rationale: Indefinite article missing before singular countable noun

// Complex Sentence Detected (Line 45, 67 words total) [Severity: Minor] [Priority: P2]
// Main Structure: [Subject + Predicate + Object]
// Modifiers:
//   - [Relative Clause] which...
//   - [Purpose Adverbial] to...
// Suggested Rewrite: [Simplified Version]

| Original / 原文 | Revised / 改进版本 | Issue Type / 问题类型 | Rationale / 优化理由 |
|-----------------|---------------------|----------------------|---------------------|
| We use machine learning to get better results. | We employ machine learning to achieve superior performance. | Weak verbs | Replace "use" → "employ", "get" → "achieve" for academic tone |
| 我们使用了深度学习方法。 | 本文采用深度学习方法进行特征提取。 | Colloquial expression | "我们使用" → "本文采用" (academic standard); supplement method purpose |

// EXPRESSION (Line 23) [Severity: Minor] [Priority: P2]: Enhance academic tone
// Original: We use machine learning to get better results.
// Revised: We employ machine learning to achieve superior performance.
// Rationale: Replace weak verbs with academic alternatives

// Logical Cohesion (Line 45) [Severity: Major] [Priority: P1]: Logical gap between paragraphs
// Issue: Direct jump from problem description to solution without transition
// Original: The data has noise. This paper proposes a filtering method.
// Revised: The data has noise, which interferes with subsequent analysis. Therefore, this paper proposes a filtering method to solve this problem.
// Rationale: Add causal transition to connect problem and solution

// Methodology Depth (Line 78) [Severity: Major] [Priority: P1]: Lack of justification for method selection
// Issue: No explanation for method selection
// Original: This paper uses ResNet as the backbone network.
// Revised: This paper uses ResNet as the backbone network, whose residual connection structure can effectively alleviate the vanishing gradient problem and performs excellently in feature extraction tasks.
// Rationale: Justify architecture selection with technical principles

| Chinese | English | Domain |
|---------|---------|--------|
| 注意力机制 | attention mechanism | DL |
| 时间序列预测 | time series forecasting | TS |

// Original: 本文提出了一种基于Transformer的方法
// Translation: We propose a Transformer-based approach
// Annotation: "本文提出" → "We propose" (academic standard expression)

#bibliography("references.bib", style: "ieee")

#bibliography("references.yml", style: "apa")

// In-text citation
According to @smith2020, the method...
Recent studies @smith2020 @jones2021 show...

// Bibliography list
#bibliography("references.bib", style: "ieee")

// ============================================================
// De-AI Editing (Line 23 - Introduction)
// ============================================================
// Original: This method achieves significant performance improvement.
// Revised: The proposed method improves performance in the experiments.
//
// Modification Explanation:
// 1. Remove empty words: "significant" → deleted
// 2. Retain original assertion, avoid adding specific metrics
//
// ⚠️ [To Be Verified: Experimental data support needed, add specific metrics]
// ============================================================

= Introduction
The proposed method improves performance in the experiments...

python scripts/optimize_title.py main.typ --generate

**Optimize Existing Title**:
```bash
python scripts/optimize_title.py main.typ --optimize

**Check Title Quality**:
```bash
python scripts/optimize_title.py main.typ --check

**Title Quality Standards** (based on IEEE Author Center and top conferences/journals):

| Standard | Weight | Description |
|----------|--------|-------------|
| **Conciseness** | 25% | Delete "A Study of", "Research on", "Novel", "New" |
| **Searchability** | 30% | Core terms (method + problem) within the first 65 characters |
| **Length** | 15% | Optimal: 10-15 words (English) / 15-25 characters (Chinese) |
| **Specificity** | 20% | Specific method/problem names, avoid vague statements |
| **Normativity** | 10% | Avoid obscure abbreviations (except common ones like AI, LSTM, DNA) |

**Title Generation Workflow**:

**Step 1: Content Analysis**
Extract from abstract/introduction:
- **Research Problem**: What challenge is solved?
- **Research Method**: What method is proposed?
- **Application Domain**: What application scenario?
- **Core Contribution**: What are the main outcomes? (optional)

**Step 2: Keyword Extraction**
Identify 3-5 core keywords:
- Method Keywords: "Transformer", "Graph Neural Network", "Reinforcement Learning"
- Problem Keywords: "Time Series Forecasting", "Fault Detection", "Image Segmentation"
- Domain Keywords: "Industrial Control", "Medical Imaging", "Autonomous Driving"

**Step 3: Title Template Selection**
Common patterns used by top conferences/journals:

| Pattern | English Example | Chinese Example | Applicable Scenario |
|---------|-----------------|-----------------|---------------------|
| Method for Problem | "Transformer for Time Series Forecasting" | "Transformer Method for Time Series Forecasting" | General research |
| Method: Problem in Domain | "Graph Neural Networks: Fault Detection in Industrial Systems" | "Graph Neural Networks: Fault Detection in Industrial Systems" | Domain-specific research |
| Problem via Method | "Time Series Forecasting via Attention Mechanisms" | "Time Series Forecasting via Attention Mechanisms" | Method-focused research |
| Method + Key Feature | "Lightweight Transformer for Real-Time Detection" | "Lightweight Transformer for Real-Time Detection" | Performance-focused research |

**Step 4: Generate Title Candidates**
Generate 3-5 candidates with different focuses:
1. Method-focused
2. Problem-focused
3. Application-focused
4. Balanced (recommended)
5. Concise variant

**Step 5: Quality Scoring**
Each candidate title receives:
- Overall score (0-100)
- Sub-scores for each standard
- Specific improvement suggestions

**Title Optimization Rules**:

**❌ Remove Invalid Words**:

**English**:
| Avoid Using | Reason |
|-------------|--------|
| A Study of | Redundant (all papers are studies) |
| Research on | Redundant (all papers are research) |
| Novel / New | Implied by publication |
| Improved / Enhanced | Vague without specifics |
| Based on | Often unnecessary |
| Using / Utilizing | Can be replaced with prepositions |

**Chinese**:
| Avoid Using | Reason |
|-------------|--------|
| 关于...的研究 | Redundant (all papers are research) |
| ...的探索 | Redundant and unspecific |
| 新型 / 新颖的 | Publication implies novelty |
| 改进的 / 优化的 | Unspecific, need to explain how improved |
| 基于...的 | Can be simplified to direct expression |

**✅ Recommended Structure**:

**English Examples**:

**Chinese Examples**:

**Keyword Layout Strategy**:
- **First 65 characters (English) / First 20 characters (Chinese)**: Most important keywords (method + problem)
- **Avoid starting with**: Articles (A, An, The) / "关于", "对于"
- **Prefer using**: Nouns and technical terms, rather than verbs and adjectives

**Abbreviation Usage Guidelines**:
| ✅ Acceptable | ❌ Avoid in Title |
|---------------|-------------------|
| AI, ML, DL | Obscure domain-specific acronyms |
| LSTM, GRU, CNN | Chemical formulas (unless very common) |
| IoT, 5G, GPS | Lab-specific abbreviations |
| DNA, RNA, MRI | Non-standard method names |

**Conference/Journal Specific Requirements**:

**IEEE Transactions**:
- Avoid subscripted formulas (unless very simple, e.g., "Nd–Fe–B")
- Use Title Case (first letter of major words capitalized)
- Typical length: 10-15 words
- Example: "Deep Learning for Predictive Maintenance in Smart Manufacturing"

**ACM Conferences**:
- Can use more creative titles
- Can use colons to add subtitles
- Typical length: 8-12 words
- Example: "AttentionFlow: Visualizing Attention Mechanisms in Neural Networks"

**Springer Journals**:
- Prefer descriptive over creative titles
- Can be slightly longer (up to 20 words)
- Example: "A Comprehensive Framework for Real-Time Anomaly Detection in Industrial IoT Systems"

**NeurIPS/ICML**:
- Concise and impactful (8-12 words)
- Method names are usually highlighted
- Example: "Transformers Learn In-Context by Gradient Descent"

**Output Format**:

**English Papers**:
```typst
// ============================================================
// TITLE OPTIMIZATION REPORT
// ============================================================
// Current Title: "A Novel Study on Time Series Forecasting Using Deep Learning"
// Quality Score: 45/100
//
// Issues Detected:
// 1. [Critical] Contains "Novel Study" (remove ineffective words)
// 2. [Major] Vague method description ("Deep Learning" too broad)
// 3. [Minor] Length acceptable (9 words) but could be more specific
//
// Recommended Titles (Ranked):
//
// 1. "Transformer-Based Time Series Forecasting for Industrial Control" [Score: 92/100]
//    - Concise: ✅ (8 words)
//    - Searchable: ✅ (Method + Problem in first 50 chars)
//    - Specific: ✅ (Transformer, not just "Deep Learning")
//    - Domain: ✅ (Industrial Control)
//
// 2. "Attention Mechanisms for Multivariate Time Series Prediction" [Score: 88/100]
//    - Concise: ✅ (7 words)
//    - Searchable: ✅ (Key terms upfront)
//    - Specific: ✅ (Attention, Multivariate)
//    - Note: Consider adding domain if space allows
//
// 3. "Deep Learning Approach to Time Series Forecasting in Smart Manufacturing" [Score: 78/100]
//    - Concise: ⚠️ (10 words, acceptable)
//    - Searchable: ✅
//    - Specific: ⚠️ ("Deep Learning" still broad)
//    - Domain: ✅ (Smart Manufacturing)
//
// Keyword Analysis:
// - Primary: Transformer, Time Series, Forecasting
// - Secondary: Industrial Control, Attention, LSTM
// - Searchability: "Transformer Time Series" appears in 1,234 papers (good balance)
//
// Suggested Typst Update:
// #align(center)[
//   #text(size: 18pt, weight: "bold")[
//     Transformer-Based Time Series Forecasting for Industrial Control
//   ]
// ]
// ============================================================

// ============================================================
// TITLE OPTIMIZATION REPORT
// ============================================================
// Current Title: "A Study on Time Series Forecasting Based on Deep Learning"
// Quality Score: 48/100
//
// Issues Detected:
// 1. [Critical] Contains "A Study on..." (remove redundant words)
// 2. [Major] Method description is too broad ("Deep Learning" is too vague)
// 3. [Minor] Length is acceptable (18 characters) but can be more specific
//
// Recommended Titles (Ranked by Score):
//
// 1. "Transformer Method for Time Series Forecasting in Industrial Control Systems" [Score: 94/100]
//    - Conciseness: ✅ (19 characters)
//    - Searchability: ✅ (Method + Problem in first 15 characters)
//    - Specificity: ✅ (Transformer, not just "Deep Learning")
//    - Domain: ✅ (Industrial Control Systems)
//
// 2. "Attention Mechanism for Multivariate Time Series Prediction" [Score: 89/100]
//    - Conciseness: ✅ (17 characters)
//    - Searchability: ✅ (Core terms upfront)
//    - Specificity: ✅ (Attention Mechanism, Multivariate)
//    - Suggestion: Consider adding application domain
//
// Suggested Typst Update:
// #align(center)[
//   #text(size: 18pt, weight: "bold", font: "Source Han Serif")[
//     Transformer Method for Time Series Forecasting in Industrial Control Systems
//   ]
// ]
// ============================================================

python scripts/optimize_title.py main.typ --interactive

**Batch Mode** (multiple papers):
```bash
python scripts/optimize_title.py papers/*.typ --batch --output title_report.txt

python scripts/optimize_title.py main.typ --compare "Title A" "Title B" "Title C"

**Best Practices Summary**:

**English Papers**:
1. **Keywords upfront**: Method + Problem in the first 10 words
2. **Be specific**: "Transformer" > "Deep Learning" > "Machine Learning"
3. **Remove redundancy**: Delete "Novel", "Study", "Research", "Based on"
4. **Control length**: Target 10-15 words
5. **Test searchability**: Can you find your paper using these keywords?
6. **Avoid obscurity**: Unless it's a widely recognized abbreviation (AI, LSTM, CNN)
7. **Match conference style**: IEEE (descriptive), ACM (creative), NeurIPS (concise)

**Chinese Papers**:
1. **Keywords upfront**: Method + Problem in the first 20 characters
2. **Be specific**: "Transformer" > "Deep Learning" > "Machine Learning"
3. **Remove redundancy**: Delete "关于", "研究", "新型", "基于"
4. **Control length**: Target 15-25 characters
5. **Test searchability**: Can you find your paper using these keywords?
6. **Avoid obscurity**: Unless it's a widely recognized term (AI, LSTM, CNN)
7. **Chinese-English alignment**: Ensure English title corresponds to Chinese title

**Typst Title Setting Examples**:

**English Papers**:
```typst
#align(center)[
  #text(size: 18pt, weight: "bold")[
    Transformer-Based Time Series Forecasting for Industrial Control
  ]
]

#align(center)[
  #text(size: 18pt, weight: "bold", font: "Source Han Serif")[
    Transformer Method for Time Series Forecasting in Industrial Control Systems
  ]
  
  #v(0.5em)
  
  #text(size: 14pt, font: "Times New Roman")[
    Transformer-Based Time Series Forecasting for Industrial Control Systems
  ]
]