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

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

undefined

**Auto-detection**: The script automatically selects XeLaTeX when detecting ctex, xeCJK, or Chinese characters.

---

python scripts/map_structure.py main.tex

python scripts/check_format.py main.tex
python scripts/check_format.py main.tex --strict

| Original | Improved Version | Issue Type | Optimization Reason |
|------|----------|----------|----------|
| 我们使用了ResNet模型。 | This paper adopts the ResNet model as a feature extractor. | Colloquial Expression | "我们使用" → "This paper adopts" (academic standard); added description of model purpose |
| 效果很好，可以看出性能提升明显。 | Experimental results show that this method has significant performance advantages. | Colloquial + Subjective Expression | Avoid colloquial terms like "很好" (very good) and "可以看出" (it can be seen); use "Experimental results show" to enhance objectivity |
| 显然，这种方法更优越。 | Experimental results show that this method outperforms the baseline method on multiple metrics. | Overly Subjective | Removed "显然" (obviously); supported conclusion with experimental results; clarified comparison object |

% ═══════════════════════════════════════════
% Modification Suggestion (Line 23) [Severity: Major] [Priority: P1]
% ═══════════════════════════════════════════
% Original: 我们使用了ResNet模型。
% Modified: This paper adopts the ResNet model as a feature extractor.
% Improvements:
% 1. "我们使用" → "This paper adopts" (academic standard)
% 2. Added description of model purpose
% Reason: Colloquial expression does not comply with academic standards
% ═══════════════════════════════════════════

% Logical Cohesion (Line 45) [Severity: Major] [Priority: P1]: Logical gap between paragraphs
% Issue: Jumps directly from problem description to solution without transition
% Original: 数据存在噪声。本文提出一种滤波方法。
% Modified: The data contains noise, which interferes with subsequent analysis. Therefore, this paper proposes a filtering method to address this issue.
% Reason: Added 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: 本文采用ResNet作为骨干网络。
% Modified: This paper adopts ResNet as the backbone network, whose residual connection structure can effectively alleviate the gradient vanishing problem and performs excellently in feature extraction tasks.
% Reason: Justified architecture selection with technical principles

% Long & Complex Sentence Detected (Line 45, 87 characters total) [Severity: Minor] [Priority: P2]
% Main Clause: Our method achieves excellent performance on multiple datasets.
% Modifiers:
%   - [Attributive] Deep learning-based
%   - [Method] By introducing attention mechanisms
%   - [Condition] On the premise of ensuring real-time performance
% Suggested Rewrite:
%   This paper proposes a deep learning-based method. By introducing attention mechanisms,
%   this method achieves excellent performance on multiple datasets while ensuring real-time performance.

python scripts/verify_bib.py references.bib
python scripts/verify_bib.py references.bib --tex main.tex    # Check citations
python scripts/verify_bib.py references.bib --standard gb7714 # National standard check

python scripts/detect_template.py main.tex

python scripts/deai_check.py main.tex --section introduction

**Batch Processing** (For entire chapters or documents):
```bash
python scripts/deai_batch.py main.tex --chapter chapter3/introduction.tex
python scripts/deai_batch.py main.tex --all-sections  # Process entire document

% ============================================================
% De-AI Editing (Line 23 - Introduction)
% ============================================================
% Original: 本文提出的方法取得了显著的性能提升。
% Modified: The method proposed in this paper shows performance improvement in experiments.
%
% Modification Notes:
% 1. Removed empty slogan: "显著" (significant) → deleted
% 2. Retained original claim, avoided adding specific metrics or comparison benchmarks
%
% ⚠️  [To Be Verified: Requires experimental data support, add specific metrics]
% ============================================================

\section{Introduction}
The method proposed in this paper shows performance improvement in experiments...

python scripts/deai_check.py main.tex --analyze

Refer to [DEAI_GUIDE.md](references/DEAI_GUIDE.md)

---

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

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

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

**Title Quality Standards** (Based on GB/T 7713.1-2006 and international best practices):

| Standard | Weight | Description |
|------|------|------|
| **Conciseness** | 25% | Remove redundant phrases like "关于...的研究" (A study on...), "...的探索" (An exploration of...), "新型" (new type), "改进的" (improved) |
| **Searchability** | 30% | Core terms (method + problem) appear in the first 20 characters |
| **Length** | 15% | Optimal: 15-25 characters; Acceptable: 10-30 characters |
| **Specificity** | 20% | Specific method/problem names, avoid vague statements |
| **Standardization** | 10% | Comply with academic thesis title standards, avoid obscure abbreviations |

**Title Generation Workflow**:

**Step 1: Content Analysis**
Extract from abstract/introduction:
- **Research Problem**: What challenge is being addressed?
- **Research Method**: What method is proposed?
- **Application Field**: 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 Prediction", "Fault Detection", "Image Segmentation"
- Field Keywords: "Industrial Control", "Medical Imaging", "Autonomous Driving"

**Step 3: Title Template Selection**
Common patterns for academic theses:

| Pattern | Example | Applicable Scenario |
|------|------|----------|
| Research on Problem Based on Method | "Research on Time Series Prediction Method Based on Transformer" | Method innovation |
| Problem and Method in Field | "Graph Neural Network Method for Fault Detection in Industrial Systems" | Application-oriented |
| Method for Problem and Its Application | "Attention Mechanism for Time Series Prediction and Its Application in Industrial Control" | Theory + Application |
| Method Research for Field | "Deep Learning-based Predictive Maintenance Method for Smart Manufacturing" | Field-specific |

**Step 4: Generate Title Candidates**
Generate 3-5 title 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 Vocabulary**:
| Avoid Using | Reason |
|----------|------|
| 关于...的研究 (A study on...) | Redundant (all theses are research) |
| ...的探索 (An exploration of...) | Redundant and non-specific |
| 新型 / 新颖的 (New / Novel) | Publication implies novelty |
| 改进的 / 优化的 (Improved / Optimized) | Non-specific; need to explain how it's improved |
| 基于...的 (Based on...) | Can be simplified to direct expression |

**✅ Recommended Structure**:

**Keyword Layout Strategy**:
- **First 20 characters**: Most important keywords (method + problem)
- **Avoid starting with**: "关于" (about), "对于" (for), "针对" (for) (can be placed in the middle)
- **Prefer using**: Nouns and technical terms, rather than verbs and adjectives

**Abbreviation Usage Guidelines**:
| ✅ Acceptable | ❌ Avoid in Titles |
|----------|--------------|
| AI, Machine Learning, Deep Learning | Lab-specific abbreviations |
| LSTM, GRU, CNN | Chemical formulas (unless extremely common) |
| IoT, 5G, GPS | Non-standard method name abbreviations |
| DNA, RNA, MRI | Obscure field-specific abbreviations |

**University Template Special Requirements**:

**Tsinghua University (thuthesis)**:
- Chinese title: No more than 36 Chinese characters
- English title: Corresponding translation of Chinese title
- Avoid abbreviations and formulas
- Example: "Application of Deep Learning in Predictive Maintenance for Smart Manufacturing"

**Peking University (pkuthss)**:
- Chinese title: Concise, generally no more than 25 characters
- Subtitles allowed (separated by em dash)
- Example: "Graph Neural Network Fault Detection Method — Research for Industrial Control Systems"

**General Requirements (ctexbook)**:
- Comply with GB/T 7713.1-2006 standard
- Chinese title: 15-25 characters is optimal
- English title: Corresponding translation, pay attention to articles and prepositions
- Example: "Transformer-Based Time Series Prediction Method and Application"

**Output Format**:

```latex
% ============================================================
% Title Optimization Report
% ============================================================
% Current Title: "A Study on Time Series Prediction Based on Deep Learning"
% Quality Score: 48/100
%
% Detected Issues:
% 1. [Critical] Contains "关于...的研究" (A study on...) (remove redundant phrase)
% 2. [Important] Method description is too broad ("Deep Learning" is too general)
% 3. [Minor] Length is acceptable (18 characters) but can be more specific
%
% Recommended Titles (Sorted by Score):
%
% 1. "Transformer Method for Time Series Prediction in Industrial Control Systems" [Score: 94/100]
%    - Conciseness: ✅ (19 characters)
%    - Searchability: ✅ (Method + problem in first 15 characters)
%    - Specificity: ✅ (Transformer, instead of "Deep Learning")
%    - Field Relevance: ✅ (Industrial Control Systems)
%    - Standardization: ✅ (Complies with academic thesis standards)
%
% 2. "Attention Mechanism for Multivariate Time Series Prediction" [Score: 89/100]
%    - Conciseness: ✅ (17 characters)
%    - Searchability: ✅ (Core terms at the beginning)
%    - Specificity: ✅ (Attention Mechanism, Multivariate)
%    - Suggestion: Consider adding application field
%
% 3. "Deep Learning Time Series Prediction Method and Its Application in Smart Manufacturing" [Score: 81/100]
%    - Conciseness: ⚠️ (24 characters, acceptable)
%    - Searchability: ✅
%    - Specificity: ⚠️ ("Deep Learning" is still too general)
%    - Field Relevance: ✅ (Smart Manufacturing)
%
% Keyword Analysis:
% - Primary: Transformer, Time Series, Prediction
% - Secondary: Industrial Control, Attention, LSTM
% - Searchability: "Transformer Time Series" appears in 456 papers on CNKI (good balance)
%
% Suggested LaTeX Update:
% \title{Transformer Method for Time Series Prediction in Industrial Control Systems}
% \englishtitle{Transformer-Based Time Series Forecasting for Industrial Control Systems}
% ============================================================

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

**Batch Mode** (Multiple Theses):
```bash
python scripts/optimize_title.py chapters/*.tex --batch --output title_report.txt

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

**Best Practices Summary**:
1. **Keywords First**: Place method + problem in the first 20 characters
2. **Be Specific**: "Transformer" > "Deep Learning" > "Machine Learning"
3. **Remove Redundancy**: Delete "关于" (about), "研究" (study), "新型" (new), "基于" (based on)
4. **Control Length**: Target 15-25 characters (Chinese)
5. **Test Searchability**: Can your thesis be found using these keywords?
6. **Avoid Obscurity**: Only use widely recognized terms (AI, LSTM, CNN)
7. **Comply with Standards**: Follow university templates and GB/T 7713.1-2006 standard

Refer to [GB_STANDARD.md](references/GB_STANDARD.md), [UNIVERSITIES/](references/UNIVERSITIES/)

---

undefined

---