De-AI Writing Guide for Typst Academic Papers

目录

• 目的 (Purpose)
• 核心原则 (Core Principles)
  • 1. Syntax Fidelity (语法保真优先)
  • 2. Zero Fabrication (零捏造)
  • 3. Information Density (提高信息密度)
  • 4. Academic Restraint (克制措辞)
• Academic Humanization Contract
• Common AI Writing Patterns to Remove
  • Category 1: Empty Phrases (空话口号)
  • Category 2: Over-Confident Language (过度确定)
  • Category 3: Mechanical Structures (机械排比)
  • Category 4: Vague Quantification (模糊量化)
  • Category 5: Template Introductions (模板引言)
• Structural-Level Traces (LLM-judgment)
• Section-Specific Guidelines
  • Abstract (摘要)
  • Introduction (引言)
  • Related Work (相关工作)
  • Methods (方法)
  • Results (结果)
  • Discussion (讨论)
  • Conclusion (结论)
• Output Format for De-AI Editing
• Change Categories
• Typst-Specific Syntax Preservation
  • Protected Elements (NEVER Modify)
  • Modifiable Elements (Text Only)
• Quick Reference: Common Replacements
• Bibliography

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目的 (Purpose)

This guide helps reduce AI-generated writing traces while maintaining technical accuracy and Typst syntax integrity.

Target Mode: IEEE TOP期刊 (Mode I) - Concise, precise, and restrained

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核心原则 (Core Principles)

1. Syntax Fidelity (语法保真优先)

• NEVER modify: Typst functions (#set, #show, #let), math environments, citations (@cite), labels (<label>)
• ONLY modify: Visible paragraph text, section titles, caption text
• Preserve: All structural integrity for compilation

2. Zero Fabrication (零捏造)

• NEVER add: New data, metrics, comparisons, experimental settings
• NEVER add: New claims, contributions, or conclusions
• ONLY improve: Expression clarity and natural flow

3. Information Density (提高信息密度)

• Every sentence must convey verifiable information
• Delete empty phrases without substance
• Replace vague claims with specific statements (if available)
• Mark unverifiable claims as [PENDING VERIFICATION]

4. Academic Restraint (克制措辞)

• Avoid over-confident language without evidence
• Use appropriate hedging for speculative claims
• Present contributions objectively, not hyperbolically

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Academic Humanization Contract

Reducing AI tone is not detector evasion. Preserve the Typst paper before smoothing prose:

1. Protect syntax anchors: @cite, <label>, #set, #show, #let, math, code, macros, and source layout.
2. Extract the academic payload: facts/evidence, author stance, section logic, claim-evidence links, and boundaries.
3. Remove Chinese or English structure shells only after the payload is clear.

Default output should be findings, a risk summary, or a rewrite blueprint. Give prose proposals only when the user asks for prose. Mark missing support as [PENDING VERIFICATION] / 待补证; do not invent citations, baselines, metrics, experiments, or conclusions.

Bilingual Structure-Shell Checks

Category	Common trigger	Academic fix
Empty contrast shell	不是 A 而是 B; not merely A but B	Keep only when it names a baseline, criterion, and evidence
Fake insight marker	真正的问题; 本质上; essentially; the key is	Remove the marker and state the evidence-backed claim directly
Lecture colon	我的结论是：; The conclusion is:	Use a normal academic sentence or a concrete inventory noun
Vague referent	这些东西; this shows; things; factors	Name the object, method, mechanism, result, or limitation
Vague comparative	更适合; 更自然	Name the comparison baseline and evaluation criterion

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Common AI Writing Patterns to Remove

Category 1: Empty Phrases (空话口号)

❌ AI-like	✅ Human-like	Notes
significant improvement	reduces error by X%	Use specific numbers
comprehensive analysis	analyzes X, Y, Z	List what was analyzed
effective solution	outperforms baseline by X	State comparison metric
important contribution	proposes method for X	State the contribution
robust performance	maintains accuracy under Y	Specify condition
novel approach	extends X by introducing Y	Explain what's new

Detection Pattern: Look for adjectives that can be replaced with measurable claims.

Category 2: Over-Confident Language (过度确定)

❌ Absolute	✅ Qualified
obviously	the results indicate
clearly	evidence suggests
necessarily	under these conditions
completely	in most cases
undoubtedly	appears to be
always	consistently in our experiments
never	rarely observed

Detection Pattern: Absolute claims without qualification or evidence.

For graded conservative-wording tables (causal / firstness / universality / effect-size / application), see OVER_CLAIM_GUARD.md.

Category 3: Mechanical Structures (机械排比)

Three-part parallelisms without substance: ❌ "Our method is fast, accurate, and efficient." ✅ "Our method processes 1000 samples/sec with 95% accuracy."

Template transitions: ❌ "In recent years, deep learning has developed rapidly." ✅ "Deep learning has achieved state-of-the-art performance in X since 2020."

Generic openings: ❌ "With the rapid development of technology..." ✅ Start directly with the specific problem context.

Detection Pattern: Phrases that could apply to any paper in any field.

Category 4: Vague Quantification (模糊量化)

❌ Vague	✅ Specific
many studies	three recent studies @ref1 @ref2 @ref3
numerous experiments	experiments on X datasets
substantial gain	12% improvement
the majority	78% of cases
significantly better	outperforms by p<0.01

Detection Pattern: Quantifiers without actual numbers or references.

Category 5: Template Introductions (模板引言)

❌ "Time series forecasting is an important problem with wide applications." ✅ "Time series forecasting is critical for supply chain optimization @ref1, energy management @ref2, and financial planning @ref3."

❌ "Machine learning has revolutionized many fields." ✅ "Machine learning has improved prediction accuracy in healthcare @ref1, manufacturing @ref2, and finance @ref3."

Detection Pattern: Broad generalizations that could be in any textbook.

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Structural-Level Traces (LLM-judgment)

These are not word- or sentence-level tells — they live in document structure, so a script cannot catch them. Judge them by reading the whole draft, and tag any finding [LLM].

1. Over-symmetric IMRAD — every section padded to the same shape (introduction always 4 paragraphs; discussion always "recap + compare + implication + limitation"). Real papers are uneven: some sections short, some long. Signal: paragraph counts symmetric across sections.
2. Declarative scaffolding transitions — "Having established X, we next turn to Y." / "With this in hand, we proceed to…". Real writing transitions implicitly: the next sentence enters the new topic without announcing it.
3. Stance-less discussion — pros and cons each listed but none committed to. Real authors take a position ("we consider X more plausible than Y because…").
4. Uniform paragraph length — 80% of paragraphs are 5–7 sentences. Real cadence varies: a 3-sentence emphasis paragraph next to a 10-sentence argument.

How to fix: break the symmetry — merge thin paragraphs, split overloaded ones, delete announced transitions, and make the discussion commit to a view.

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Section-Specific Guidelines

Abstract (摘要)

Structure: Purpose → Method → Key Results (with numbers) → Conclusion

Common AI Traps:

• ❌ "We propose a novel approach for..."

• ✅ "We propose an attention-based mechanism for..."

• ❌ "Experimental results show significant improvements."

• ✅ "On dataset X, our method reduces MAE by 12% compared to the baseline."

• ❌ "This work has important implications for..."

• ✅ "This method enables real-time forecasting with <10ms latency."

Constraints:

• No generic claims ("novel", "significant", "important") without specifics
• Include concrete numbers for key results
• State specific contributions, not general value

Example:

// ❌ AI-like
This paper proposes a novel deep learning approach for time series
forecasting. The method achieves significant performance improvements
over existing methods. Experimental results demonstrate the effectiveness
of our approach.

// ✅ Human-like
This paper proposes an attention-based mechanism for multivariate time
series forecasting. Our method reduces MAE by 12% on the UCR archive
compared to the Transformer baseline @ref1. Experimental results show that
the attention mechanism improves long-term dependency capture.

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Introduction (引言)

Structure: Importance → Gap → Contribution → Organization

Common AI Traps:

• ❌ "Time series forecasting plays an important role in modern society."

• ✅ "Time series forecasting is critical for energy grid optimization @ref1."

• ❌ "However, existing methods have limitations."

• ✅ "However, existing methods fail to capture long-term dependencies in noisy environments @ref2 @ref3."

• ❌ "Our main contributions are as follows:"

• ✅ "This paper makes three contributions:"

Contribution Statement Rules:

• Each contribution must be verifiable
• Avoid "novel", "first", "state-of-the-art" without evidence
• State what you did, not how important it is

Example:

// ❌ AI-like
Time series forecasting is very important. Many researchers study this
problem. However, existing methods have some limitations. This paper
proposes a novel method with significant improvements.

// ✅ Human-like
Time series forecasting enables proactive decision-making in energy
management @ref1 and supply chain optimization @ref2. Recent approaches
based on Transformers @ref3 @ref4 show promise but struggle with noisy
data @ref5. This paper proposes a noise-robust attention mechanism that
reduces prediction error by 12% compared to standard Transformers.

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Related Work (相关工作)

Structure: Categorize → Compare → Position

Common AI Traps:

• ❌ "Smith et al. proposed a method. It is good."

• ✅ "@smith2020 proposed X, which achieves Y accuracy on dataset Z."

• ❌ "Existing methods can be divided into two types: A and B."

• ✅ "Existing methods follow two paradigms: statistical approaches @ref1 @ref2 @ref3 and deep learning approaches @ref4 @ref5 @ref6."

• ❌ "Our method is different from them."

• ✅ "Unlike @ref1 @ref2, our method incorporates attention mechanisms to..."

Guidelines:

• Group by approach/paradigm, not chronologically
• Compare specific technical differences
• State what you do differently
• Avoid vague praise ("excellent", "outstanding")

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Methods (方法)

Structure: Overview → Details → Algorithm → Complexity

Common AI Traps:

• ❌ "We use a neural network. It is very powerful."

• ✅ "We use a 3-layer LSTM with 256 hidden units."

• ❌ "The algorithm works well."

• ✅ "The algorithm converges within 100 epochs."

• ❌ "The model has good performance."

• ✅ "The model processes 1000 samples/second."

Guidelines:

• Provide implementation details for reproducibility
• State hyperparameters and architecture choices
• Include algorithm complexity if relevant
• Focus on what you did, not how well it works (that's Results)

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Results (结果)

Structure: Main results → Ablation → Analysis

Common AI Traps:

• ❌ "Our method performs much better than baselines."

• ✅ "Our method reduces MAE by 12% compared to the best baseline."

• ❌ "The results demonstrate the effectiveness of our method."

• ✅ "@tab:results shows that our method achieves lowest MAE on 4/5 datasets."

• ❌ "We can see from Figure 2 that our method is superior."

• ✅ "@fig:comparison shows that our method maintains accuracy with 50% less training data."

Guidelines:

• Report facts and numbers only
• Don't explain why (that's Discussion)
• Avoid interpretive language ("superior", "outperforms" without numbers)
• Let tables/figures speak for themselves

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Discussion (讨论)

Structure: Interpretation → Mechanism → Limitations → Future Work

Common AI Traps:

• ❌ "The good performance proves our method is excellent."

• ✅ "The improved accuracy suggests that attention mechanisms capture long-term dependencies."

• ❌ "Our method has no limitations."

• ✅ "Our method requires more training time (2.3 hours vs 1.5 hours for baselines)."

• ❌ "Future work includes more experiments."

• ✅ "Future work will explore the attention mechanism's interpretability."

Guidelines:

• Explain mechanisms, not just outcomes
• Acknowledge failures and boundary conditions
• State limitations honestly
• Propose specific future work

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Conclusion (结论)

Structure: Summary → Answer research question → Future work

Common AI Traps:

• ❌ "In this paper, we proposed a novel method that achieved significant improvements."

• ✅ "This paper proposed an attention-based mechanism that reduces MAE by 12%."

• ❌ "Our work has important theoretical and practical value."

• ✅ "This work enables real-time forecasting with limited computational resources."

• ❌ "In the future, we will continue to improve our method."

• ✅ "Future work will extend this method to multivariate time series with missing data."

Guidelines:

• Answer the research question directly
• No new results or claims
• No new experiments
• Specific, actionable future work

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Output Format for De-AI Editing

// ============================================================
// DE-AI EDITING (Line X - [Section Name])
// ============================================================
// Original: [AI-like text]
// Revised: [Human-like text]
//
// Changes:
// 1. [Type of change]: [details]
// 2. [Type of change]: [details]
//
// ⚠️ [PENDING VERIFICATION]: [claim needing evidence]
// ============================================================

[revised source code]

Change Categories

1. Removed empty phrase: Deleted vague adjective/adverb
2. Added specificity: Replaced vague with concrete
3. Split long sentence: Divided sentence >50 words
4. Reordered structure: Improved logical flow
5. Downgraded claim: Added appropriate hedging
6. Deleted redundancy: Removed repetitive content
7. Added subject: Inserted missing grammatical subject
8. Fixed template expression: Replaced generic with specific

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Typst-Specific Syntax Preservation

Protected Elements (NEVER Modify)

1. Function Calls

#set text(...)      // NEVER modify
#show heading: ...  // NEVER modify
#let func = ...     // NEVER modify

2. Citations and References

@smith2020          // NEVER modify citation keys
<fig:example>       // NEVER modify labels
@fig:example        // NEVER modify cross-references

3. Math Environments

$x^2 + y^2 = z^2$   // NEVER modify math content
$ integral x dif x $ // NEVER modify display math

4. Markup Syntax

*bold*              // Can modify text, keep markup
_italic_            // Can modify text, keep markup
`code`              // NEVER modify code content

Modifiable Elements (Text Only)

1. Paragraph Text

// ✅ Can modify
This method achieves significant improvements.
→ This method reduces error by 12%.

2. Heading Text

// ✅ Can modify text, keep syntax
= Novel Approach for Time Series
→ = Attention-Based Mechanism for Time Series

3. Caption Text

// ✅ Can modify text inside caption
#figure(
  ...,
  caption: [This shows significant improvements.]
)
→
#figure(
  ...,
  caption: [This shows 12% error reduction.]
)

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Quick Reference: Common Replacements

❌ Remove	✅ Replace With
significant improvement	[specific metric + number]
comprehensive study	analyze X, Y, Z
effective solution	outperforms baseline by X%
novel approach	extends X by introducing Y
robust performance	maintains accuracy under [condition]
clearly/obviously	evidence suggests / results indicate
many studies	[number] studies [citations]
in recent years	since [year] / in [specific period]
more and more	increasingly / growing from X to Y
play an important role	enables / is critical for / is essential to

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Bibliography

This guide should be used together with:

• STYLE_GUIDE.md: General academic writing rules
• COMMON_ERRORS.md: Chinglish patterns to avoid
• VENUES.md: Venue-specific requirements
• TYPST_SYNTAX.md: Typst syntax reference