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+# Writing Techniques and Patterns
+
+This file contains actionable sentence patterns, transition phrases, and writing techniques extracted from successful ML conference papers.
+
+---
+
+## Transition Phrases
+
+### Literature Review Transitions
+**Source:** Various NeurIPS/ICML papers
+
+**Introducing Problems:**
+- "However, these methods suffer from [limitation]."
+- "Despite recent progress, [challenge] remains unsolved."
+- "While existing approaches address [aspect], they struggle with [issue]."
+
+**Presenting Solutions:**
+- "To address this, we propose..."
+- "We overcome this limitation by..."
+- "Our key insight is that..."
+
+**Connecting to Related Work:**
+- "Building on [prior work], we extend..."
+- "Unlike approaches that [method], we instead..."
+- "Following the success of [paper], we apply..."
+
+### Methods Section Transitions
+**Source:** "BERT: Pre-training of Deep Bidirectional Transformers", NAACL (2019)
+
+**Describing Components:**
+- "Our model consists of two main components: [A] and [B]."
+- "We divide our approach into [N] stages: [list]."
+
+**Explaining Rationale:**
+- "We choose this architecture because..."
+- "This formulation allows us to..."
+- "Motivated by [intuition], we design..."
+
+### Results Section Transitions
+**Source:** "Attention Is All You Need", NeurIPS (2017)
+
+**Presenting Findings:**
+- "Our method achieves [result], outperforming baselines by [margin]."
+- "As shown in Table 1, our approach..."
+- "Figure 2 demonstrates that..."
+
+**Analyzing Results:**
+- "These results suggest that [insight]."
+- "Notably, we observe that..."
+- "This improvement indicates that..."
+
+### Discussion Transitions
+**Source:** "Language Models are Few-Shot Learners", GPT-3 (2020)
+
+**Interpreting Findings:**
+- "These findings reveal that..."
+- "This performance gap suggests that..."
+- "The strong correlation between...indicates..."
+
+**Connecting to Broader Context:**
+- "Beyond the specific task, our results imply..."
+- "This has important implications for..."
+
+**Acknowledging Limitations:**
+- "It is important to note that our study is limited to..."
+- "While these results are promising, several questions remain..."
+
+---
+
+## Sentence Patterns
+
+### Claim Presentation
+**Source:** "Attention Is All You Need", NeurIPS (2017)
+
+**Strong Claims:**
+- "We show that [approach] achieves [result]."
+- "We demonstrate that [method] outperforms..."
+- "We prove that [technique] converges to..."
+
+**Nuanced Claims:**
+- "Our results suggest that [factor] contributes to..."
+- "We observe that [phenomenon] emerges when..."
+- "Experiments indicate that [approach] is particularly effective for..."
+
+### Technical Description
+**Source:** "Adam: A Method for Stochastic Optimization", ICLR (2015)
+
+**Algorithm Description:**
+- "Formally, we optimize [objective] using [method]."
+- "The update rule for [parameter] is given by..."
+- "We modify the standard [approach] by..."
+
+**Implementation Details:**
+- "In practice, we implement [feature] as..."
+- "For computational efficiency, we approximate..."
+- "We initialize [parameters] using..."
+
+### Results Presentation
+**Source:** "BERT: Pre-training of Deep Bidirectional Transformers", NAACL (2019)
+
+**Quantitative Results:**
+- "Our model achieves [score] (±[std]), improving over..."
+- "On [dataset], we obtain [result], compared to..."
+- "We observe a [percentage]% improvement over baselines."
+
+**Statistical Reporting:**
+- "Results are averaged over N runs with different seeds."
+- "Standard deviations are shown in parentheses."
+- "The improvement is statistically significant (p<0.01)."
+
+---
+
+## Clarity Techniques
+
+### Active Voice Usage
+**Source:** Various well-written papers
+
+**Passive (avoid):**
+- "The model was trained using..."
+- "Experiments were conducted on..."
+
+**Active (prefer):**
+- "We trained the model using..."
+- "We conducted experiments on..."
+
+**Guideline:** Use active voice for actions you performed. Use passive for general facts or when the actor is unclear.
+
+### Specificity Over Generality
+**Source:** "Attention Is All You Need", NeurIPS (2017)
+
+**Vague (avoid):**
+- "This approach improves performance."
+- "The method learns good representations."
+
+**Specific (prefer):**
+- "This approach improves accuracy by 15%."
+- "The method learns representations that transfer to downstream tasks."
+
+**Guideline:** Be quantitative whenever possible. Use specific numbers and metrics.
+
+### Signposting
+**Source:** "BERT: Pre-training of Deep Bidirectional Transformers", NAACL (2019)
+
+**Section Openings:**
+- "We now describe our model architecture."
+- "We evaluate on three tasks: [list]."
+- "The results suggest three key insights:"
+
+**Internal Structure:**
+- "First, we [action]. Next, we [action]. Finally, we [action]."
+- "Our approach has three stages: [A], [B], and [C]."
+
+**Guideline:** Use explicit signposting to help tired reviewers follow your paper.
+
+---
+
+## Common Phrase Templates
+
+### Opening Abstract
+**Good Examples:**
+- "We introduce [method], a novel approach for [task]."
+- "We present [method], which achieves [result] by [mechanism]."
+- "We propose [framework] to address [challenge]."
+
+**Avoid:**
+- "In this paper, we study..." (generic)
+- "Large language models have..." (overused opening)
+
+### Introducing Related Work
+**Good Examples:**
+- "Recent work has shown promise in [area] [refs]."
+- "Several approaches have been proposed for [task] [refs]."
+- "The standard approach to [problem] is [method] [refs]."
+
+### Describing Experiments
+**Good Examples:**
+- "We evaluate on [datasets], comparing against [baselines]."
+- "We conduct ablation studies to validate [component]."
+- "To verify [claim], we experiment with [variations]."
+
+### Presenting Results
+**Good Examples:**
+- "Table 1 shows that our method outperforms all baselines."
+- "As shown in Figure 3, performance improves as [factor] increases."
+- "Our method achieves state-of-the-art on [task/metric]."
+
+### Discussing Limitations
+**Good Examples:**
+- "Our approach has limitations: [constraint]."
+- "We note that our method is currently restricted to [condition]."
+- "A key limitation is [issue], which we leave for future work."
+
+---
+
+## Writing Principles
+
+### From Top Papers
+
+**Clarity First:**
+- "Make it easy for reviewers to understand your contribution."
+- "Use concrete examples and specific language."
+- "Avoid vague or ambiguous statements."
+
+**Rigorous Presentation:**
+- "Provide enough detail for reproduction."
+- "Include error bars and statistical tests."
+- "Show negative results when relevant."
+
+**Storytelling:**
+- "Your paper tells a story: problem → approach → solution → impact."
+- "Make the narrative clear in the introduction."
+- "Each section should advance the story."
+
+**Honesty:**
+- "Acknowledge limitations explicitly."
+- "Don't overclaim results."
+- "Trust reviewers to appreciate honesty."
+
+---
+
+## Notes
+
+- **Adapt patterns**: These templates can and should be adapted to your specific context
+- **Venue matters**: Some venues prefer certain styles (check venue-specific guides)
+- **Consistency**: Use consistent terminology throughout
+- **Tone**: Maintain professional, objective tone
+- **Length**: Keep transitions concise; don't over-explain
+
+**Attribution:** All patterns extracted from analyzed papers with source citations for traceability.
+
+
+---
+
+## "Surprisingly" Findings: Multi-Level Reporting Pattern
+
+**Source**: Kaiming He et al., "Exploring Plain Vision Transformer Backbones for Object Detection" (ViTDet, ECCV 2022), "Mean Flows" (2025)
+
+**Paper Type**: Design simplification, unexpected findings
+
+### The Three-Level "Surprisingly" Pattern
+
+#### Level 1: Basic Surprise (Abstract/Opening)
+
+**Pattern**:
+```markdown
+Surprisingly, we observe: (i) [simple sufficient without common practice]
+and (ii) [simple sufficient without common practice]
+```
+
+**Example (ViTDet Abstract)**:
+```latex
+Surprisingly, we observe: (i) it is sufficient to build a simple feature
+pyramid from a single-scale feature map (without the common FPN design) and
+(ii) it is sufficient to use window attention (without shifting) aided with
+very few cross-window propagation blocks.
+```
+
+**Key Techniques**:
+- **Structured list**: Use (i) and (ii) to separate findings
+- **"sufficient"**: Scientific phrasing (not "optimal")
+- **"without [common practice]"**: Negative differentiation
+
+#### Level 2: Competitive Surprise (Introduction)
+
+**Pattern**:
+```markdown
+More surprisingly, under some circumstances, our [method] can compete
+with the leading [competitors].
+```
+
+**Example (ViTDet Introduction)**:
+```latex
+More surprisingly, under some circumstances, our plain-backbone detector,
+named ViTDet, can compete with the leading hierarchical-backbone detectors
+(e.g., Swin, MViT).
+```
+
+**Key Techniques**:
+- **"More surprisingly"**: Progressive emphasis
+- **"under some circumstances"**: Measured claim
+- **"can compete with"**: Not "beat", competitive
+- **Name competitors**: Specific (Swin, MViT)
+
+#### Level 3: Superiority Surprise (Results)
+
+**Pattern**:
+```markdown
+With [specific condition], our [method] can outperform the [competitors]
+that use [stronger condition]. The gains are more prominent for [condition].
+```
+
+**Example**:
+```latex
+With Masked Autoencoder (MAE) pre-training, our plain-backbone detector can
+outperform the hierarchical counterparts that are pre-trained on ImageNet-1K/21K
+with supervision (Figure 3). The gains are more prominent for larger model sizes.
+```
+
+**Key Techniques**:
+- **Specific conditions compared**: MAE vs ImageNet supervised
+- **"outperform"**: Stronger claim here (qualified by conditions)
+- **"The gains are more prominent for..."**: Pattern observation
+
+---
+
+### "Surprisingly" Variants
+
+#### "Interestingly" - Pattern Observation + Explanation
+
+**Pattern**:
+```markdown
+Interestingly, [observation]. This is in line with the observation in [paper]
+that [their finding]. [Additional explanation].
+```
+
+**Example (ViTDet)**:
+```latex
+Interestingly, performing propagation in the last 4 blocks is nearly as
+good as even placement. This is in line with the observation in ViT [14]
+that ViT has longer attention distance in later blocks and is more localized
+in earlier ones.
+```
+
+**Use when**: You have literature support for your observation
+
+#### "Notably" - Important Detail
+
+**Pattern**:
+```markdown
+Notably, [counter-intuitive result or impressive number].
+```
+
+**Examples**:
+- "Notably, even embedding only the interval t−r yields reasonable results."
+- "Notably, our method is self-contained and trained entirely from scratch."
+
+**Use when**: Emphasizing importance or counter-intuitive finding
+
+#### "It is worth noting that" - Caveat/Clarification
+
+**Pattern**:
+```markdown
+It is worth noting that [technical caveat or clarification].
+```
+
+**Examples**:
+- "It is worth noting that even when the conditional flows are designed to be straight ('rectified'), the marginal velocity field typically induces a curved trajectory."
+- "It is worth noting that the 3.34× memory (49G) is estimated as if the same training implementation could be used, which is not practical and requires special memory optimization."
+
+**Use when**: Preventing misunderstanding or clarifying technical details
+
+---
+
+### When to Use "Surprisingly"
+
+**DO use**:
+- When finding genuinely contradicts common practice
+- When simple solution works as well as complex one
+- When you have explanation (literature, hypothesis, theory)
+- With measured claims ("under some circumstances", "can compete")
+- With "sufficient" not "optimal"
+
+**DON'T use**:
+- For incremental improvements (use "additionally" instead)
+- Without explanation/justification
+- Overgeneralizing ("always", "proves")
+- For expected results
+
+---
+
+## Ablation Study Writing Techniques
+
+**Source**: Kaiming He papers (ViTDet, MeanFlows, MoCo v2)
+
+### Table Design: Incremental Progression
+
+**Pattern**:
+```markdown
+Table X: [Component] Ablation
+┌──────────────────────────────────────────┐
+│ no [component]        | AP     | Δ       │
+│ (a) [common variant]  | AP     | +X.X    │
+│ (b) [another variant] | AP     | +Y.Y    │
+│ (c) ours: simple      | AP     | +Z.Z ✓  │
+└──────────────────────────────────────────┘
+```
+
+**Example (ViTDet Table 1)**:
+```latex
+pyramid design              APbox   APmask
+─────────────────────────────────────────
+no feature pyramid          47.8    42.5
+(a) FPN, 4-stage           50.3    44.9
+(b) FPN, last-map          50.9    45.3
+(c) simple feature pyramid  51.2    45.5
+```
+
+**Techniques**:
+- **Baseline**: "no [X]" shows it's needed
+- **(a), (b), (c)**: Progressive variations
+- **Δ标注**: (+2.5) - Show incremental gains
+- **Correspondence**: "The entries (a-c) correspond to Figure X (a-c)"
+- **Conclusion**: "our simple pyramid is sufficient"
+
+---
+
+### Destructive Ablation: Proving Necessity
+
+**Pattern**:
+```markdown
+We conduct a destructive comparison in which [wrong choice] is intentionally
+performed. Meaningful results are achieved only when [correct choice].
+```
+
+**Example (MeanFlows Table 1b)**:
+```latex
+In Tab. 1b, we conduct a destructive comparison in which incorrect JVP
+computation is intentionally performed.
+
+jvp tangent          FID, 1-NFE
+(v, 0, 1) [correct]   61.06
+(v, 0, 0) [wrong]     268.06
+(v, 1, 0) [wrong]     329.22
+(v, 1, 1) [wrong]     137.96
+
+It shows that meaningful results are achieved only when the JVP computation
+is correct.
+```
+
+**Use when**: You need to prove a design choice is necessary (not just optional)
+
+---
+
+### Ablation Narrative: Observation → Explanation
+
+**Pattern 1: Observation + Literature Support**
+```latex
+We observe that [observation]. This is consistent with the observation in
+[paper] that [their finding].
+```
+
+**Pattern 2: Observation + Hypothesis**
+```latex
+We hypothesize that this is because [reason 1] and also because [reason 2].
+```
+
+**Pattern 3: Observation + Theory**
+```latex
+[Observation]. This indicates that [theoretical explanation].
+```
+
+---
+
+## Theory-Driven Paper Keywords
+
+**Source**: Kaiming He et al., "Mean Flows for One-step Generative Modeling" (2025)
+
+### Naturalness Keywords (use to describe your theory)
+
+- **"naturally"** - "This naturally leads to..."
+- **"intrinsic"** - "intrinsic relation between..."
+- **"well-defined"** - "well-defined problem"
+- **"principled"** - "principled basis for..."
+- **"first principles"** - "from first principles"
+- **"solely originated from"** - "solely from definition"
+
+### Independence Keywords
+
+- **"does not depend on"** - Theory independence from implementation
+- **"independent of"** - Independent of specific choices
+- **"self-contained"** - System independence
+- **"from scratch"** - No external dependencies
+- **"without any X"** - Negative list (what you don't need)
+
+### Differentiation Keywords
+
+- **"in contrast to"** - Conceptual contrast
+- **"unlike"** - Direct comparison
+- **"typically"** - "typically modeled" (their approach)
+- **"prior works typically rely on"** - Their limitation
+- **"imposed as"** - Artificial constraint (theirs)
+
+### Avoid (Too Promotional)
+
+- ❌ "revolutionary" - Let others say it
+- ❌ "breakthrough" - Overused
+- ❌ "completely eliminates" - Too absolute
+- ✅ "significantly outperforms" - Strong but measured
+- ✅ "substantial improvement" - Professional
+
+---
+
+## Design Simplification Paper Keywords
+
+**Source**: Kaiming He et al., "Exploring Plain Vision Transformer Backbones for Object Detection" (ViTDet, 2022)
+
+### Philosophy Keywords
+
+- **"minimal"** - "minimal adaptations"
+- **"sufficient"** - "is sufficient to" (not "optimal")
+- **"simple"** - "simple feature pyramid"
+- **"plain"** - "plain backbone"
+- **"decouple"** - "decouple pre-training from fine-tuning"
+- **"independence"** - "independence of upstream vs downstream"
+
+### Direction Keywords
+
+- **"pursue a different direction"** - Clear positioning
+- **"in contrast to"** - Differentiation
+- **"abandons"** - What you give up (respectfully)
+- **"enables"** - What your approach allows
+
+### Measured Claim Keywords
+
+- **"under some circumstances"** - Not always
+- **"can compete with"** - Competitive, not dominant
+- **"more prominent for"** - When effect is stronger
+- **"is sufficient"** - Necessary, not maximal
+
+
+---
+
+## Updated: 何凯明的写作技巧
+
+> 来源: 分析了何凯明的 11 篇代表性论文（扩展分析，包括 MeanFlows、ViTDet、MAR 等）
+> 添加时间: 2026-01-26
+
+> 扩展内容包括：
+> - "Surprisingly" 发现的多层次报告模式
+> - Ablation Study 的增量式和破坏性实验设计
+> - 理论驱动型论文的关键词策略
+> - 设计简化型论文的关键词策略
+
+### 句子结构偏好
+
+**主动语态优先** (被动语态仅 9.3%)
+何凯明偏好使用主动、直接的陈述：
+
+**✅ 推荐 (何凯明的风格):**
+- "We present a framework for [task]"
+- "Our method achieves [result]"
+- "This formulation enables [benefit]"
+
+**❌ 避免:**
+- "A framework is presented for [task]"
+- "Results are achieved by our method"
+
+### 贡献表达方式
+
+何凯明常用的贡献表达模式：
+
+**模式 1: 直接陈述**
+```
+We propose [method] that [feature].
+We demonstrate [result] on [dataset].
+```
+
+**模式 2: 对比强调**
+```
+Unlike [previous work], our approach [difference].
+This leads to [improvement] in [metric].
+```
+
+**模式 3: 问题-解决方案**
+```
+[Challenge] remains difficult. We address this by [solution].
+```
+
+### 技术术语使用
+
+何凯明论文中的高频术语组合：
+
+| 术语类别 | 常用术语 |
+|---------|---------|
+| **网络架构** | deep neural networks, convolutional, residual, activation |
+| **训练过程** | training, validation, optimization, convergence |
+| **性能评估** | outperforms, achieves, improves, surpasses |
+| **方法定位** | state-of-the-art, baseline, framework, algorithm |
+| **所有权** | our method, our approach, our framework |
+
+### 过渡短语
+
+何凯明论文中常用的过渡短语（按频率排序）：
+
+1. **however** - 用于对比不同观点
+2. **in addition/additionally** - 补充信息
+3. **furthermore** - 递进说明
+4. **therefore/thus** - 得出结论
+5. **specifically** - 举例说明
+6. **conversely** - 对比说明
+
+### 数值结果呈现
+
+何凯明在呈现数值结果时的模式：
+
+**精确性优先:**
+```
+Our method achieves 76.4% accuracy (Table X).
+This represents a 28% relative improvement.
+```
+
+**对比式呈现:**
+```
+Compared to baseline (73.2%), our method (76.4%) improves
+by 3.2 percentage points.
+```
+
+**强调意义:**
+```
+This result won the 1st place in [competition/task].
+```
+
+### 图表引用模式
+
+何凯明引用图表的标准格式：
+
+**图表引入:**
+- "Fig. X shows [现象]"
+- "Table Y summarizes [结果]"
+- "As shown in Fig. Z, [结论]"
+
+**图表描述:**
+- "The solid line denotes [条件 A], the dashed line [条件 B]"
+- "The blue curve shows [指标], while the red curve shows [指标]"
+
+### 网络架构描述
+
+何凯明在描述网络架构时的特点：
+
+1. **表格化呈现** - 使用表格列出层配置
+2. **可视化辅助** - 配合架构图
+3. **简洁符号** - 使用清晰的数学符号
+4. **示例:**
+```
+layer name | output size | configuration
+conv1      | 112×112   | 7×7, 64, /2
+```