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diff --git a/.agents/skills/ml-paper-writing/references/knowledge/README.md b/.agents/skills/ml-paper-writing/references/knowledge/README.md
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+# Academic Writing Knowledge Base
+
+This knowledge base contains reusable academic writing knowledge mined from papers.
+
+## Canonical maintained memory
+
+The canonical paper-miner memory is:
+
+- `paper-miner-writing-memory.md`
+
+This is the **only maintained paper-miner writing memory**.
+
+It stores:
+- writing patterns mined,
+- structure signals,
+- reusable phrasing,
+- venue-specific signals,
+- how those signals help future writing,
+- and a source index.
+
+## Maintenance rule
+
+`paper-miner` always writes mined writing knowledge into `paper-miner-writing-memory.md`.
+
+This memory is:
+- **global**,
+- **cross-project**,
+- **not project-specific**.
+
+If `paper-miner` is invoked inside a project, it may use project context to understand relevance, but it still writes only to the global memory.
+
+## Legacy files
+
+Older files such as:
+- `structure.md`
+- `writing-techniques.md`
+- `submission-guides.md`
+- `review-response.md`
+
+may still exist as historical material, but new paper-miner updates should treat `paper-miner-writing-memory.md` as the canonical maintained memory.
+
+## Usage
+
+Use this knowledge base when:
+- drafting papers,
+- improving section structure,
+- borrowing reusable phrasing patterns,
+- preparing rebuttals,
+- studying venue-facing writing signals.
+
+## Contributing
+
+When `paper-miner` analyzes a new paper:
+1. extract actionable writing knowledge,
+2. merge it into `paper-miner-writing-memory.md`,
+3. preserve source attribution,
+4. avoid duplicate patterns,
+5. keep the memory compact and reusable.
diff --git a/.agents/skills/ml-paper-writing/references/knowledge/design-simplification-papers-kaiming-he.md b/.agents/skills/ml-paper-writing/references/knowledge/design-simplification-papers-kaiming-he.md
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+# Design Simplification Papers: Less Is More
+
+**Source**: Kaiming He et al., "Exploring Plain Vision Transformer Backbones for Object Detection" (ViTDet, 2022)
+
+**Paper Type**: Design simplification / Minimal adaptations paper
+
+**Core Pattern**: Challenge design assumptions → Minimize changes → Surprising effectiveness → Fair comparison
+
+---
+
+## 1. Abstract Structure: The "Surprisingly" Framework
+
+### Pattern: Conventional Practice → Simple Alternative → Unexpected Results
+
+**Template**:
+```markdown
+Abstract:
+1. [Context] Standard practice in [domain] is [conventional design]
+2. [Challenge] With [new technology], this faces [challenges]
+3. [Common Solution] Most work addresses this by [abandoning philosophy / adding complexity]
+4. [Our Direction] We explore [different direction]: [minimal approach]
+5. [Surprisingly 1] Surprisingly, we observe: (i) [simple finding 1]
+   and (ii) [simple finding 2]
+6. [Surprisingly 2] More surprisingly, [stronger claim under conditions]
+7. [Implications] This enables [benefit] without [traditional requirement]
+```
+
+### ViTDet Abstract Example (annotated):
+
+```latex
+Modern object detectors consist of hierarchical backbone feature extractors
+and detection-specific necks/heads (e.g., FPN, RPN).
+
+With Vision Transformers (ViT) emerging as powerful backbones, their plain,
+non-hierarchical nature poses challenges: How to address multi-scale objects?
+
+One solution abandons the plain ViT philosophy, re-introducing hierarchical
+designs (e.g., Swin).
+
+We pursue a different direction: plain ViT backbones with minimal adaptations.
+
+Surprisingly, we observe: (i) A simple feature pyramid from a single-scale map
+is sufficient (without FPN), and (ii) Window attention without shifting is
+sufficient (with a few propagation blocks).
+
+More surprisingly, under some circumstances, our ViTDet can compete with
+leading hierarchical detectors like Swin.
+
+With MAE pre-training, ViTDet outperforms hierarchical counterparts,
+especially for larger models.
+
+This decouples pre-training from fine-tuning, maintaining independence of
+upstream vs downstream tasks.
+```
+
+### Key Techniques:
+
+1. **"Modern...consist of..."** - Establish conventional practice
+2. **"With...emerging as..."** - New technology, new challenge
+3. **"abandons the...philosophy"** - Critique common solutions
+4. **"We pursue a different direction"** - Clear positioning
+5. **"Surprisingly, we observe: (i)... and (ii)..."** - First surprise
+6. **"More surprisingly..."** - Second, deeper surprise
+7. **"under some circumstances"** - Measured claim
+8. **"sufficient"** - Scientific (not "optimal")
+9. **"without [common practice]"** - Negative emphasis
+
+---
+
+## 2. Introduction: The "Challenge Assumptions" Framework
+
+### Pattern: Tradition → New Challenge → Common Compromise → Your Alternative → Philosophy
+
+**Structure**:
+```markdown
+1. [Traditional Practice] Established design in [field]
+2. [Evolution] How this emerged historically ("For a long while...")
+3. [New Challenge] [New technology] with [different characteristics]
+4. [Philosophy Clash] Original [tech] has "'minimalist' pursuit"
+   - Questions: "How can we...?" "Is [X] too inefficient?"
+5. [Common Solution] One solution: [abandon philosophy] → [revert to old design]
+   - Acknowledge: "has shown successful results"
+6. [Your Direction] "we pursue a different direction"
+   - Motivation: "If successful, enables [benefit]"
+7. [Philosophy] "in part follows the [philosophy] of '[concept]'"
+8. [Surprising Findings] "Surprisingly, we observe..."
+9. [Implications] "More surprisingly..." → Competitive results
+```
+
+### ViTDet Introduction Flow:
+
+#### Traditional Practice (Establish Context)
+```latex
+Modern object detectors in general consist of a backbone feature extractor
+that is agnostic to the detection task and a set of necks and heads that
+incorporate detection-specific prior knowledge.
+
+Common components in the necks/heads may include Region-of-Interest (RoI)
+operations, Region Proposal Networks (RPN) or anchors, Feature Pyramid
+Networks (FPN), etc.
+```
+
+**Technique**:
+- **"in general consist of"** - Standard architecture
+- **"agnostic to"** vs **"detection-specific"** - Clear division
+- **"may include"** - Examples, not exhaustive
+
+#### Historical Evolution
+```latex
+For a long while, these backbones have been multi-scale, hierarchical
+architectures due to the de facto design of convolutional networks (ConvNet),
+which has heavily influenced the neck/head design for detecting objects at
+multiple scales (e.g., FPN).
+```
+
+**Technique**:
+- **"For a long while"** - Historical dimension
+- **"due to...which has heavily influenced"** - Causal chain
+- **"de facto design"** - Established convention
+
+#### New Technology Challenge
+```latex
+Over the past year, Vision Transformers (ViT) have been established as a
+powerful backbone for visual recognition.
+
+Unlike typical ConvNets, the original ViT is a plain, non-hierarchical
+architecture that maintains a single-scale feature map throughout.
+
+Its 'minimalist' pursuit is met with challenges when applied to object
+detection—e.g., How can we address multi-scale objects in a downstream task
+with a plain backbone from upstream pre-training? Is a plain ViT too
+inefficient to use with high-resolution detection images?
+```
+
+**Technique**:
+- **"Over the past year...have been established as"** - Timeframe
+- **"Unlike typical ConvNets"** - Direct contrast
+- **"plain, non-hierarchical"**, **"single-scale"** - Key characteristics
+- **"'minimalist' pursuit"** - Philosophy (in quotes)
+- **Two questions**: Challenge reader to think
+
+#### Common Solution (Acknowledge then Pivot)
+```latex
+One solution, which abandons this pursuit, is to re-introduce hierarchical
+designs into the backbone.
+
+This solution, e.g., Swin Transformers and related works, can inherit the
+ConvNet-based detector design and has shown successful results.
+```
+
+**Technique**:
+- **"which abandons this pursuit"** - Critique (respectful)
+- **"can inherit"** - Acknowledge advantage
+- **"has shown successful results"** - Don't deny effectiveness
+
+#### Your Different Direction
+```latex
+In this work, we pursue a different direction: we explore object detectors
+that use only plain, non-hierarchical backbones.
+
+If this direction is successful, it will enable the use of original ViT
+backbones for object detection; this will decouple the pre-training design
+from the fine-tuning demands, maintaining the independence of upstream vs.
+downstream tasks, as has been the case for ConvNet-based research.
+```
+
+**Technique**:
+- **"we pursue a different direction"** - Clear positioning
+- **"If this direction is successful, it will enable..."** - Motivation
+- **"decouple"**, **"independence"** - Philosophy keywords
+- **"as has been the case for..."** - Historical precedent
+
+#### Philosophy Elevation
+```latex
+This direction also in part follows the ViT philosophy of 'fewer inductive
+biases' in the pursuit of universal features.
+
+As the non-local self-attention computation can learn translation-equivariant
+features, they may also learn scale-equivariant features from certain forms
+of supervised or self-supervised pre-training.
+```
+
+**Technique**:
+- **"in part follows the...philosophy of"** - Theoretical connection
+- **"fewer inductive biases"** - Core concept
+- **Analogy**: translation-equivariant → scale-equivariant
+- **"may also learn"** - Speculation (honest)
+
+#### Surprising Findings
+```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.
+```
+
+**Technique**:
+- **"Surprisingly, we observe:"** - Marker
+- **(i)** and **(ii)** - Structured list
+- **"sufficient to"** - Not "optimal", scientific phrasing
+- **"without the common [X]"** - Negative differentiation
+
+#### Deeper Surprise
+```latex
+More surprisingly, under some circumstances, our plain-backbone detector,
+named ViTDet, can compete with the leading hierarchical-backbone detectors
+(e.g., Swin, MViT).
+
+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.
+```
+
+**Technique**:
+- **"More surprisingly"** - Progressive emphasis
+- **"under some circumstances"** - Measured claim
+- **"named ViTDet"** - Brand at results
+- **Specific comparison**: MAE vs ImageNet supervised
+- **"The gains are more prominent for..."** - Pattern observation
+
+---
+
+## 3. Methods Section: The "Minimal Adaptations" Narrative
+
+### Pattern: We Don't Aim to Invent, We Minimize
+
+**Structure**:
+```markdown
+1. [Declaration] We do NOT aim to develop new components
+2. [Philosophy] Instead, make minimal adaptations sufficient to overcome challenges
+3. [Specific] In particular, [what we actually do]
+4. [Abandonment] This abandons [traditional component]
+5. [Decoupling] Adaptations only during fine-tuning, do not alter pre-training
+6. [Contrast] This is in contrast to [recent methods] that [what they do]
+7. [Benefit] Our scenario enables [benefit], without [cost]
+```
+
+### ViTDet Methods Narrative:
+
+```latex
+In our study, we do not aim to develop new components; instead, we make
+minimal adaptations that are sufficient to overcome the aforementioned
+challenges.
+
+In particular, our detector builds a simple feature pyramid from only the
+last feature map of a plain ViT backbone (Figure 1).
+
+This abandons the FPN design and waives the requirement of a hierarchical
+backbone.
+
+These adaptations are made only during fine-tuning and do not alter pre-training.
+
+This is in contrast to the recent methods that modify the attention computation
+directly with backbone pre-training (e.g., Swin, MViT).
+
+Our scenario enables us to use the original ViT backbone for detection, without
+redesigning pre-training architectures.
+```
+
+**Techniques**:
+- **"we do not aim to develop new components"** - Clear scope
+- **"minimal adaptations"** - Philosophy
+- **"sufficient to"** - Not maximal, necessary
+- **"This abandons..."** - What you give up
+- **"only during fine-tuning"** - Temporal boundary
+- **"do not alter pre-training"** - Upstream independence
+- **"This is in contrast to"** - Competitor positioning
+- **"enables us to use"** - Practical benefit
+
+---
+
+## 4. Fair Comparison: The "Equal Effort" Declaration
+
+### Pattern: Admit Complexity → Claim Effort → Demonstrate Fairness
+
+**Structure**:
+```markdown
+1. [Admit] Modern systems involve [complexity]
+2. [Claim] To compare as fairly as possible, we [effort]
+3. [Specific 1] Use same [implementation] for all
+4. [Specific 2] Different backbones get [appropriate treatment]
+5. [Validation] Our results for [competitor] are [better/equal] to original
+6. [Implication] Since we reproduce others well, comparisons are fair
+```
+
+### ViTDet Fair Comparison Statement:
+
+```latex
+Modern detection systems involve many implementation details and subtleties.
+
+To focus on comparing backbones under as fair conditions as possible, we
+incorporate the Swin and MViTv2 backbones into our implementation.
+
+We use the same implementation of Mask R-CNN and Cascade Mask R-CNN for all
+ViT, Swin, and MViTv2 backbones.
+
+We use FPN for the hierarchical backbones of Swin/MViTv2.
+
+We search for optimal hyper-parameters separately for each backbone.
+```
+
+**Techniques**:
+- **"involve many implementation details and subtleties"** - Admit difficulty
+- **"under as fair conditions as possible"** - Effort disclaimer
+- **"incorporate...into our implementation"** - What we did
+- **"use the same...for all"** - Unified framework
+- **"search for optimal...separately"** - Equal effort
+
+#### Self-Validation
+```latex
+Our Swin results are better than their counterparts in the original paper;
+our MViTv2 results are better than or on par with those reported in the
+original paper.
+```
+
+**Technique**:
+- Report self-results → Show competence → Imply fairness
+
+---
+
+## 5. Results: Multi-Factor Analysis
+
+### Pattern: Factors, Trends, Wall-Clock Time
+
+**Structure**:
+```markdown
+1. [Acknowledge Complexity] Comparisons involve [factors]
+2. [Identify Trend] Our method presents better [trend behavior]
+3. [Qualify] When [condition], our method [advantage]
+4. [Expand] Moreover, [second dimension advantage]
+5. [Explain] as [reason related to simplicity]
+```
+
+### ViTDet Results Narrative:
+
+```latex
+Figure 3 plots the trade-offs.
+
+The comparisons here involve two factors: the backbone and the pre-training
+strategy.
+
+Our plain-backbone detector, combined with MAE pre-training, presents better
+scaling behavior.
+
+When the models are large, our method outperforms the hierarchical
+counterparts of Swin/MViTv2, including those using IN-21K supervised
+pre-training.
+
+Moreover, the plain ViT has a better wall-clock performance (Figure 3 right),
+as the simpler blocks are more hardware-friendly.
+```
+
+**Techniques**:
+- **"involve two factors"** - Analysis depth
+- **"presents better scaling behavior"** - Trend, not just points
+- **"When the models are large"** - Qualify claim
+- **"Moreover"** - Second dimension
+- **"better wall-clock performance"** - Practical metric
+- **"simpler blocks are more hardware-friendly"** - Explain why
+
+---
+
+## 6. "Surprisingly" Usage: Multi-Level Pattern
+
+### Level 1: Basic Surprise (Abstract)
+
+```latex
+Surprisingly, we observe: (i) [simple sufficient without common practice]
+and (ii) [simple sufficient without common practice]
+```
+
+**Characteristics**:
+- Two findings (i) and (ii)
+- "sufficient" not "optimal"
+- "without [common practice]"
+- Structured presentation
+
+### Level 2: Competitive Surprise (Introduction)
+
+```latex
+More surprisingly, under some circumstances, our [method] can compete
+with the leading [competitors].
+```
+
+**Characteristics**:
+- "More surprisingly" - Progressive
+- "under some circumstances" - Measured
+- "compete with" - Not "beat", competitive
+- Name competitors specifically
+
+### Level 3: Superiority Surprise (Introduction)
+
+```latex
+With [specific condition], our [method] can outperform the [competitors]
+that use [stronger condition].
+
+The gains are more prominent for [specific condition].
+```
+
+**Characteristics**:
+- Specific conditions compared
+- "outperform" - Stronger claim here
+- Pattern observation: "more prominent for"
+- Shows understanding of when/where
+
+---
+
+## 7. Ablation Study: Incremental + Destructive
+
+### Pattern: Baseline → Incremental Additions → Sufficient
+
+**Table Design**:
+```markdown
+Table X: [Component] Ablation
+┌──────────────────────────────────────────┐
+│ Baseline          | Metric  | Δ         │
+├──────────────────────────────────────────┤
+│ no [component]    | 47.8    | -         │
+│ (a) [common]      | 50.3    | +2.5      │
+│ (b) [variant]     | 50.9    | +3.1      │
+│ (c) ours: simple  | 51.2    | +3.4 ✓   │
+├──────────────────────────────────────────┤
+│ Conclusion: Our simple [X] is sufficient  │
+└──────────────────────────────────────────┘
+```
+
+### ViTDet Table 1 Example:
+
+```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
+- **Conclusion text**: "our simple pyramid is sufficient"
+
+---
+
+## 8. "Interestingly" Usage: Pattern + Explanation
+
+### Pattern: Observation → Literature Support → Explanation
+
+**Structure**:
+```markdown
+Interestingly, [observation].
+
+This is in line with the observation in [paper] that [their finding].
+
+[Additional explanation or hypothesis].
+```
+
+### ViTDet Example:
+
+```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.
+```
+
+**Techniques**:
+- **"Interestingly"** - Marker for unexpected
+- **Observation**: Specific finding
+- **"in line with the observation in"** - Literature support
+- **Explanation**: Why it makes sense
+
+---
+
+## 9. Minimalism Keywords: Design Simplification Vocabulary
+
+**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" - Positioning
+- "in contrast to" - Differentiation
+- "abandons" - What you give up
+- "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
+
+**Surprise Markers** (use in order):
+1. "Surprisingly" - First finding
+2. "More surprisingly" - Deeper finding
+3. "Interestingly" - Pattern observation
+4. "Notably" - Important detail
+5. "It is worth noting that" - Caveat/clarification
+
+---
+
+## 10. Common Mistakes in Design Simplification Papers
+
+### ❌ Don't:
+- Claim your method is "optimal" - You're simplifying, not optimizing
+- Attack common practices - Acknowledge their value first
+- Overgeneralize - "under some circumstances" is honest
+- Forget to show fair comparison - Prove you tried hard with baselines
+- Hide complexity - Admit what you don't address
+
+### ✅ Do:
+- Use "sufficient" instead of "optimal"
+- Say what you DON'T do ("do not aim to develop new components")
+- Emphasize minimal changes ("minimal adaptations")
+- Report when your method wins and when it doesn't
+- Show "surprisingly" findings with proper qualification
+- Demonstrate fair comparison effort
+- Adapt only where necessary (fine-tuning, not pre-training)
+
+---
+
+## 11. Revision Checklist for Design Simplification Papers
+
+**Before Submission, Verify:**
+
+- [ ] Abstract has "Surprisingly, we observe: (i)... and (ii)..."
+- [ ] Introduction establishes conventional practice first
+- [ ] Common solution is acknowledged ("has shown successful results")
+- [ ] "We pursue a different direction" is stated clearly
+- [ ] Philosophy is elevated ("fewer inductive biases")
+- [ ] "More surprisingly" used for deeper claim
+- [ ] Methods section says "we do not aim to develop new components"
+- [ ] "minimal adaptations" philosophy stated
+- [ ] "only during fine-tuning" boundary specified
+- [ ] Fair comparison effort described explicitly
+- [ ] Self-validation shown (our reproduction of others is good)
+- [ ] Multi-factor analysis in results (scaling, wall-clock)
+- [ ] Ablations show incremental progression
+- [ ] "sufficient" used, not "optimal"
+- [ ] Under what conditions is stated ("under some circumstances")
+
+---
+
+## 12. Example: Applying This Pattern
+
+### Original Idea (Not Design Simplification):
+"We propose a new feature pyramid that improves detection AP by 3%."
+
+### Design Simplification Version:
+"Modern detectors use hierarchical backbones with FPN. With plain ViT
+emerging as powerful backbones, a common solution re-introduces hierarchy
+(abandoning the plain philosophy). We pursue a different direction: plain
+backbones with minimal adaptations. Surprisingly, we observe a simple feature
+pyramid from a single-scale map is sufficient (without FPN). More
+surprisingly, with MAE pre-training, ViTDet competes with hierarchical
+detectors, especially for larger models. This decouples pre-training from
+fine-tuning, maintaining upstream/downstream independence."
+
+**The Design Simplification Frame**:
+- Conventional: Hierarchy + FPN
+- Challenge: Plain ViT is...plain
+- Common: Swin (abandons philosophy)
+- Ours: Minimal adaptations
+- Surprise: Simple is sufficient
+- Philosophy: Decoupling, independence
+
+---
+
+## Paper Metadata
+
+**Title**: Exploring Plain Vision Transformer Backbones for Object Detection (ViTDet)
+
+**Authors**: Yanghao Li, Hanzi Mao, Kaiming He
+
+**Venue**: ECCV 2022
+
+**arXiv**: 2203.16527
+
+**Key Concepts**:
+- Plain ViT for detection (no hierarchy needed)
+- Simple feature pyramid (no FPN needed)
+- Minimal adaptations philosophy
+- Decoupling pre-training from fine-tuning
+- MAE pre-training synergy
+
+---
+
+## Extracted by
+
+**Date**: 2026-01-26
+
+**Source**: Analysis of ViTDet paper (21 pages)
+
+**Extraction Focus**: Design simplification paper writing patterns, "surprisingly"
+findings reporting, minimal adaptations philosophy, fair comparison strategies
+
+**For Integration**: ml-paper-writing skill knowledge base
diff --git a/.agents/skills/ml-paper-writing/references/knowledge/kaiming_he_injection_record.json b/.agents/skills/ml-paper-writing/references/knowledge/kaiming_he_injection_record.json
new file mode 100644
index 0000000..7ffd675
--- /dev/null
+++ b/.agents/skills/ml-paper-writing/references/knowledge/kaiming_he_injection_record.json
@@ -0,0 +1,153 @@
+{
+  "metadata": {
+    "source": "Kaiming He Papers Analysis",
+    "date": "2026-01-26",
+    "papers_analyzed": 11,
+    "analysis_method": "Text extraction and pattern mining",
+    "latest_addition": {
+      "papers": ["Mean Flows", "ViTDet", "MoCo v2", "Deconstructing Denoising Diffusion Models", "Autoregressive Image Generation (MAR)"],
+      "extraction_date": "2026-01-26",
+      "new_knowledge_files": [
+        "theory-driven-papers-kaiming-he.md",
+        "design-simplification-papers-kaiming-he.md"
+      ]
+    }
+  },
+  "knowledge_files": {
+    "structure.md": {
+      "status": "updated",
+      "last_update": "2026-01-26",
+      "contains": "Basic structure patterns from 19 Kaiming He papers"
+    },
+    "writing-techniques.md": {
+      "status": "needs_update",
+      "last_update": "2026-01-26",
+      "contains": "Basic writing techniques from 19 Kaiming He papers"
+    },
+    "rethinking-papers-kaiming-he.md": {
+      "status": "complete",
+      "focus": "Rethinking papers, challenging conventional wisdom",
+      "source_paper": "Autoregressive Image Generation without Vector Quantization (NeurIPS 2024 Spotlight)"
+    },
+    "theory-driven-papers-kaiming-he.md": {
+      "status": "new",
+      "focus": "Theory-driven papers, first principles, MeanFlow Identity",
+      "source_paper": "Mean Flows for One-step Generative Modeling (2025)"
+    },
+    "design-simplification-papers-kaiming-he.md": {
+      "status": "new",
+      "focus": "Design simplification, minimal adaptations, 'Surprisingly' findings",
+      "source_paper": "Exploring Plain Vision Transformer Backbones for Object Detection (ViTDet, ECCV 2022)"
+    }
+  },
+  "patterns_extracted": {
+    "introduction_frameworks": {
+      "principle_introduction": {
+        "source": "MeanFlows",
+        "pattern": "Background → Problem → Critique (Despite...) → Core Concept → Theory → Advantage → Results",
+        "keywords": ["principled", "intrinsic", "well-defined", "naturally", "first principles"]
+      },
+      "challenge_assumptions": {
+        "source": "ViTDet",
+        "pattern": "Traditional → New Challenge → Common Solution → Our Direction → Philosophy → Surprisingly → Implications",
+        "keywords": ["minimal adaptations", "sufficient", "decouple", "independence", "surprisingly"]
+      },
+      "rethinking_conventional_wisdom": {
+        "source": "MAR",
+        "pattern": "Conventional wisdom → Question → Analysis → Alternative → Results → Vision",
+        "keywords": ["Conventional wisdom holds that", "Is it necessary", "not a necessity"]
+      }
+    },
+    "surprisingly_findings": {
+      "level_1": {
+        "pattern": "Surprisingly, we observe: (i)... and (ii)...",
+        "usage": "First-level surprise - basic findings",
+        "example": "ViTDet Abstract"
+      },
+      "level_2": {
+        "pattern": "More surprisingly, under some circumstances...",
+        "usage": "Second-level surprise - competitive results",
+        "example": "ViTDet Introduction"
+      },
+      "level_3": {
+        "pattern": "With [condition], outperforms... gains more prominent for...",
+        "usage": "Third-level surprise - superiority under conditions",
+        "example": "ViTDet Introduction"
+      },
+      "variants": {
+        "interestingly": "Observation + literature support + explanation",
+        "notably": "Important detail or counter-intuitive result",
+        "it_is_worth_noting": "Technical caveat or clarification"
+      }
+    },
+    "ablation_techniques": {
+      "incremental_tables": {
+        "pattern": "Baseline → (a) → (b) → (c) with Δ标注",
+        "source": "ViTDet Table 1"
+      },
+      "destructive_comparison": {
+        "pattern": "Intentionally wrong values to prove necessity",
+        "source": "MeanFlows Table 1b"
+      },
+      "narrative_structure": {
+        "observation_then_explain": "Observe pattern → Provide explanation (literature/hypothesis/theory)"
+      }
+    },
+    "theoretical_derivation": {
+      "naming_identity": {
+        "pattern": "Define → Derive → Name ('X Identity')",
+        "source": "MeanFlows MeanFlow Identity"
+      },
+      "step_by_step": {
+        "pattern": "Motivation → Derivation with 'Now we...' → Justification with 'where...'",
+        "source": "MeanFlows Section 2"
+      }
+    },
+    "comparison_techniques": {
+      "principled_vs_heuristic": {
+        "pattern": "At the core...does not depend on...In contrast, typically rely on...",
+        "source": "MeanFlows"
+      },
+      "fair_comparison_declaration": {
+        "pattern": "Admit complexity → Claim effort → Demonstrate fairness",
+        "source": "ViTDet"
+      },
+      "multi_factor_analysis": {
+        "pattern": "Factors identified → Trend behavior → Wall-clock time",
+        "source": "ViTDet Results"
+      }
+    },
+    "keyword_strategies": {
+      "theory_paper": ["principled", "intrinsic", "well-defined", "naturally", "self-contained", "solely originated from"],
+      "design_paper": ["minimal", "sufficient", "decouple", "independence", "surprisingly", "abandons"],
+      "rethinking_paper": ["Conventional wisdom holds that", "not a necessity", "orthogonal to", "uncharted realm"]
+    }
+  },
+  "papers_analyzed_list": [
+    "Non-local Neural Networks",
+    "SlowFast Networks",
+    "Rethinking ImageNet Pre-training",
+    "Faster R-CNN",
+    "Delving Deep into Rectifiers (PReLU)",
+    "Spatial Pyramid Pooling (SPP-net)",
+    "Deconstructing Denoising Diffusion Models",
+    "Autoregressive Image Generation without Vector Quantization (MAR)",
+    "Mean Flows for One-step Generative Modeling",
+    "Exploring Plain Vision Transformer Backbones for Object Detection (ViTDet)",
+    "MoCo v2: Improved Baselines with Momentum Contrastive Learning"
+  ],
+  "integration_summary": {
+    "total_papers": 11,
+    "knowledge_files": 5,
+    "patterns_extracted": 25,
+    "paper_types_identified": [
+      "Theory-driven (MeanFlows)",
+      "Design simplification (ViTDet)",
+      "Rethinking (MAR)",
+      "Deconstruction (DDM)",
+      "Milestone (PReLU)",
+      "Multi-task (SPP-net)",
+      "Technical note (MoCo v2)"
+    ]
+  }
+}
diff --git a/.agents/skills/ml-paper-writing/references/knowledge/paper-miner-writing-memory.md b/.agents/skills/ml-paper-writing/references/knowledge/paper-miner-writing-memory.md
new file mode 100644
index 0000000..7320060
--- /dev/null
+++ b/.agents/skills/ml-paper-writing/references/knowledge/paper-miner-writing-memory.md
@@ -0,0 +1,37 @@
+# Paper-Miner Writing Memory
+
+This is the **canonical global writing memory** maintained by `paper-miner`.
+
+It stores reusable academic writing knowledge mined from papers across venues and projects.
+
+## Rules
+
+- This is the **only maintained paper-miner writing memory**.
+- `paper-miner` writes here even when invoked inside a specific project.
+- Do **not** create project-specific paper-miner writing memory.
+- Do **not** split new mined knowledge across multiple maintained category files.
+- Keep source attribution explicit and avoid duplicate entries.
+
+## Writing patterns mined
+
+<!-- paper-miner adds reusable rhetorical patterns here -->
+
+## Structure signals
+
+<!-- paper-miner adds section-flow and organization signals here -->
+
+## Reusable phrasing
+
+<!-- paper-miner adds concise reusable phrasing and transition templates here -->
+
+## Venue-specific signals
+
+<!-- paper-miner adds venue-facing style and convention signals here -->
+
+## How this helps our writing
+
+<!-- paper-miner explains how mined signals can inform future writing decisions here -->
+
+## Source index
+
+<!-- one short source entry per analyzed paper -->
diff --git a/.agents/skills/ml-paper-writing/references/knowledge/rethinking-papers-kaiming-he.md b/.agents/skills/ml-paper-writing/references/knowledge/rethinking-papers-kaiming-he.md
new file mode 100644
index 0000000..26a7c86
--- /dev/null
+++ b/.agents/skills/ml-paper-writing/references/knowledge/rethinking-papers-kaiming-he.md
@@ -0,0 +1,1023 @@
+# "Rethinking" Papers: Challenging Conventional Wisdom
+
+**Source**: Kaiming He et al., "Autoregressive Image Generation without Vector Quantization" (NeurIPS 2024 Spotlight)
+
+**Paper Type**: Paradigm-challenging / "Rethinking" paper
+
+**Core Pattern**: Question deeply-held assumptions → Propose alternative → Demonstrate effectiveness
+
+---
+
+## 1. Abstract Structure: Challenging Conventional Wisdom
+
+### Pattern: The "Conventional Wisdom" Opening
+
+**Template**:
+```markdown
+Abstract:
+1. [Hook] Conventional wisdom holds that [widely-believed assumption].
+2. [Observation] We observe that [counter-point or nuance].
+3. [Question] Is it necessary for [core assumption]?
+4. [Proposal] In this work, we propose [alternative approach].
+5. [Solution] Rather than [traditional method], we [novel method].
+6. [Results] This approach [eliminates/enables] X, achieving [strong results].
+7. [Vision] We hope this work will [broader impact statement].
+```
+
+### MAR Abstract Example (annotated):
+
+```latex
+Conventional wisdom holds that [autoregressive models for image generation
+are typically accompanied by vector-quantized tokens].
+
+We observe that while [discrete-valued space can facilitate representing
+a categorical distribution], it is [not a necessity for autoregressive modeling].
+
+In this work, we propose to [model the per-token probability distribution
+using a diffusion procedure], which allows us to [apply autoregressive models
+in a continuous-valued space].
+
+Rather than using [categorical cross-entropy loss], we define a
+[Diffusion Loss function] to model the per-token probability.
+
+This approach [eliminates the need for discrete-valued tokenizers].
+
+We evaluate its effectiveness across [a wide range of cases], including
+[standard autoregressive models and generalized masked autoregressive (MAR) variants].
+
+By removing vector quantization, our image generator achieves [strong results]
+while enjoying [the speed advantage of sequence modeling].
+
+We hope this work will motivate [the use of autoregressive generation in
+other continuous-valued domains and applications].
+```
+
+### Key Techniques:
+
+1. **"Conventional wisdom holds that..."** - Strong, respectful opening that acknowledges prevailing beliefs
+2. **"We observe that..."** - Signals empirical insight rather than mere speculation
+3. **"it is not a necessity for..."** - Gentle but direct challenge to the assumption
+4. **"Rather than..."** - Clear alternative positioning
+5. **"This approach eliminates the need for..."** - Practical benefit statement
+6. **"We hope this work will..."** - Forward-looking vision
+
+---
+
+## 2. Introduction: The Question-Driven Framework
+
+### Pattern: Start with the Question
+
+**Traditional Introduction** (less effective):
+- "Image generation is an important problem..."
+- "Existing methods use VQ tokenizers..."
+- "We propose a new method..."
+
+**"Rethinking" Introduction** (Kaiming He style):
+```markdown
+1. [Establish Context] Domain context + prevailing approach
+2. [Identify Assumption] The widely-held belief
+3. [Formulate Question] "Is it necessary for..."
+4. [Analyze Requirements] What is truly essential?
+5. [Propose Alternative] If alternative model exists...
+6. [Present Solution] Our specific proposal
+7. [Broader Implications] What this enables
+```
+
+### MAR Introduction Structure (lines 20-79):
+
+#### Part 1: Context and Prevailing Belief (lines 20-29)
+```latex
+Autoregressive models are currently the de facto solution to generative
+models in natural language processing [38, 39, 3]. These models predict
+the next word or token in a sequence based on the previous words as input.
+
+Given the discrete nature of languages, the inputs and outputs of these
+models are in a categorical, discrete-valued space.
+
+This prevailing approach has led to a widespread belief that autoregressive
+models are inherently linked to discrete representations.
+```
+
+**Technique**: Start from uncontroversial facts (NLP success) → Show how they led to a belief (AR ↔ discrete)
+
+#### Part 2: Consequences of the Belief (lines 25-29)
+```latex
+As a result, research on generalizing autoregressive models to continuous-
+valued domains—most notably, image generation—has intensely focused on
+discretizing the data [6, 13, 40].
+
+A commonly adopted strategy is to train a discrete-valued tokenizer on
+images, which involves a finite vocabulary obtained by vector quantization
+(VQ) [51, 41, 41].
+```
+
+**Technique**: Show concrete consequences of the belief (VQ tokenizers everywhere)
+
+#### Part 3: The Research Question (lines 30-33)
+```latex
+In this work, we aim to address the following question:
+
+Is it necessary for autoregressive models to be coupled with vector-
+quantized representations?
+```
+
+**Technique**: Explicit question format. Bold, direct, centered.
+
+#### Part 4: Analyzing What's Essential (lines 31-37)
+```latex
+We note that the autoregressive nature, i.e., "predicting next tokens based
+on previous ones", is independent of whether the values are discrete or
+continuous.
+
+What is needed is to model the per-token probability distribution, which
+can be measured by a loss function and used to draw samples from.
+
+Discrete-valued representations can be conveniently modeled by a categorical
+distribution, but it is not conceptually necessary.
+```
+
+**Technique**: Distinguish mechanism (autoregression) from implementation (discrete tokens)
+
+#### Part 5: The "If..." Condition (lines 36-37)
+```latex
+If alternative models for per-token probability distributions are presented,
+autoregressive models can be approached without vector quantization.
+```
+
+**Technique**: Logical bridge - "If X, then Y" sets up your contribution
+
+#### Part 6: Your Solution (lines 38-58)
+```latex
+With this observation, we propose to model the per-token probability
+distribution by a diffusion procedure operating on continuous-valued domains.
+
+[Technical description of Diffusion Loss...]
+```
+
+#### Part 7: Benefits and Implications (lines 59-79)
+```latex
+Our approach eliminates the need for discrete-valued tokenizers. Vector-
+quantized tokenizers are difficult to train and are sensitive to gradient
+approximation strategies [51, 41, 40, 27]. Their reconstruction quality
+often falls short compared to continuous-valued counterparts [42].
+
+Our approach allows autoregressive models to enjoy the benefits of higher-
+quality, non-quantized tokenizers.
+
+[...]
+
+The effectiveness of our method reveals a largely uncharted realm of image
+generation: modeling the interdependence of tokens by autoregression,
+jointly with the per-token distribution by diffusion.
+```
+
+**Technique**: Multiple benefit levels:
+- **Eliminates pain points**: VQ is hard to train
+- **Enables benefits**: Higher-quality tokenizers
+- **Reveals new territory**: "uncharted realm"
+
+---
+
+## 3. Rethinking Section: Dissecting the Status Quo
+
+### Pattern: "Rethinking X" Chapter
+
+When challenging conventional wisdom, dedicate a section to re-examining the assumptions.
+
+### MAR Section 3.1: "Rethinking Discrete-Valued Tokens"
+
+**Structure**:
+```markdown
+1. [Setup] Define the conventional approach mathematically
+2. [Analysis] Identify the essential properties (not the implementation)
+3. [Insight] Show that the implementation is not necessary
+4. [Conclusion] State what's actually needed
+```
+
+### MAR Example (lines 110-126):
+
+#### Step 1: Define the Conventional Approach
+```latex
+To begin with, we revisit the roles of discrete-valued tokens in
+autoregressive generation models.
+
+Denote as x the ground-truth token to be predicted at the next position.
+With a discrete tokenizer, x can be represented as an integer: 0 ≤ x < K,
+with a vocabulary size K.
+
+The autoregressive model produces a continuous-valued D-dim vector z ∈ R^D,
+which is then projected by a K-way classifier matrix W ∈ R^(K×D).
+
+Conceptually, this formulation models a categorical probability distribution
+in the form of p(x|z) = softmax(Wz).
+```
+
+**Technique**: Mathematically precise setup. Shows you deeply understand the status quo.
+
+#### Step 2: Identify Essential Properties
+```latex
+In the context of generative modeling, this probability distribution must
+exhibit two essential properties.
+
+(i) A loss function that can measure the difference between the estimated
+and true distributions.
+
+(ii) A sampler that can draw samples from the distribution x ~ p(x|z) at
+inference time.
+```
+
+**Technique**: Abstract away from implementation to functional requirements
+
+#### Step 3: Show Convention is Not Necessary
+```latex
+In the case of categorical distribution, this can be simply done by the
+cross-entropy loss.
+
+[...]
+
+This analysis suggests that discrete-valued tokens are not necessary for
+autoregressive models.
+```
+
+**Technique**: "This analysis suggests" - evidence-based conclusion
+
+#### Step 4: What's Actually Needed
+```latex
+Instead, it is the requirement of modeling a distribution that is essential.
+
+A discrete-valued token space implies a categorical distribution, whose loss
+function and sampler are simple to define.
+
+What we actually need are a loss function and its corresponding sampler for
+distribution modeling.
+```
+
+**Technique**: Reposition the problem - it's about distribution modeling, not discrete tokens
+
+---
+
+## 4. Unifying Different Methods: The "Generalized Framework" Pattern
+
+### Pattern: Show Two Seemingly Different Methods Are Special Cases
+
+**MAR Section 3.4**: "Unifying Autoregressive and Masked Generative Models"
+
+**Structure**:
+```markdown
+1. [Observation] These methods seem different but share a core principle
+2. [Insight] The core principle is X, not the surface differences
+3. [Demonstration] Show how Method A fits the principle
+4. [Demonstration] Show how Method B fits the principle
+5. [Unification] Both are special cases of a general framework
+6. [Benefit] This understanding enables new combinations/insights
+```
+
+### MAR Example (lines 181-259):
+
+#### Opening Observation
+```latex
+We show that masked generative models, e.g., MaskGIT [4] and MAGE [29],
+can be generalized under the broad concept of autoregression, i.e.,
+next token prediction.
+```
+
+#### The Insight
+```latex
+Bidirectional attention can perform autoregression.
+
+The concept of autoregression is orthogonal to network architectures:
+autoregression can be done by RNNs [50], CNNs [49, 7], and
+Transformers [38, 36, 6].
+
+When using Transformers, although autoregressive models are popularly
+implemented by causal attention, we show that they can also be done by
+bidirectional attention.
+```
+
+**Technique**: "Orthogonal to" - shows independence of concepts
+
+#### Visual Demonstration (Figure 2)
+```latex
+Note that the goal of autoregression is to predict the next token given
+the previous tokens; it does not constrain how the previous tokens
+communicate with the next token.
+
+We can adopt the bidirectional attention implementation as done in Masked
+Autoencoder (MAE) [21]. See Figure 2(b).
+```
+
+**Technique**: Use visualization to show "unexpected" equivalence
+
+#### Generalization (Figure 3)
+```latex
+Autoregressive models in random orders...
+
+Masked autoregressive models: In masked generative modeling [4, 29],
+the models predict a random subset of tokens based on known/predicted
+tokens.
+
+This can be formulated as permuting the token sequence by a random order,
+and then predicting multiple tokens based on previous tokens.
+
+Conceptually, this is an autoregressive procedure...
+```
+
+**Technique**: Mathematical unification - same formula, different instantiations
+
+#### Naming the Generalization
+```latex
+We refer to this variant as Masked Autoregressive (MAR) models.
+
+MAR is a random-order autoregressive model that can predict multiple
+tokens simultaneously.
+```
+
+**Technique**: Define new terminology to capture the unified concept
+
+---
+
+## 5. Flexibility Arguments: Demonstrating Generality
+
+### Pattern: Show Your Method Works in Many Configurations
+
+**MAR Section 5.1**: "Flexibility of Diffusion Loss"
+
+**Structure**:
+```markdown
+1. [Claim] One significant advantage is flexibility with various X
+2. [Demonstration 1] Works with configuration A
+3. [Demonstration 2] Works with configuration B (even surprising case)
+4. [Demonstration 3] Works with configuration C
+5. [Insight] This flexibility enables Y
+```
+
+### MAR Example (lines 349-366, Table 2):
+
+#### Opening Claim
+```latex
+One significant advantage of Diffusion Loss is its flexibility with
+various tokenizers.
+```
+
+#### Surprising Case 1: VQ Tokenizers
+```latex
+Diffusion Loss can be easily used even given a VQ tokenizer.
+
+We simply treat the continuous-valued latent before the VQ layer as the
+tokens.
+
+This variant gives us 7.82 FID (w/o CFG), compared favorably with 8.79
+FID (Table 1) of cross-entropy loss using the same VQ tokenizer.
+```
+
+**Technique**: Show it works even with the "wrong" tokenizer type (VQ)
+
+#### Surprising Case 2: Mismatched Stride
+```latex
+Interestingly, Diffusion Loss also enables us to use tokenizers with
+mismatched strides.
+
+In Table 2, we study a KL-8 tokenizer whose stride is 8 and output
+sequence length is 32×32. Without increasing the sequence length of the
+generator, we group 2×2 tokens into a new token.
+
+Despite the mismatch, we are able to obtain decent results...
+```
+
+**Technique**: "Interestingly" + "Despite the mismatch" - emphasizes robustness
+
+#### Surprising Case 3: Different Architecture
+```latex
+Further, this property allows us to investigate other tokenizers, e.g.,
+Consistency Decoder [35], a non-VQ tokenizer of a different
+architecture/stride designed for different goals.
+```
+
+**Technique**: Show applicability beyond original design space
+
+#### Comprehiveness Check
+```latex
+For comprehensiveness, we also train a KL-16 tokenizer on ImageNet using
+the code of [42]...
+```
+
+**Technique**: "For comprehensiveness" - signals thoroughness
+
+---
+
+## 6. Speed/Accuracy Trade-offs: Plotting the Pareto Frontier
+
+### Pattern: Show Your Method Dominates or Expands the Frontier
+
+**MAR Section 5.2**: "Speed/accuracy Trade-off" (Figure 6)
+
+**Structure**:
+```markdown
+1. [Setup] We enjoy flexibility of X
+2. [Visualization] Figure/plot showing trade-off curves
+3. [Comparison 1] Compare within your variants
+4. [Comparison 2] Compare to other methods
+5. [Highlight] Notable point on the curve
+```
+
+### MAR Example (lines 425-474):
+
+#### Setup
+```latex
+Following MaskGIT [4], our MAR enjoys the flexibility of predicting
+multiple tokens at a time.
+
+This is controlled by the number of autoregressive steps at inference time.
+```
+
+#### Comparison Within Family
+```latex
+Figure 6 plots the speed/accuracy trade-off.
+
+MAR has a better trade-off than its AR counterpart, noting that AR is
+with the efficient kv-cache.
+```
+
+**Technique**: "noting that..." - acknowledge AR's advantage, still win
+
+#### Comparison to Other Methods
+```latex
+With Diffusion Loss, MAR also shows a favorable trade-off in comparison
+with the recently popular Diffusion Transformer (DiT) [37].
+
+As a latent diffusion model, DiT models the interdependence of all tokens
+by the diffusion process. The speed/accuracy trade-off of DiT is mainly
+controlled by its diffusion steps.
+
+Unlike our diffusion process on a small MLP, the diffusion process of DiT
+involves the entire Transformer architecture.
+
+Our method is more accurate and faster.
+```
+
+**Technique**: Explain *why* you win (architectural difference)
+
+#### Highlight Specific Point
+```latex
+Notably, our method can generate at a rate of <0.3 second per image with
+a strong FID of <2.0.
+```
+
+**Technique**: "Notably" + impressive numbers - memorable takeaway
+
+---
+
+## 7. System-Level Comparison Tables: Organizing by Categories
+
+### Pattern: Group Related Work, Highlight Your Position
+
+**MAR Table 4**: "System-level comparison on ImageNet 256×256"
+
+**Structure**:
+```markdown
+Table 4:
+┌─────────────────────────────────┐
+│ Category 1: pixel-based         │
+│ - Method A                      │
+│ - Method B                      │
+├─────────────────────────────────┤
+│ Category 2: vector-quantized     │
+│ - Method C                      │
+│ - Method D                      │
+├─────────────────────────────────┤
+│ Category 3: continuous-valued   │
+│ - Method E                      │
+│ - Method F                      │
+├─────────────────────────────────┤
+│ **Your Methods** (highlighted)  │
+│ - Your Method B                 │
+│ - Your Method L                 │
+│ - Your Method H                 │
+└─────────────────────────────────┘
+```
+
+### MAR Example (lines 448-470):
+
+```latex
+Table 4: System-level comparison on ImageNet 256×256 conditional
+generation. Diffusion Loss enables Masked Autoregression to achieve
+leading results in comparison with previous systems.
+
+†: LDM operates on continuous-valued tokens, though this result uses
+a quantized tokenizer.
+
+w/o CFG              w/ CFG
+#params  FID↓  IS↑  Pre.↑  Rec.↑  FID↓  IS↑  Pre.↑  Rec.↑
+────────────────────────────────────────────────────────────
+pixel-based
+ADM [10]      554M  10.94  101.0  0.69  0.63  4.59  186.7  0.82  0.52
+VDM++ [26]    2B    2.40   225.3   -     -     2.12  267.7   -     -
+
+vector-quantized tokens
+Autoreg. w/ VQGAN [13]  1.4B  15.78  78.3   -     -     -     -      -     -
+MaskGIT [4]             227M   6.18  182.1  0.80  0.51   -     -      -     -
+MAGE [29]               230M   6.93  195.8   -     -     -     -      -     -
+MAGVIT-v2 [55]          307M   3.65  200.5   -     -     1.78  319.4   -     -
+
+continuous-valued tokens
+LDM-4† [42]            400M  10.56  103.5  0.71  0.62  3.60  247.7  0.87  0.48
+U-ViT-H/2-G [2]        501M    -     -      -     -     2.29  263.9  0.82  0.57
+DiT-XL/2 [37]          675M   9.62  121.5  0.67  0.67  2.27  278.2  0.83  0.57
+[... other methods ...]
+
+MAR-B, Diff Loss       208M   3.48  192.4  0.78  0.58  2.31  281.7  0.82  0.57
+MAR-L, Diff Loss       479M   2.60  221.4  0.79  0.60  1.78  296.0  0.81  0.60
+MAR-H, Diff Loss       943M   2.35  227.8  0.79  0.62  1.55  303.7  0.81  0.62
+```
+
+**Key Techniques**:
+1. **Group by paradigm**: pixel-based → VQ → continuous-valued
+2. **Show progression**: Earlier methods → later methods → yours
+3. **Multiple metrics**: FID, IS, Precision, Recall (comprehensive)
+4. **Multiple configurations**: w/o CFG and w/ CFG (fair comparison)
+5. **Model sizes**: Show efficiency (your 479M vs their 675M)
+6. **Dagger (†)**: Clarify edge cases (LDM uses quantized despite continuous)
+7. **Bold your methods**: Visual separation (here shown as last group)
+
+---
+
+## 8. Admitting Limitations: Honest and Specific
+
+### Pattern: Acknowledge Weaknesses Proactively
+
+**MAR Appendix A**: "Limitations and Broader Impacts"
+
+**Structure**:
+```markdown
+Limitations:
+1. [Specific artifact] Show example (Figure 8)
+2. [Dependency] Acknowledge reliance on external components
+3. [Scope] Be honest about what you haven't tested
+
+Broader Impacts:
+1. [Positive] How this advances the field
+2. [Positive] Practical applications
+3. [Negative] Potential misuse considerations
+```
+
+### MAR Limitations Example (lines 629-642):
+
+#### Limitation 1: Visual Artifacts
+```latex
+First of all, our image generation system can produce images with
+noticeable artifacts (Figure 8).
+
+This limitation is commonly observed in existing methods, especially when
+trained on controlled, academic data (e.g., ImageNet).
+
+Research-driven models trained on ImageNet still have a noticeable gap
+in visual quality in comparison with commercial models trained on
+massive data.
+```
+
+**Techniques**:
+- Show example (Figure 8) - transparency
+- "commonly observed" - you're not uniquely bad
+- Contextualize: academic vs. commercial data
+
+#### Limitation 2: External Dependency
+```latex
+Second, our image generation system relies on existing pre-trained
+tokenizers.
+
+The quality of our system can be limited by the quality of these tokenizers.
+
+Pre-training better tokenizers is beyond the scope of this paper.
+```
+
+**Techniques**:
+- "relies on" - direct acknowledgment
+- "can be limited by" - honest constraint
+- "beyond the scope" - boundary setting
+
+#### Limitation 3: Unexplored Territory
+```latex
+Last, we note that given the limited computational resources, we have
+primarily tested our method on the ImageNet benchmark.
+
+Further validation is needed to assess the scalability and robustness
+of our approach in more diverse and real-world scenarios.
+```
+
+**Techniques**:
+- "given limited computational resources" - context, not excuse
+- "primarily tested" - honesty about scope
+- "Further validation is needed" - explicit call for future work
+
+---
+
+## 9. Visionary Conclusion: The "We Hope" Pattern
+
+### Pattern: End with Forward-Looking Impact Statement
+
+**MAR Conclusion** (lines 482-488):
+
+```latex
+The effectiveness of Diffusion Loss on various autoregressive models
+suggests new opportunities: modeling the interdependence of tokens by
+autoregression, jointly with the per-token distribution by diffusion.
+
+This is unlike the common usage of diffusion that models the joint
+distribution of all tokens.
+
+Our strong results on image generation suggest that autoregressive models
+or their extensions are powerful tools beyond language modeling.
+
+These models do not need to be constrained by vector-quantized
+representations.
+
+We hope our work will motivate the research community to explore sequence
+models with continuous-valued representations in other domains.
+```
+
+### Structure:
+```markdown
+1. [Synthesis] What your results suggest (new opportunities)
+2. [Contrast] How this differs from common usage
+3. [Generalization] Broader lesson (powerful tools beyond X)
+4. [Freedom] Removing constraints (not limited by Y)
+5. [Call to action] "We hope" - inspire future work
+```
+
+### Key Phrases:
+- "suggests new opportunities"
+- "unlike the common usage"
+- "powerful tools beyond [original domain]"
+- "do not need to be constrained by"
+- "We hope our work will motivate..."
+
+---
+
+## 10. Writing Style: Kaiming He's Signature Techniques
+
+### Tone Characteristics:
+
+1. **Respectful but Firm Challenge**
+   - "Conventional wisdom holds that..." (respect)
+   - "...is not a necessity" (firm)
+   - Never dismissive of prior work
+
+2. **Empirically Grounded**
+   - "We observe that..." (not "We believe that...")
+   - "This analysis suggests..." (evidence-based)
+   - Show, don't just tell
+
+3. **Logical Precision**
+   - Distinguish mechanism from implementation
+   - "If X, then Y" formulations
+   - Mathematical clarity in conceptual arguments
+
+4. **Measured Claims**
+   - "can be" not "is always"
+   - "suggests" not "proves"
+   - "enables" not "guarantees"
+
+5. **Generous Citation**
+   - Cite the methods you're challenging
+   - Credit relevant prior work
+   - "following [X]" for techniques you adopt
+
+### Sentence Patterns:
+
+**Challenge Patterns:**
+- "Conventional wisdom holds that [X]. We observe that [Y]."
+- "This prevailing approach has led to a widespread belief that [X]."
+- "[X] is not a necessity for [Y]."
+- "What is needed is [X], not [Y]."
+
+**Insight Patterns:**
+- "The concept of [X] is orthogonal to [Y]."
+- "This is unlike the common usage of [X]."
+- "These models do not need to be constrained by [X]."
+- "This reveals a largely uncharted realm of [X]."
+
+**Demonstration Patterns:**
+- "Interestingly, [X] also enables us to..."
+- "Despite [challenge], we are able to..."
+- "For comprehensiveness, we also..."
+- "Notably, [impressive result]."
+
+**Vision Patterns:**
+- "We hope this work will motivate..."
+- "This suggests new opportunities for..."
+- "We believe this will be beneficial to..."
+- "Further validation is needed to assess..."
+
+---
+
+## 11. Figures: Making the Invisible Visible
+
+### Pattern: Use Visualization to Show Conceptual Equivalence
+
+**MAR Figure 2**: "Bidirectional attention can do autoregression"
+
+**Purpose**: Challenge the assumption that autoregression requires causal attention
+
+**Technique**: Side-by-side comparison
+- (a) Causal attention (conventional)
+- (b) Bidirectional attention (surprising but equivalent)
+
+**Annotation Key Points**:
+- "next token prediction" (highlight both do this)
+- "loss on [all/unknown] tokens" (show the difference)
+- Arrows showing attention flow
+
+**MAR Figure 3**: "Generalized Autoregressive Models"
+
+**Purpose**: Show AR, random-order AR, and MAR are special cases
+
+**Technique**: Progressive generalization
+- (a) AR, raster order (standard)
+- (b) AR, random order (small variation)
+- (c) Masked AR (generalization)
+
+**Visual Language**:
+- "unknown" vs "known/predicted" tokens
+- Color coding for what's predicted at each step
+- Consistent notation across panels
+
+**Key**: Figures should make conceptual contributions visually obvious
+
+---
+
+## 12. Related Work: Positioning Within Traditions
+
+### Pattern: Acknowledge Lineage, Show Departure
+
+**MAR Section 2**: "Related Work" (lines 80-103)
+
+**Structure**:
+```markdown
+1. [Main tradition] Sequence Models for Image Generation
+   - Pioneering work [citations]
+   - Your position: "Related to our work, [X] also focuses on..."
+   - Contrast: "In [X], [limitation]. In contrast, our method..."
+
+2. [Adjacent tradition] Diffusion for Representation Learning
+   - Prior work [citations]
+   - Clarify difference: "These efforts have been focused on [X],
+     rather than [Y]"
+   - "In their scenarios, [goal] is not a goal"
+
+3. [Conceptual connection] Diffusion for Policy Learning
+   - "Our work is conceptually related to [X]"
+   - Analogy: "In image generation, we can think of [X] as [Y]"
+   - Difference: "Despite this conceptual connection, [key difference]"
+```
+
+### Key Techniques:
+
+1. **"Related to our work..."** - Acknowledge contemporaries generously
+2. **"In contrast..."** - Sharp differentiation
+3. **"These efforts have been focused on..."** - Respectful boundary setting
+4. **"In their scenarios..."** - Contextualize differences
+5. **"Conceptually related to..."** - Find connections across domains
+6. **"We can think of X as Y"** - Analogies for insight
+7. **"Despite this conceptual connection..."** - Honest differences
+
+---
+
+## 13. Experiments: Ablation as Storytelling
+
+### Pattern: Experiments Should Validate the "Rethinking"
+
+**MAR Section 5**: Experiments organized to prove the rethinking
+
+**Flow**:
+```markdown
+1. Properties of Diffusion Loss (prove the alternative works)
+   - vs Cross-entropy (Table 1)
+   - Flexibility (Table 2)
+   - Ablations (Table 3, Figures 4-5)
+
+2. Properties of Generalized AR (prove the unification)
+   - From AR to MAR (Table 1)
+   - Speed/accuracy trade-off (Figure 6)
+
+3. Benchmarking (prove the results are strong)
+   - System-level comparison (Table 4)
+   - Qualitative results (Figure 7)
+```
+
+**Key**: Every experiment should map to a conceptual claim from the introduction
+
+### Table Design Patterns:
+
+**Comparison Tables** (Table 1):
+- Group by variants (AR → MAR)
+- Show progression (Cross-ent → Diff Loss)
+- Multiple configurations (w/ and w/o CFG)
+- Clear metrics (FID↓, IS↑)
+
+**Flexibility Tables** (Table 2):
+- "Flexibility of X" in caption
+- Diverse configurations
+- Architecture variations
+- Stride mismatches
+- rFID column (reconstruction quality)
+
+**Ablation Tables** (Table 3):
+- One dimension varied (MLP width)
+- Show params and inference time
+- Demonstrate efficiency
+- Default row marked (implicit)
+
+---
+
+## 14. Summary: The "Rethinking" Paper Template
+
+### When to Use This Pattern:
+
+**Your paper is a "Rethinking" paper if:**
+- You challenge a widely-held assumption in your field
+- You show two seemingly different methods are equivalent
+- You propose removing a "necessary" component
+- You unify multiple approaches under one framework
+
+**Not a "Rethinking" paper if:**
+- You propose an incremental improvement
+- You combine existing techniques without conceptual insight
+- You're optimizing within an established paradigm
+
+### Abstract Template:
+```
+Conventional wisdom holds that [assumption]. We observe that [nuance].
+Is it necessary for [assumption]? In this work, we propose [alternative].
+Rather than [traditional method], we [novel method]. This approach [benefits].
+We hope this work will [vision].
+```
+
+### Introduction Template:
+```
+[Context] Domain X uses method Y widely.
+[Belief] This has led to belief that Z.
+[Question] Is Z necessary?
+[Analysis] What's actually needed is W.
+[Proposal] We propose alternative that achieves W.
+[Results] Strong performance + [specific benefit].
+[Vision] This opens new direction.
+```
+
+### Section Template ("Rethinking X"):
+```
+1. Define the conventional approach mathematically
+2. Identify essential properties (2-3 items)
+3. Show conventional is just one way to achieve these
+4. State what's actually needed (abstract)
+5. Position your method as satisfying these needs
+```
+
+### Unification Template:
+```
+[Observation] Methods A and B seem different.
+[Insight] But they share core principle X.
+[Demonstration] Show A = X + implementation Y
+[Demonstration] Show B = X + implementation Z
+[Generalization] Both are special cases of framework F
+[Benefit] This understanding enables [new possibilities]
+```
+
+### Conclusion Template:
+```
+Our results suggest [new opportunity].
+Unlike common usage that [old approach], we show [new insight].
+This reveals [uncharted realm].
+We hope this will motivate [future direction].
+```
+
+---
+
+## 15. Common Pitfalls to Avoid
+
+### ❌ Don't:
+- Dismiss prior work as "wrong" - it led to the belief for good reasons
+- Overstate your case - "completely eliminates" vs "reduces the need for"
+- Ignore implementation realities - VQ was solving real problems
+- Be vague about alternatives - show concrete alternatives
+- Forget to cite the methods you're challenging
+
+### ✅ Do:
+- Acknowledge why the conventional wisdom emerged
+- Show limitations respectfully
+- Provide rigorous comparisons
+- Admit when your method doesn't help
+- Credit relevant work extensively
+- Use precise language ("can be" not "is always")
+
+---
+
+## 16. Citation Integration Examples
+
+### How MAR Cites Challenges:
+
+**Citing the method you're improving:**
+```latex
+Discrete tokenizers are difficult to train [51, 41, 40, 27].
+```
+- Multiple citations show it's a known problem
+- Recent citations ([27]) show it's still active
+
+**Citing contemporaries:**
+```latex
+Related to our work, the recent work on GIVT [48] also focuses on
+continuous-valued tokens in sequence models.
+
+GIVT and our work both reveal the significance and potential of this
+direction.
+
+In GIVT, [limitation]. In contrast, our method [advantage].
+```
+- "Related to our work" - generous positioning
+- "both reveal" - shared progress
+- "In contrast" - clear differentiation
+
+**Citing the technique you adapt:**
+```latex
+We can adopt the bidirectional attention implementation as done in
+Masked Autoencoder (MAE) [21].
+
+See Figure 2(b).
+```
+- "as done in" - giving credit
+- Reference to figure for transparency
+
+---
+
+## 17. Revision Checklist for "Rethinking" Papers
+
+**Before Submission, Verify:**
+
+- [ ] Abstract clearly states the challenged assumption
+- [ ] Introduction asks an explicit question ("Is it necessary...")
+- [ ] "Rethinking" section dissects the status quo rigorously
+- [ ] Unification section shows mathematical/conceptual equivalence
+- [ ] Experiments directly test the rethinking claims
+- [ ] Flexibility/robustness experiments included
+- [ ] Limitations acknowledge remaining challenges
+- [ ] Related work cites challenged methods generously
+- [ ] Conclusion frames work as opening new territory
+- [ ] Figures make conceptual contributions visible
+- [ ] Tone is respectful but firm
+- [ ] Claims are measured ("can be", "suggests", not "proves")
+- [ ] Vision statement inspires without overpromising
+
+---
+
+## 18. Example: Applying This Pattern
+
+### Original Idea (Not "Rethinking"):
+"We propose a new tokenizer that improves image quality by 10%."
+
+### Rethinking Version:
+"Conventional wisdom holds that autoregressive models require
+vector-quantized tokenizers. We observe that VQ introduces artifacts and
+training difficulties. Is quantization necessary for autoregressive
+image generation? We propose Diffusion Loss that enables continuous-valued
+tokens, eliminating VQ while achieving SOTA results. We hope this will
+motivate continuous-valued modeling in other domains."
+
+**The Rethinking Frame:**
+- Assumption: AR ↔ VQ (challenged)
+- Insight: What's needed is distribution modeling, not discreteness
+- Alternative: Diffusion Loss for continuous tokens
+- Benefit: Eliminates VQ artifacts + enables better tokenizers
+- Vision: Continuous-valued AR beyond images
+
+---
+
+## Paper Metadata
+
+**Title**: Autoregressive Image Generation without Vector Quantization
+
+**Authors**: Tianhong Li, Yonglong Tian, He Li, Mingyang Deng, Kaiming He
+
+**Venue**: NeurIPS 2024 (Spotlight)
+
+**arXiv**: 2406.11838
+
+**Code**: https://github.com/LTH14/mar
+
+**Key Citations**:
+- VQ-VAE [51]: Original VQ paper
+- MaskGIT [4], MAGE [29]: Masked generative models
+- MAE [21]: Bidirectional attention implementation
+- DiT [37]: Speed/accuracy comparison
+- LDM [42]: Tokenizer source
+
+---
+
+## Extracted by
+
+**Date**: 2025-01-26
+
+**Source**: Full PDF analysis of arXiv:2406.11838v3
+
+**Extraction Focus**: Writing patterns for challenging conventional wisdom,
+unifying methods, and structuring "rethinking" papers
+
+**For Integration**: ml-paper-writing skill knowledge base
diff --git a/.agents/skills/ml-paper-writing/references/knowledge/review-response.md b/.agents/skills/ml-paper-writing/references/knowledge/review-response.md
new file mode 100644
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+# Review Response and Rebuttal Strategies
+
+This file contains effective strategies for responding to reviewer comments and addressing reviewer concerns, extracted from successful ML conference paper rebuttals.
+
+---
+
+## General Rebuttal Principles
+
+### Core Philosophy
+**Source:** Analysis of successful NeurIPS/ICML rebuttals
+
+**Key Principles:**
+1. **Respectful tone**: Thank reviewers for their time
+2. **Direct addressing**: Respond point-by-point to each concern
+3. **Evidence-based**: Support claims with data, experiments, or citations
+4. **Concise communication**: Be clear but brief
+5. **No over-committing**: Only promise what can be done
+
+### Response Structure
+
+**Template:**
+```markdown
+# Response to Reviewer [Number]
+
+Thank you for this insightful comment. We [address the concern].
+
+[Specific response to concern].
+
+[Additional evidence/experiments if needed].
+
+We have revised the manuscript to clarify this point (see changes marked in blue).
+```
+
+---
+
+## Addressing Specific Concerns
+
+### Concern: Clarity Issues
+
+**Strategy:**
+- Acknowledge the confusion
+- Clarify with revised text
+- Add examples if helpful
+
+**Template:**
+```markdown
+# Response to Clarity Concern
+
+We apologize for the confusion. The original text was:
+
+[Original unclear text]
+
+We have revised this to:
+"Revised text with clearer explanation"
+
+We also added an example (Figure X) to illustrate this concept.
+```
+
+**Real Example:**
+- **Concern:** "The algorithm description is unclear."
+- **Response:** "We've rewritten Algorithm 1 with more detailed steps and added pseudocode. We also included a concrete example in Appendix B to illustrate the algorithm's execution."
+
+### Concern: Missing Experiments
+
+**Strategy:**
+- Assess whether experiment is feasible
+- If yes: add experiment and report results
+- If not: explain why experiment is not essential
+- Offer alternative evidence if possible
+
+**Template:**
+```markdown
+# Response to Missing Experiment Request
+
+We agree that [experiment] would strengthen the evaluation. We have:
+[Option 1: Added experiment and results]
+OR
+[Option 2: Explained why not essential with alternative evidence]
+
+We believe this addresses the concern while maintaining focus on our core contribution.
+```
+
+**Real Example:**
+- **Concern:** "Add comparison with Method X on dataset Y."
+- **Response:** "We've added results on dataset Y (Table 3). Our method outperforms Method X by 5%. We also include ablation showing our improvement comes from [feature], not just better optimization."
+
+### Concern: Statistical Significance
+
+**Strategy:**
+- Add statistical tests if appropriate
+- Report confidence intervals
+- Discuss practical significance vs statistical significance
+- Note sample size limitations
+
+**Template:**
+```markdown
+# Response to Statistical Significance
+
+We agree statistical testing is important. We have:
+- Added paired t-test results showing significance (p<0.01)
+- Included 95% confidence intervals in Figure 3
+- Reported standard deviations across 5 runs
+- Noted that while some differences are not statistically significant due to sample size, they are practically meaningful for [application]
+
+We have updated Section 4.2 with these statistical details.
+```
+
+### Concern: Insufficient Baselines
+
+**Strategy:**
+- Add missing baselines if available
+- Explain why certain baselines are inappropriate
+- Cite reasons for exclusions with references
+
+**Template:**
+```markdown
+# Response to Baseline Concern
+
+We have added comparisons with:
+- [Method A]: Added in Table 2
+- [Method B]: Excluded because [reason with citation]
+
+For Method B, while it seems related, it [specific reason why not comparable], making direct comparison inappropriate.
+```
+
+### Concern: Writing Quality
+
+**Strategy:**
+- Revise problematic text
+- Fix grammatical issues
+- Improve flow and clarity
+- Add signposting
+
+**Template:**
+```markdown
+# Response to Writing Concern
+
+We've revised the writing to address your concerns:
+- Restructured Section 3 for better flow
+- Fixed typos and grammar
+- Added transition sentences between paragraphs
+- Clarified technical terminology
+
+The revised manuscript has been proofread and edited for clarity.
+```
+
+### Concern: Overclaiming
+
+**Strategy:**
+- Tone down absolute statements
+- Add qualifications where appropriate
+- Acknowledge limitations more explicitly
+- Reframe claims to match evidence
+
+**Template:**
+```markdown
+# Response to Overclaiming Concern
+
+We accept that our original claim was too strong. We have revised the text:
+
+Original: "Our method achieves state-of-the-art on all tasks."
+Revised: "Our method achieves state-of-the-art on [specific tasks] and competitive performance on [other tasks]."
+
+We also added a Limitations section acknowledging that our method may not generalize to [condition].
+```
+
+---
+
+## Tone and Phrasing Patterns
+
+### Opening Statements
+
+**Thanking:**
+- "Thank you for this insightful comment."
+- "We appreciate the reviewer's suggestion to..."
+- "We thank the reviewer for pointing this out."
+
+**Acknowledging Valid Points:**
+- "The reviewer is right that..."
+- "We agree this is a limitation."
+- "This is an excellent suggestion."
+
+### Addressing Disagreements
+
+**Respectful Disagreement:**
+- "We respectfully disagree with this assessment based on..."
+- "While we understand the concern, our results suggest..."
+- "We believe our approach is justified because..."
+
+**Providing Evidence:**
+- "Our experimental results (Table 3) show..."
+- "As shown in Figure 4, the difference is..."
+- "This is supported by prior work [Citation]."
+
+### Making Commitments
+
+**Full Commitments:**
+- "We will add this experiment in the revised version."
+- "We have added additional ablation studies in Section 5."
+
+**Partial Commitments:**
+- "We have added clarification in the appendix due to space constraints."
+- "We've expanded discussion of this point in the revision."
+
+**Declining Requests:**
+- "Unfortunately, due to [constraint], we cannot add this experiment."
+- "This would require substantial additional resources beyond our current scope."
+- "We believe this is beyond the scope of the current paper but note it as future work."
+
+---
+
+## Common Rebuttal Strategies
+
+### Strategy: Organized Response
+
+**Structure:**
+```markdown
+# Summary of Changes
+
+We thank the reviewers for their constructive feedback. In this response, we:
+- [Major change 1]
+- [Major change 2]
+- [Improvement 3]
+
+We believe these changes have significantly strengthened the paper.
+
+# Response to Reviewer 1
+
+[Point-by-point responses]
+
+# Response to Reviewer 2
+
+[Point-by-point responses]
+```
+
+### Strategy: Evidence-Based Arguments
+
+**Template:**
+```markdown
+# Response to Technical Concern
+
+Our approach is valid because:
+1. [Reason 1 with reference/evidence]
+2. [Reason 2 with data/figure]
+3. [Reason 3 with theoretical justification]
+
+This is supported by [Citation], which demonstrates that [fact].
+```
+
+### Strategy: Highlighting Improvements
+
+**Template:**
+```markdown
+# Major Revisions
+
+1. **New Experiments**: Added comparison with [method] on [dataset]
+2. **New Analysis**: Included ablation study in Table 4
+3. **Clarified Writing**: Rewrote Section 3 for clarity
+4. **Added Limitations**: New section 5.2 acknowledging constraints
+
+These additions strengthen our core claims about [contribution].
+```
+
+---
+
+## Venue-Specific Considerations
+
+### NeurIPS
+
+**Emphasis:**
+- Novelty and conceptual contribution
+- Broader impact (lay summary)
+- Reproducibility checklist
+
+**Rebuttal Focus:**
+- How work advances understanding
+- Significance of contribution
+- Ethical considerations
+
+### ICML
+
+**Emphasis:**
+- Methodological rigor
+- Theoretical contributions
+- Broader impact statement
+
+**Rebuttal Focus:**
+- Soundness of methods
+- Theoretical guarantees
+- Practical implications
+
+### ICLR
+
+**Emphasis:**
+- Experimental thoroughness
+- Limitations acknowledgment
+- LLM usage disclosure
+
+**Rebuttal Focus:**
+- Comprehensive evaluation
+- Honest limitation discussion
+- Transparency about methods
+
+### ACL
+
+**Emphasis:**
+- Linguistic appropriateness
+- Ethical considerations
+- Clear limitations
+
+**Rebuttal Focus:**
+- Language quality and appropriateness
+- Data provenance and ethics
+- Practical utility
+
+---
+
+## Tips for Successful Rebuttals
+
+### Before Writing
+
+1. **Understand the concerns**: Read carefully, identify key issues
+2. **Prioritize**: Address major concerns first
+3. **Be realistic**: Only promise what can deliver
+4. **Gather evidence**: Collect data, results, citations
+5. **Coordinate**: Discuss with co-authors if applicable
+
+### While Writing
+
+1. **Be specific**: Reference exact sections, figures, tables
+2. **Be concise**: Keep responses focused and brief
+3. **Be respectful**: Thank reviewers, acknowledge good points
+4. **Be confident**: Defend your work appropriately
+5. **Be honest**: Acknowledge limitations, don't overpromise
+
+### Common Mistakes to Avoid
+
+- **Defensive tone**: Don't argue excessively
+- **Vague responses**: Be specific about changes
+- **Ignoring concerns**: Address every point
+- **Over-promising**: Only commit to feasible additions
+- **Disorganized:**
+- **Poor formatting:** Use clear sections and structure
+- **Rude language:** Maintain professional tone
+
+---
+
+## Rebuttal Examples
+
+### Example 1: Clarity Concern
+
+**Reviewer:** "The method description in Section 3 is unclear and hard to follow."
+
+**Response:**
+```markdown
+We apologize for the confusion. We have rewritten Section 3.2 to clarify our algorithm:
+
+**Original:** "We process the data using our method and get results."
+
+**Revised:** "Our method consists of three stages: (1) We first normalize the input
+features using [technique]. (2) We then apply our core algorithm, which iteratively [process].
+(3) Finally, we post-process the outputs using [method]."
+
+We also added Algorithm 1 with detailed steps and included a concrete example in
+Appendix A. We believe this revision makes the method reproducible and clear.
+```
+
+### Example 2: Missing Baseline
+
+**Reviewer:** "You should compare with Method X (Smith et al., 2022)."
+
+**Response:**
+```markdown
+Thank you for this suggestion. We have added comparisons with Method X in our
+revised manuscript:
+
+**Results in Table 3:** Our method achieves 92% accuracy compared to Method X's
+85% on dataset Y. This 7% improvement demonstrates the value of our [key innovation].
+
+**Ablation Study:** We show in Table 4 that our improvement comes specifically from
+[feature], not just better optimization.
+
+We chose not to include Method Z because [reason with citation].
+```
+
+### Example 3: Overclaiming
+
+**Reviewer:** "The abstract claims 'state-of-the-art' too broadly."
+
+**Response:**
+```markdown
+We accept this critique. Our original claim was too broad. We have revised the
+abstract:
+
+**Original:** "Our method achieves state-of-the-art performance across all tasks."
+
+**Revised:** "Our method achieves state-of-the-art on [specific tasks A and B] (Table 1)
+and competitive performance on [other tasks C and D] (Table 2)."
+
+We also added a Limitations section (Section 5) noting that performance may vary
+across domains and tasks.
+```
+
+---
+
+## Final Checklist
+
+Before submitting rebuttal:
+
+- [ ] All reviewer concerns addressed
+- [ ] Responses are clear and specific
+- [ ] Tone is respectful and professional
+- [ ] Changes are marked in manuscript
+- [ ] Evidence provided for claims
+- [ ] Feasible commitments made
+- [ ] Co-authors agree with responses
+- [ ] Proofread for errors
+- [ ] Check formatting requirements
+
+---
+
+## Notes
+
+- **Learn from successful rebuttals**: Read well-received papers' reviewer exchanges
+- **Practice humility**: Acknowledge mistakes, show willingness to improve
+- **Focus on core contribution**: Defend your main contribution without overclaiming
+- **Keep it concise**: Reviewers are busy; be respectful of their time
+
+**Updates:** This file is periodically updated with new strategies and examples from successful rebuttals.
diff --git a/.agents/skills/ml-paper-writing/references/knowledge/structure.md b/.agents/skills/ml-paper-writing/references/knowledge/structure.md
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+# Paper Structure Patterns
+
+This file contains actionable patterns for organizing ML conference papers, extracted from successful publications.
+
+---
+
+## Introduction Patterns
+
+### Pattern: Contribution Statement Structure
+**Source:** "Attention Is All You Need", NeurIPS (2017)
+**Context:** Introducing the main contribution
+
+**Pattern:**
+1. Start with broader context or problem
+2. Narrow down to specific limitation
+3. Present your approach as solution
+4. State clear contribution upfront
+
+**Example Template:**
+```markdown
+[Context/Problem]: Existing approaches struggle with [limitation] due to [reason].
+
+[Our Approach]: We propose [method name], which [key innovation].
+
+[Contribution]: This achieves [result] and enables [capability].
+```
+
+**Application:** Use this pattern when introducing your main contribution in the first or second paragraph of the introduction.
+
+---
+
+### Pattern: Bulleted Contribution List
+**Source:** "BERT: Pre-training of Deep Bidirectional Transformers", NAACL (2019)
+**Context:** Summarizing contributions for clarity
+
+**Pattern:**
+- Place near end of Introduction (after Related Work)
+- Use 2-4 bullets
+- Each bullet: 1-2 lines max (in two-column format)
+- Start with strong verbs ("We propose", "We demonstrate", "We show")
+
+**Example Template:**
+```markdown
+Our contributions are three-fold:
+- We propose [method], which achieves [result].
+- We demonstrate that [technique] improves [metric].
+- We show that [approach] enables [new capability].
+```
+
+**Application:** Use this when you need to clearly delineate multiple contributions for reviewers.
+
+---
+
+### Pattern: Related Work Organization
+**Source:** "Attention Is All You Need", NeurIPS (2017)
+**Context:** Structuring literature review
+
+**Pattern:**
+- Organize methodologically, not chronologically
+- Group papers by approach/assumption
+- Contrast your approach with each group
+- Use "One line of work uses X whereas we use Y because..."
+
+**Example Template:**
+```markdown
+[Approach Category]: Several approaches use [assumption A] [refs].
+[Contrast]: We adopt [assumption B] because it allows [benefit].
+
+[Alternative Category]: Other methods focus on [aspect C] [refs].
+[Positioning]: We build on this by adding [our innovation].
+```
+
+**Application:** Use this to position your work relative to existing literature without paper-by-paper reviews.
+
+---
+
+## Methods Section Patterns
+
+### Pattern: Algorithm Presentation
+**Source:** "Adam: A Method for Stochastic Optimization", ICLR (2015)
+**Context:** Describing algorithms clearly
+
+**Pattern:**
+1. High-level overview first
+2. Mathematical formulation
+3. Algorithm pseudocode (if complex)
+4. Implementation details
+
+**Example Template:**
+```markdown
+[Overview]: We formulate [problem] as optimization. Let [objective] be our goal.
+
+[Method]: Our approach optimizes [objective] using [technique].
+Specifically, we [algorithm description].
+
+[Algorithm]: The full procedure is shown in Algorithm 1.
+
+[Implementation]: In practice, we [practical details].
+```
+
+**Application:** Use this when presenting novel algorithms or optimization methods.
+
+---
+
+### Pattern: Component Breakdown
+**Source:** "BERT: Pre-training of Deep Bidirectional Transformers", NAACL (2019)
+**Context:** Describing multi-component systems
+
+**Pattern:**
+- Present model architecture first
+- Break down into key components
+- Explain each component's role
+- Show how components interact
+
+**Example Template:**
+```markdown
+[Architecture]: Our model consists of [N components]: [list].
+
+[Component 1]: The [component] module [function].
+[Component 2]: The [component] layer [operation].
+
+[Integration]: These components are stacked sequentially, with [connection pattern].
+```
+
+**Application:** Use this when describing complex architectures with multiple interacting parts.
+
+---
+
+## Results Section Patterns
+
+### Pattern: Quantitative Opening
+**Source:** "BERT: Pre-training of Deep Bidirectional Transformers", NAACL (2019)
+**Context:** Presenting main findings
+
+**Pattern:**
+- Start with strongest quantitative result
+- Use exact numbers and metrics
+- Include comparison to baselines
+- State statistical significance
+
+**Example Template:**
+```markdown
+[Main Result]: Our method achieves [score] on [dataset], improving
+over the previous best of [baseline] by [margin] (p<0.001).
+
+[Comparison]: Compared to baselines:
+- [Method A]: [score]
+- [Method B]: [score]
+- Ours: [score]
+
+[Significance]: Results are averaged over N runs; standard deviations shown in parentheses.
+```
+
+**Application:** Use this to open your Results section with your strongest finding.
+
+---
+
+### Pattern: Table Integration
+**Source:** "Attention Is All All You Need", NeurIPS (2017)
+**Context:** Presenting results in tables
+
+**Pattern:**
+- Bold best results in each column
+- Include direction indicators (↑↓)
+- Provide table caption that stands alone
+- Reference table in text before presenting
+
+**Example Template:**
+```markdown
+Table 1 shows our method's performance. Our model (bold) outperforms
+all baselines across datasets.
+
+[Table content]
+
+As shown in Table 1, we achieve state-of-the-art on [datasets].
+```
+
+**Application:** Use this when presenting comparative results in table format.
+
+---
+
+## Discussion Section Patterns
+
+### Pattern: Limitations First
+**Source:** "Attention Is All You Need", NeurIPS (2017)
+**Context:** Acknowledging limitations proactively
+
+**Pattern:**
+- State limitations clearly in first paragraph
+- Explain why limitations don't undermine core claims
+- Distinguish between limitations and future work
+
+**Example Template:**
+```markdown
+[Limitation Statement]: Our approach has [limitation]. Specifically,
+[constraint].
+
+[Mitigation]: Despite this, our core findings about [main contribution] remain
+valid because [reason].
+
+[Future Work]: Addressing this limitation is an important direction for
+future research.
+```
+
+**Application:** Use this to acknowledge limitations honestly while maintaining paper strength.
+
+---
+
+### Pattern: Broader Impact Framing
+**Source:** "Language Models are Few-Shot Learners", GPT-3 Paper (2020)
+**Context:** Discussing wider implications
+
+**Pattern:**
+- Start with direct implications
+- Expand to related domains
+- Consider societal impact (if appropriate)
+- End with forward-looking statement
+
+**Example Template:**
+```markdown
+[Direct Impact]: Our findings suggest that [implication for domain].
+
+[Broader Implications]: Beyond [specific domain], this approach could
+enable [application in other areas].
+
+[Future Outlook]: As [trend] continues, methods like ours will become
+increasingly important for [reason].
+```
+
+**Application:** Use this when writing the final paragraphs of Discussion or Conclusion.
+
+---
+
+## Transition Patterns
+
+### Pattern: Section Transitions
+**Source:** "Attention Is All You Need", NeurIPS (2017)
+**Context:** Moving between sections
+
+**Pattern:**
+- Introduction → Methods: "We now describe our approach."
+- Methods → Results: "We evaluate our method on [tasks]."
+- Results → Discussion: "These results suggest that [insight]."
+
+**Example Template:**
+```markdown
+[Transition to Methods]: Having established [motivation], we present
+our method.
+
+[Transition to Results]: To validate our approach, we conduct experiments
+on [datasets].
+
+[Transition to Discussion]: The experimental results reveal several insights
+about [phenomenon], which we discuss next.
+```
+
+**Application:** Use these to create smooth transitions between major sections.
+
+---
+
+## Notes
+
+- **Consistency**: Maintain consistent terminology throughout the paper
+- **Flow**: Each section should logically lead to the next
+- **Clarity**: Make structure explicit with signposting
+- **Audience**: Write for tired reviewers - make their job easy
+
+
+
+## 何凯明（Kaiming He）的论文结构模式
+
+> 来源: 分析了何凯明的 19 篇代表性论文
+> 添加时间: {datetime.now().strftime('%Y-%m-%d')}
+
+### 摘要结构模式
+
+何凯明在摘要中常用的开场模式：
+
+**模式 1: 直接陈述贡献**
+```
+We introduce [method name], a [key feature] framework for [task].
+We show that [method] achieves [result] on [dataset].
+```
+
+**模式 2: 问题-解决方案**
+```
+[Problem] is difficult for [task]. We present [solution]
+that addresses this by [key mechanism].
+```
+
+**示例** (来自 ResNet):
+```
+Deeper neural networks are more difficult to train. We present a
+residual learning framework to ease the training of networks that
+are substantially deeper than those used previously.
+```
+
+### 引言结构模式
+
+**三段式引言:**
+1. **问题陈述** (2-3段) - 描述挑战和现有方法
+2. **方法概述** (1-2段) - 简洁介绍解决方案
+3. **主要贡献** (1段) - 列表形式，每条 1-2 行
+
+**贡献列表模式:**
+```markdown
+- 我们提出了 [方法]，解决了 [问题]
+- 我们展示了 [方法] 在 [数据集] 上的 [性能提升]
+- 我们证明了 [原理] 是有效的
+```
+
+### 方法部分结构
+
+何凯明的方法部分通常包含：
+
+1. **符号定义** - 清晰定义所有变量和符号
+2. **问题形式化** - 数学公式表达
+3. **方法描述** - 逐步算法解释
+4. **实现细节** - 网络架构、训练设置
+
+**常用句式:**
+- "Let us consider [变量] as [定义]"
+- "Formally, we define [公式]"
+- "We hypothesize that [假设]"
+- "To the extreme, [极端情况]"
+
+### 实验部分结构
+
+1. **实验设置** - 数据集、评价指标、实现细节
+2. **主要结果** - 核心性能对比
+3. **消融实验** - 组件分析
+4. **可视化分析** - 图表展示
+
+**结果描述模式:**
+- "Table X shows that [结果]"
+- "Fig. Y illustrates that [观察]"
+- "Our method achieves [指标] on [任务]"
+- "This represents a [X]% improvement over baseline"
+
+### 相关工作部分组织
+
+何凯明倾向于**主题式组织**而非时间顺序：
+
+**好的组织方式:**
+- "One line of work uses [方法A] [引用], whereas we use [方法B]"
+- "[方法A] [引用] assumes [假设], but we show [反驳]"
+
+**避免:**
+- "X et al. introduced [方法]. Y et al. improved [方法]"
diff --git a/.agents/skills/ml-paper-writing/references/knowledge/submission-guides.md b/.agents/skills/ml-paper-writing/references/knowledge/submission-guides.md
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+# Submission Guides and Venue Requirements
+
+This file contains venue-specific submission requirements, formatting guidelines, and checklist items extracted from ML conference and journal publications.
+
+---
+
+## NeurIPS Requirements
+
+### Format Requirements
+**Source:** NeurIPS 2025 Conference Guidelines
+
+**Page Limits:**
+- Main paper: 9 pages (excluding references)
+- References: Unlimited (don't count toward page limit)
+- Appendices: Allowed but reviewers not required to read
+
+**Required Sections:**
+- Abstract: 150-250 words
+- Introduction: Must clearly state contribution
+- Methods: Sufficient for reproduction
+- Experiments: Comprehensive evaluation
+- Discussion: Interpret results and limitations
+- References: Complete citations
+- **Checklist**: Mandatory submission checklist (16 items)
+- **Lay Summary**: Required for accepted papers (1 page, non-technical)
+
+**Formatting:**
+- Double-blind review (anonymize submissions)
+- LaTeX template required
+- 9pt font, two-column format
+- Margins: 1 inch on all sides
+
+**Submission Checklist Items:**
+1. Do the main claims of the paper match the checklist?
+2. Have you checked the checklist for missing items?
+3. Is the paper anonymized?
+4. Are all figures/tables clear and readable?
+5. Have you included code and data availability statements?
+6. Are all equations properly formatted?
+7. Is the abstract within word limit?
+8. Are all citations complete and correct?
+9. Have you addressed potential ethical concerns?
+10. Are experimental settings clearly described?
+11. Is statistical significance properly reported?
+12. Have you included limitations?
+13. Is the broader impact discussed (if required)?
+14. Are all figures referenced in text?
+15. Is the supplementary material well-organized?
+16. Have you proofread for typos and grammar?
+
+---
+
+## ICML Requirements
+
+### Format Requirements
+**Source:** ICML 2026 Conference Guidelines
+
+**Page Limits:**
+- Main paper: 8 pages
+- Camera-ready: +1 page (9 total)
+- References: Unlimited (don't count toward page limit)
+
+**Required Sections:**
+- Abstract: Clear summary of contribution
+- Introduction: Problem and contribution
+- Methods: Complete description
+- Experiments: Thorough evaluation
+- **Broader Impact Statement**: Required (after conclusion)
+- References: Complete citations
+
+**Formatting:**
+- Double-blind review (anonymize submissions)
+- LaTeX template required
+- Two-column format
+- Margins as per template
+
+**Broader Impact Statement:**
+- Discuss positive and negative societal impacts
+- Consider biases, fairness, environmental impact
+- 1 page maximum
+- Required for all submissions
+
+---
+
+## ICLR Requirements
+
+### Format Requirements
+**Source:** ICLR 2026 Conference Guidelines
+
+**Page Limits:**
+- Main paper: 9 pages
+- Camera-ready: +1 page (10 total)
+- References: Unlimited (don't count toward page limit)
+
+**Required Sections:**
+- Abstract: Summary of contribution
+- Introduction: Clear problem statement
+- Methods: Reproducible description
+- Experiments: Comprehensive evaluation
+- **Limitations Section**: MANDATORY
+- **LLM Disclosure**: Required if using LLMs
+- References: Complete citations
+
+**Formatting:**
+- Double-blind review (anonymize submissions)
+- LaTeX template required
+- Two-column format
+
+**LLM Disclosure Requirements:**
+- Describe LLM use in the paper
+- Include model details (architecture, training data, compute)
+- Acknowledge LLM limitations
+- Note any automated text generation
+
+---
+
+## ACL Requirements
+
+### Format Requirements
+**Source:** ACL 2025 Conference Guidelines
+
+**Page Limits:**
+- Main paper: 8 pages (long papers)
+- Short papers: 4 pages
+- References: Unlimited (don't count toward page limit)
+
+**Required Sections:**
+- Abstract: 150-200 words
+- Introduction: Background and contribution
+- Methods: Technical description
+- Experiments: Evaluation
+- **Limitations Section**: MANDATORY
+- **Ethics Statement**: Required if applicable
+- References: Complete citations
+
+**Formatting:**
+- Double-blind review (anonymize submissions)
+- LaTeX template required (ACL style files)
+- Two-column format
+
+**Ethics Considerations:**
+- Human subjects research: IRB approval required
+- Data privacy: Anonymization and consent
+- Environmental impact: Compute resource usage
+
+---
+
+## AAAI Requirements
+
+### Format Requirements
+**Source:** AAAI 2026 Conference Guidelines
+
+**Page Limits:**
+- Main paper: 7 pages
+- Camera-ready: +1 page (8 total)
+- References: Unlimited (don't count toward page limit)
+
+**Required Sections:**
+- Abstract: 150-250 words
+- Introduction: Problem and contribution
+- Methods: Technical description
+- Experiments: Evaluation
+- References: Complete citations
+
+**Formatting:**
+- Double-blind review (anonymize submissions)
+- **Strict style file adherence**: Must use official template
+- Two-column format
+- No modifications to style files
+
+**Strict Requirements:**
+- Follow AAAI template exactly
+- No custom formatting beyond template
+- Font sizes and margins as specified
+- Page limits strictly enforced
+
+---
+
+## COLM Requirements
+
+### Format Requirements
+**Source:** COLM 2025 Conference Guidelines
+
+**Page Limits:**
+- Main paper: 9 pages
+- Camera-ready: +1 page (10 total)
+- References: Unlimited (don't count toward page limit)
+
+**Required Sections:**
+- Abstract: Summary of contribution
+- Introduction: Problem and contribution
+- Methods: Technical description
+- Experiments: Evaluation
+- **Focus**: Language models specifically
+- References: Complete citations
+
+**Formatting:**
+- Double-blind review (anonymize submissions)
+- LaTeX template required
+- Two-column format
+
+**Language Model Focus:**
+- Papers should address language model challenges
+- Method contributions applicable to LM community
+- Experimental setup relevant to language tasks
+
+---
+
+## Common Submission Requirements
+
+### Double-Blind Review
+**Applies to:** NeurIPS, ICML, ICLR, ACL, AAAI, COLM
+
+**Requirements:**
+- Remove author names and affiliations
+- Anonymize citations to own work (use [Anonymous, 2024])
+- Remove acknowledgments that reveal identity
+- Avoid distinctive phrases that identify authors
+- Supplementary materials must also be anonymized
+
+**Common Mistakes:**
+- Forgetting to anonymize GitHub links
+- Including author names in file paths
+- Thanking specific colleagues in acknowledgments
+- Citing own work with author names
+
+### Code and Data Availability
+
+**Increasingly Required:**
+
+**NeurIPS:** Encourages code/data availability statements
+**ICML:** Encourages reproducibility
+**ICLR:** Recommends code sharing
+
+**Best Practices:**
+- Include code repository link (anonymized if under review)
+- Provide data access instructions
+- Describe hyperparameters and settings
+- Note any proprietary constraints
+
+### Supplementary Materials
+
+**General Guidelines:**
+- Appendices allowed but not required reading
+- Use for additional experiments, proofs, tables
+- Keep main paper self-contained
+- Reference supplementary in main text
+
+**Formatting:**
+- Same style as main paper
+- Clear section numbering (S1, S2, etc.)
+- Include in submission PDF or as separate file
+
+---
+
+## Citation Styles
+
+### Common Styles in ML
+
+**IEEE Style (Numbered):**
+```
+[1] J. Doe, "Paper title," Conference Name, Year.
+[2] A. Smith, "Another paper," Journal Name, vol. 10, pp. 1-15, 2020.
+```
+
+**ACM Style (Numbered):**
+```
+[J. Doe and A. Smith, "Paper title," Conference Name, Year.
+[A. Smith and B. Jones, "Another paper," Journal Name, 2020.
+```
+
+**Author-Year (APA-like):**
+```
+Doe (2020) J. Doe. Paper title. Conference Name.
+Smith (2019) A. Smith. Another paper. Journal Name.
+```
+
+### Reference Management
+
+**Best Practices:**
+- Use consistent style throughout
+- Include DOIs when available
+- Provide arXiv links for preprints
+- Check for broken links
+- Verify all citations before submission
+
+---
+
+## Checklists and Templates
+
+### Pre-Submission Checklist
+
+**Content:**
+- [ ] Novel contribution clearly stated
+- [ ] Related work comprehensive
+- [ ] Methods reproducible
+- [ ] Results support all claims
+- [ ] Limitations acknowledged
+- [ ] Broader impact addressed (if required)
+- [ ] Ethics compliance verified
+
+**Formatting:**
+- [ ] Page limits respected
+- [ ] Style file followed exactly
+- [ ] References complete and consistent
+- [ ] Figures/tables clear and readable
+- [ ] Equations numbered and referenced
+- [ ] Supplementary material organized
+
+**Anonymity:**
+- [ ] Author names removed
+- [ ] Acknowledgements anonymized
+- [ ] Self-citations anonymized
+- [ ] GitHub links anonymized
+- [ ] Identifying information removed
+
+---
+
+## Notes
+
+- **Always verify**: Requirements change between years - always check current conference guidelines
+- **Plan ahead**: Some venues have strict formatting - start early
+- **Read examples**: Look at well-received papers from previous years
+- **Ask for help**: If unsure about a requirement, consult program chairs or experienced colleagues
+
+**Updates:** This file is regularly updated as new conference requirements become available.
diff --git a/.agents/skills/ml-paper-writing/references/knowledge/theory-driven-papers-kaiming-he.md b/.agents/skills/ml-paper-writing/references/knowledge/theory-driven-papers-kaiming-he.md
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+# Theory-Driven Papers: From First Principles
+
+**Source**: Kaiming He et al., "Mean Flows for One-step Generative Modeling" (2025)
+
+**Paper Type**: Theory-driven / First-principles paper
+
+**Core Pattern**: Start from first principles → Derive theory → Build method → Demonstrate superiority
+
+---
+
+## 1. Abstract Structure: The "Principle Introduction" Framework
+
+### Pattern: From Theory to Results
+
+**Template**:
+```markdown
+Abstract:
+1. [Background] Established framework provides [foundation]
+2. [Problem] Recent research focuses on [challenge], but existing methods have [limitation]
+3. [Critique] Despite encouraging results, [specific problem with prior approaches]
+4. [Core Concept] We introduce [new concept], in contrast to [old concept]
+5. [Theory] Solely from definition, we derive [theoretical foundation]
+6. [Advantage] This provides [principled basis] vs [heuristic approaches]
+7. [Results] Achieves [strong result] - [relative improvement] over SOTA
+8. [Significance] Self-contained, [independence from external components]
+```
+
+### MeanFlows Abstract Example (annotated):
+
+```latex
+Flow Matching provides an intuitive and conceptually simple framework for
+constructing flow paths that transport one distribution to another.
+
+Recent research has paid significant attention to few-step—and in
+particular, one-step, feedforward—generative models.
+
+Despite encouraging results, the consistency constraint is imposed as a
+property of the network's behavior, while the properties of the underlying
+ground-truth field that should guide learning remain unknown.
+
+The core idea is to introduce a new ground-truth field representing the
+average velocity, in contrast to the instantaneous velocity typically
+modeled in Flow Matching.
+
+Solely originated from this definition, we derive a well-defined, intrinsic
+relation between the average and instantaneous velocities, which naturally
+serves as a principled basis for guiding network training.
+
+Our method achieves an FID of 3.43 using 1-NFE generation, significantly
+outperforming previous state-of-the-art methods by a relative margin of 50%
+to 70%.
+
+It is trained entirely from scratch, without any pre-training, distillation,
+or curriculum learning.
+```
+
+### Key Techniques:
+
+1. **"Provides an intuitive and conceptually simple framework"** - Light touch introduction
+2. **"Recent research has paid significant attention to..."** - Establish context
+3. **"Despite encouraging results..."** - The critique pattern (acknowledge then problem)
+4. **"The core idea is to introduce..."** - Clear concept statement
+5. **"in contrast to"** - Conceptual differentiation
+6. **"Solely originated from this definition"** - First-principles emphasis
+7. **"well-defined, intrinsic relation"** - Theory keywords
+8. **"naturally serves as a principled basis"** - Naturalness emphasis
+9. **Relative improvement (50-70%)** - More impactful than absolute numbers
+10. **Negative list** - What you DON'T need (pre-training, distillation, curriculum)
+
+---
+
+## 2. Introduction: The "Critique-First" Framework
+
+### Pattern: Build Up → Identify Flaw → Propose Alternative
+
+**Structure**:
+```markdown
+1. [Background] Established field with [characteristic]
+2. [Problem Shift] Research focus has moved to [new direction]
+3. [Specific Problem] Existing approaches address this by [method]
+4. [The Critique] Despite [acknowledgment], [fundamental problem]
+   - "imposed as a property of [X]"
+   - "[Y] remains unknown"
+5. [Consequences] Consequently, [practical problems]
+6. [Your Concept] We propose [alternative] with [differentiation]
+7. [Theory] From [first principles], we derive [result]
+8. [Advantage] This is [principled/natural/intrinsic] vs [heuristic/artificial]
+9. [Results] [Quantitative result] with [qualitative advantage]
+```
+
+### MeanFlows Introduction Flow:
+
+#### Background (Light Touch)
+```latex
+Flow Matching provides an intuitive and conceptually simple framework
+for constructing flow paths that transport one distribution to another.
+```
+
+**Technique**:
+- "intuitive and conceptually simple" - Modest, not revolutionary
+- Focus on what it IS, not how important it is
+
+#### Problem Shift
+```latex
+Closely related to diffusion models, Flow Matching focuses on the velocity
+fields that guide model training.
+
+Both Flow Matching and diffusion models perform iterative sampling during
+generation. Recent research has paid significant attention to few-step
+—and in particular, one-step, feedforward—generative models.
+```
+
+**Technique**:
+- "Closely related to" - Establish connection
+- "Recent research has paid significant attention to" - Research trend
+- "few-step—and in particular, one-step" - Progressive emphasis
+
+#### The Critique (Key Pattern)
+```latex
+Consistency Models [46, 43, 15, 31] achieve few-step generation by enforcing
+a consistency constraint on the velocity field.
+
+Despite encouraging results, the consistency constraint is imposed as a
+property of the network's behavior, while the properties of the underlying
+ground-truth field that should guide learning remain unknown.
+
+Consequently, training can be unstable and requires a carefully designed
+'discretization curriculum' to progressively constrain the time domain.
+```
+
+**Technique**:
+- **"Despite encouraging results"** - Always acknowledge first
+- **"imposed as a property of the network's behavior"** - It's artificial
+- **"underlying ground-truth field...remain unknown"** - Missing theory
+- **"Consequently"** - Show practical consequences
+- **Specific problems**: "training can be unstable", "requires...curriculum"
+
+#### Your Concept
+```latex
+The core idea is to introduce a new ground-truth field representing the
+average velocity, in contrast to the instantaneous velocity typically
+modeled in Flow Matching.
+```
+
+**Technique**:
+- **"The core idea is to introduce"** - Direct statement
+- **"in contrast to"** - Conceptual differentiation
+- **Old vs New**: "average velocity" vs "instantaneous velocity"
+
+#### Theory First
+```latex
+Average velocity is defined as the ratio of displacement to a time interval,
+with displacement given by the time integral of the instantaneous velocity.
+
+Solely originated from this definition, we derive a well-defined, intrinsic
+relation between the average and instantaneous velocities, which naturally
+serves as a principled basis for guiding network training.
+```
+
+**Technique**:
+- **"Solely originated from this definition"** - Pure derivation
+- **"well-defined, intrinsic relation"** - Theory keywords
+- **"naturally serves as"** - Not forced
+- **"principled basis"** - Foundation
+
+---
+
+## 3. Methods Section: The "Named Identity" Pattern
+
+### Pattern: Define → Derive → Name
+
+**Structure**:
+```markdown
+1. [Concept Name] Define with formal notation
+2. [Motivation] Explain why we need this
+3. [Derivation] Step-by-step with justifications
+4. [Naming] Give it a memorable name
+5. [Comparison] Contrast with prior approaches
+```
+
+### MeanFlows Example:
+
+#### Step 1: Concept Naming
+```latex
+Average Velocity. We define average velocity as the displacement between
+two time steps t and r (obtained by integration) divided by the time interval.
+
+Formally, the average velocity u is:
+
+u(zt, r, t) ≜ 1/(t−r) ∫_r^t v(zτ, τ)dτ.    (3)
+```
+
+**Techniques**:
+- **Bold heading**: "Average Velocity." - Makes it memorable
+- **Text description first**: Explain before formula
+- **"Formally,"**: Signals math coming
+- **≜ symbol**: "defined as" (clearer than =)
+
+#### Step 2: Derivation with Motivation
+```latex
+To have a formulation amenable to training, we rewrite Eq. (3) as:
+
+(t−r)u(zt, r, t) = ∫_r^t v(zτ, τ)dτ.    (4)
+
+Now we differentiate both sides with respect to t, treating r as independent
+of t. This leads to:
+
+d/dt(t−r)u = d/dt∫_r^t v(zτ, τ)dτ
+
+⇒ u + (t−r)d/dt u = v(zt, t),    (5)
+
+where the manipulation of the left hand side employs the product rule and
+the right hand side uses the fundamental theorem of calculus.
+```
+
+**Techniques**:
+- **"To have a formulation amenable to training"** - Explain why
+- **"Now we differentiate..."** - Guide reader
+- **Step-by-step**: Don't skip
+- **"where..."**: Explain each manipulation
+- **"⇒" symbol**: Clear direction
+
+#### Step 3: Naming the Identity
+```latex
+Rearranging terms, we obtain the identity:
+
+u(zt, r, t) = v(zt, t) − (t−r)d/dt u(zt, r, t)    (6)
+
+We refer to this equation as the "MeanFlow Identity", which describes the
+relation between v and u.
+```
+
+**Techniques**:
+- **"Rearranging terms, we obtain..."** - What you did
+- **"We refer to this equation as the 'X Identity'"** - Brand it
+- **Explain**: "which describes..." - What it does
+
+---
+
+## 4. Comparison: Principled vs Heuristic
+
+### Pattern: Emphasize Theoretical Independence
+
+**Structure**:
+```markdown
+1. [Your Core] At the core of our method is [fundamental principle]
+2. [Independence] This [does not depend on / is independent of] [implementation]
+3. [Contrast] In contrast, prior works typically rely on [heuristic/artificial constraint]
+4. [Qualitative] [Natural/principled/intrinsic] vs [imposed/empirical/heuristic]
+```
+
+### MeanFlows Example:
+
+```latex
+At the core of our method is the functional relationship between two
+underlying fields v and u, which naturally leads to the MeanFlow Identity
+that u must satisfy (Eq. (6)).
+
+This identity does not depend on the introduction of neural networks.
+
+In contrast, prior works typically rely on extra consistency constraints,
+imposed on the behavior of the neural network.
+```
+
+**Techniques**:
+- **"At the core of our method is..."** - What matters
+- **"naturally leads to"** - Not forced
+- **"does not depend on"** - Independence
+- **"In contrast"** - Clear pivot
+- **"imposed on"** - Theirs is artificial
+
+### Specific Method Comparison
+
+```latex
+Consistency Models [46, 43, 15, 31] are focused on paths anchored at the
+data side: in our notations, this corresponds to fixing r≡0 for any t.
+
+As a result, Consistency Models are conditioned on a single time variable,
+unlike ours.
+```
+
+**Techniques**:
+- **"focused on X"** - Their scope
+- **"in our notations, this corresponds to..."** - Precise mapping
+- **"As a result"** - Consequence
+- **"unlike ours"** - One-word differentiation
+
+---
+
+## 5. Results: Significant Improvements with Context
+
+### Pattern: Relative Improvement + Independence
+
+**Structure**:
+```markdown
+1. [Absolute] We achieve [metric] on [task]
+2. [Relative] This represents [X-Y%] relative improvement over [comparison]
+3. [Context] Our method is [self-contained / independent]
+4. [Negative List] without [list of things you don't need]
+```
+
+### MeanFlows Example:
+
+```latex
+Our method achieves an FID of 3.43 using 1-NFE generation.
+
+This result significantly outperforms previous state-of-the-art methods in
+its class by a relative margin of 50% to 70% (Fig. 1).
+
+In addition, our method stands as a self-contained generative model: it is
+trained entirely from scratch, without any pre-training, distillation, or
+curriculum learning.
+```
+
+**Techniques**:
+- **Absolute first**: "FID of 3.43"
+- **"significantly outperforms"** - Strong but not "dramatically"
+- **"by a relative margin of 50% to 70%"** - Range, not single number
+- **Reference to figure**: "(Fig. 1)"
+- **"In addition"** - Second dimension of value
+- **"self-contained"** - Independence keyword
+- **"trained entirely from scratch"** - Complete independence
+- **Negative list**: "without any pre-training, distillation, or curriculum learning"
+
+---
+
+## 6. Table Design: System-Level Comparison
+
+### Pattern: Multiple Paradigms, Clear Highlighting
+
+**Structure**:
+```markdown
+Table X:
+┌────────────────────────────────────┐
+│ Left side: Your direct competitors  │
+│ (1-NFE and 2-NFE methods)          │
+├────────────────────────────────────┤
+│ Right side: Other paradigms         │
+│ (GANs, autoregressive, etc.)       │
+├────────────────────────────────────┤
+│ **Your method** (bold, positioned)  │
+└────────────────────────────────────┘
+```
+
+### MeanFlows Table 2 Organization:
+
+```latex
+Table 2: Comparison on ImageNet 256×256.
+
+Left: 1-NFE and 2-NFE diffusion/flow models
+Right: Other generative models
+
+Highlighted: MeanFlow (our method)
+```
+
+**Key Techniques**:
+1. **Split paradigm**: Direct competitors on left, others on right
+2. **Fair metrics**: params, NFE, FID (same for all)
+3. **Bold your method**: Visual emphasis
+4. **Position strategically**: Where you look best
+5. **Comprehensive**: Include all major paradigms
+
+---
+
+## 7. Figure Design: Visual Storytelling
+
+### Pattern: Multi-Panel Narrative
+
+**MeanFlows Figure 1**: "One-step generation on ImageNet 256×256 from scratch"
+
+**Structure**:
+- **Main panel**: Generated images (visual evidence)
+- **Caption**: Detailed comparison table
+- **Annotations**: FID scores of competing methods
+- **Highlight**: "Our MeanFlow (MF) model achieves significantly better..."
+
+**Techniques**:
+1. **Title tells the story**: "from scratch" - key differentiator
+2. **Images + numbers**: Both visual and quantitative
+3. **Competitor scores in caption**: Reader doesn't need to flip pages
+4. **"significantly better"**: In the figure caption itself
+
+---
+
+## 8. Ablation Study: Destructive Testing
+
+### Pattern: Prove Necessity by Breaking Things
+
+**Structure**:
+```markdown
+Table X:
+┌──────────────────────────────────┐
+│ (a) Vary one design dimension    │
+│ - Show effect of parameter       │
+│ - Mark default in gray           │
+├──────────────────────────────────┤
+│ (b) Destructive comparison       │
+│ - Intentionally use WRONG values │
+│ - Show only correct works        │
+└──────────────────────────────────┘
+```
+
+### MeanFlows Table 1 Example:
+
+#### Part (a): Design Sweep
+```latex
+(a) Ratio of sampling r≠t
+% of r≠t      FID, 1-NFE
+0% (= FM)     328.91
+25%           61.06
+50%           63.14
+100%          67.32
+```
+
+**Techniques**:
+- **Descriptive caption**: "Ratio of sampling r≠t"
+- **Show failure mode**: "0% (= FM) 328.91" - pure FM fails
+- **Range**: 0% to 100% of parameter
+- **Default marked**: In original (not shown here)
+
+#### Part (b): Destructive Testing
+```latex
+(b) JVP computation
+jvp tangent          FID, 1-NFE
+(v, 0, 1)            61.06
+(v, 0, 0) [wrong]    268.06
+(v, 1, 0) [wrong]    329.22
+(v, 1, 1) [wrong]    137.96
+```
+
+**Techniques**:
+- **"Destructive comparison"** in caption
+- **"intentionally performed"** in text
+- **Only first row works**: Others are wrong by design
+- **Proves necessity**: "meaningful results are achieved only when..."
+
+---
+
+## 9. Writing Style: Theory Keywords
+
+### Emphasis Words for Theory-Driven Papers
+
+**Naturalness Keywords** (use these to describe your theory):
+- "naturally" - "This naturally leads to..."
+- "intrinsic" - "intrinsic relation"
+- "well-defined" - "well-defined problem"
+- "principled" - "principled basis"
+- "first principles" - "from first principles"
+- "solely originated from" - "solely from definition"
+
+**Independence Keywords**:
+- "does not depend on" - Theory independence
+- "independent of" - Implementation independence
+- "self-contained" - System independence
+- "from scratch" - No external dependencies
+- "without any X" - Negative list
+
+**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 These (Too Promotional):
+- ❌ "revolutionary" - Too strong
+- ❌ "breakthrough" - Let others say it
+- ❌ "completely eliminates" - Too absolute
+- ✅ "significantly outperforms" - Strong but measured
+- ✅ "substantial improvement" - Professional
+
+---
+
+## 10. Common Mistakes in Theory Papers
+
+### ❌ Don't:
+- Derive without explaining motivation - Why are we doing this?
+- Skip steps in derivation - Readers aren't you
+- Use heuristics without admitting it - Be honest
+- Overclaim - "proves optimal" vs "improves over"
+- Forget to acknowledge dependencies - If you use X, say it
+
+### ✅ Do:
+- Start from first principles explicitly
+- Give each equation/dentity a memorable name
+- Show "destructive" ablations to prove necessity
+- Report relative improvements (more impactful)
+- Use "principled" keywords consistently
+- Admit what you DON'T need (negative list)
+
+---
+
+## 11. Revision Checklist for Theory-Driven Papers
+
+**Before Submission, Verify:**
+
+- [ ] Abstract starts from established framework (not "X is important")
+- [ ] Introduction has "Despite encouraging results..." critique
+- [ ] Core concept has a memorable name
+- [ ] Derivation is step-by-step with justifications
+- [ ] Key equation is named ("X Identity")
+- [ ] Theory is contrasted as "principled" vs "heuristic"
+- [ ] Results include relative improvement (X-Y%)
+- [ ] Self-containment is emphasized (what you don't need)
+- [ ] Ablations include destructive tests
+- [ ] Tables organize by paradigm, highlight your position
+- [ ] Figures tell visual story with captions
+- [ ] Theory keywords used consistently (principled, intrinsic, natural)
+
+---
+
+## 12. Example: Applying This Pattern
+
+### Original Idea (Not Theory-Driven):
+"We propose a new training method that improves FID by 20%."
+
+### Theory-Driven Version:
+"Flow Matching provides an intuitive framework for generative modeling,
+but recent one-step methods impose consistency constraints heuristically.
+Despite encouraging results, the underlying ground-truth field properties
+remain unknown. We introduce average velocity (in contrast to instantaneous
+velocity), deriving the MeanFlow Identity solely from first principles.
+This provides a principled basis for training, achieving 3.43 FID with
+50-70% relative improvement. Our method is self-contained, trained from
+scratch without pre-training or distillation."
+
+**The Theory-Driven Frame**:
+- Foundation: Flow Matching (established)
+- Problem: Heuristic constraints (theory gap)
+- Concept: Average velocity (new)
+- Theory: MeanFlow Identity (derived)
+- Result: Strong + independent (no external deps)
+
+---
+
+## Paper Metadata
+
+**Title**: Mean Flows for One-step Generative Modeling
+
+**Authors**: Kaiming He et al.
+
+**Year**: 2025
+
+**Key Concepts**:
+- Average velocity vs instantaneous velocity
+- MeanFlow Identity
+- Principled vs heuristic training
+- Self-contained generative models
+
+---
+
+## Extracted by
+
+**Date**: 2026-01-26
+
+**Source**: Analysis of Mean Flows paper (16 pages)
+
+**Extraction Focus**: Theory-driven paper writing patterns, first-principles
+derivations, principled vs heuristic positioning
+
+**For Integration**: ml-paper-writing skill knowledge base
diff --git a/.agents/skills/ml-paper-writing/references/knowledge/writing-techniques.md b/.agents/skills/ml-paper-writing/references/knowledge/writing-techniques.md
new file mode 100644
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+++ b/.agents/skills/ml-paper-writing/references/knowledge/writing-techniques.md
@@ -0,0 +1,637 @@
+# 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
+```