---
name: ML Paper Writing
slug: ml-paper-writing
category: Writing
description: ML Paper Writing drafts publication-ready papers for AI conferences like NeurIPS, ICML, ICLR, ACL, AAAI, and COLM. Use it when turning research code and results into a manuscript, finding related work, verifying citations programmatically, or preparing camera-ready submissions.
github: "https://github.com/Galaxy-Dawn/claude-scholar/tree/main/skills/ml-paper-writing"
language: Python
stars: 5133
forks: 415
install: "npx degit https://github.com/Galaxy-Dawn/claude-scholar/tree/main/skills/ml-paper-writing ~/.claude/skills/ml-paper-writing"
installs_to: ~/.claude/skills/ml-paper-writing
source_path: skills/ml-paper-writing/SKILL.md
collection_size: 25
category_size: 1012
collection_url: "https://dirskills.com/collections/Galaxy-Dawn/claude-scholar"
added: 2026-08-16T07:00:43.157Z
last_synced: 2026-08-16T07:00:43.157Z
canonical_url: "https://dirskills.com/skills/ml-paper-writing"
---

# ML Paper Writing

ML Paper Writing drafts publication-ready papers for AI conferences like NeurIPS, ICML, ICLR, ACL, AAAI, and COLM. Use it when turning research code and results into a manuscript, finding related work, verifying citations programmatically, or preparing camera-ready submissions.

**Install:**

```bash
npx degit https://github.com/Galaxy-Dawn/claude-scholar/tree/main/skills/ml-paper-writing ~/.claude/skills/ml-paper-writing
```

## README

# ML Paper Writing for Top AI Conferences

Expert-level guidance for writing publication-ready papers targeting **NeurIPS, ICML, ICLR, ACL, AAAI, and COLM**. This skill combines writing philosophy from top researchers (Nanda, Farquhar, Karpathy, Lipton, Steinhardt) with practical tools: LaTeX templates, citation verification APIs, and conference checklists.

## Default operating order

Use this skill in the following order unless the task is unusually narrow:
1. lock the operating mode from `references/OPERATING-MODES.md`,
2. understand the repo or draft context,
3. use `references/citation-workflow.md` as the **canonical citation authority**,
4. load venue- or template-specific references only after the main writing path is clear.

Google Scholar may still help with manual discovery, but it is **not** the canonical verification authority in this skill. Default verification should use programmatic sources such as Semantic Scholar, CrossRef, and arXiv.

## Claim ledger gate

Before a project plan, experiment note, or literature summary becomes manuscript prose:
- identify the Claim Candidate or Evidence Record that supports the sentence,
- preserve allowed wording and forbidden stronger wording,
- keep project plans as hypotheses unless experiment artifacts or verified papers support them,
- do not turn related-work motivation into evidence for the paper's own result,
- mark unsupported claims as `[CLAIM NEEDS EVIDENCE]` instead of polishing them.

If the repo context is clear enough for a first draft, still apply this gate before stating contributions, results, related-work contrasts, or rebuttal-facing claims.

## Core Philosophy: Collaborative Writing

**Paper writing is collaborative, but Claude should be proactive in delivering drafts.**

The typical workflow starts with a research repository containing code, results, and experimental artifacts. Claude's role is to:

1. **Understand the project** by exploring the repo, results, and existing documentation
2. **Deliver a complete first draft** when confident about the contribution
3. **Search literature** using web search and APIs to find relevant citations
4. **Refine through feedback cycles** when the scientist provides input
5. **Ask for clarification** only when genuinely uncertain about key decisions

**Key Principle**: Be proactive. If the repo and results are clear, deliver a full draft. Don't block waiting for feedback on every section—scientists are busy. Produce something concrete they can react to, then iterate based on their response.

---

## ⚠️ CRITICAL: Never Hallucinate Citations

**This is the most important rule in academic writing with AI assistance.**

### The Problem
AI-generated citations have a **~40% error rate**. Hallucinated references—papers that don't exist, wrong authors, incorrect years, fabricated DOIs—are a serious form of academic misconduct that can result in desk rejection or retraction.

### The Rule
**NEVER generate BibTeX entries from memory. ALWAYS fetch programmatically.**

| Action | ✅ Correct | ❌ Wrong |
|--------|-----------|----------|
| Adding a citation | Search API → verify → fetch BibTeX | Write BibTeX from memory |
| Uncertain about a paper | Mark as `[CITATION NEEDED]` | Guess the reference |
| Can't find exact paper | Note: "placeholder - verify" | Invent similar-sounding paper |

### When You Can't Verify a Citation

If you cannot programmatically verify a citation, you MUST:

```latex
% EXPLICIT PLACEHOLDER - requires human verification
\cite{PLACEHOLDER_author2024_verify_this}  % TODO: Verify this citation exists
```

**Always tell the scientist**: "I've marked [X] citations as placeholders that need verification. I could not confirm these papers exist."

### Recommended: Install Exa MCP for Paper Search

For the best paper search experience, install **Exa MCP** which provides real-time academic search:

**Claude Code:**
```bash
claude mcp add exa -- npx -y mcp-remote "https://mcp.exa.ai/mcp"
```

**Cursor / VS Code** (add to MCP settings):
```json
{
  "mcpServers": {
    "exa": {
      "type": "http",
      "url": "https://mcp.exa.ai/mcp"
    }
  }
}
```

Exa MCP enables searches like:
- "Find papers on RLHF for language models published after 2023"
- "Search for transformer architecture papers by Vaswani"
- "Get recent work on sparse autoencoders for interpretability"

Then verify results with Semantic Scholar API and fetch BibTeX via DOI.

---

## Workflow 0: Starting from a Research Repository

When beginning paper writing, start by understanding the project:

```
Project Understanding:
- [ ] Step 1: Explore the repository structure
- [ ] Step 2: Read README, existing docs, and key results
- [ ] Step 3: Identify the main contribution with the scientist
- [ ] Step 4: Find papers already cited in the codebase
- [ ] Step 5: Search for additional relevant literature
- [ ] Step 6: Outline the paper structure together
- [ ] Step 7: Draft sections iteratively with feedback
```

**Step 1: Explore the Repository**

```bash
# Understand project structure
ls -la
find . -name "*.py" | head -20
find . -name "*.md" -o -name "*.txt" | xargs grep -l -i "result\|conclusion\|finding"
```

Look for:
- `README.md` - Project overview and claims
- `results/`, `outputs/`, `experiments/` - Key findings
- `configs/` - Experimental settings
- Existing `.bib` files or citation references
- Any draft documents or notes

**Step 2: Identify Existing Citations**

Check for papers already referenced in the codebase:

```bash
# Find existing citations
grep -r "arxiv\|doi\|cite" --include="*.md" --include="*.bib" --include="*.py"
find . -name "*.bib"
```

These are high-signal starting points for Related Work—the scientist has already deemed them relevant.

**Step 3: Clarify the Contribution**

Before writing, explicitly confirm with the scientist:

> "Based on my understanding of the repo, the main contribution appears to be [X].
> The key results show [Y]. Is this the framing you want for the paper,
> or should we emphasize different aspects?"

**Never assume the narrative—always verify with the human.**

**Step 4: Search for Additional Literature**

Use web search to find relevant papers:

```
Search queries to try:
- "[main technique] + [application domain]"
- "[baseline method] comparison"
- "[problem name] state-of-the-art"
- Author names from existing citations
```

Then verify and retrieve BibTeX using the citation workflow below.

**Step 5: Deliver a First Draft**

**Be proactive—deliver a complete draft rather than asking permission for each section.**

If the repo provides clear results and the contribution is apparent:
1. Check the claim ledger gate for contribution and result claims
2. Write the full first draft end-to-end only for supported claims
3. Mark unsupported or speculative claims explicitly
4. Present the complete draft for feedback
5. Iterate based on scientist's response

If genuinely uncertain about framing or major claims:
1. Draft what you can confidently
2. Flag specific uncertainties: "I framed X as the main contribution—let me know if you'd prefer to emphasize Y instead"
3. Continue with the draft rather than blocking

**Questions to include with the draft** (not before):
- "I emphasized X as the main contribution—adjust if needed"
- "I highlighted results A, B, C—let me know if others are more important"
- "Related work section includes [papers]—add any I missed"

---

## When to Use This Skill

Use this skill when:
- **Starting from a research repo** to write a paper
- **Drafting or revising** specific sections
- **Conducting literature reviews** and finding related work
- **Discovering recent papers** in your research area
- **Finding and verifying citations** for related work
- **Formatting** for conference submission
- **Resubmitting** to a different venue (format conversion)
- **Iterating** on drafts with scientist feedback

**Always remember**: First drafts are starting points for discussion, not final outputs.

---

## Workflow: Literature Research & Paper Discovery

When conducting literature reviews, finding related work, or discovering recent papers, use this workflow to systematically search, evaluate, and select ML papers.

### Workflow 5: Finding and Evaluating Papers

```
Literature Research Process:
- [ ] Step 1: Define search scope and keywords
- [ ] Step 2: Search arXiv and academic databases
- [ ] Step 3: Screen papers by title/abstract
- [ ] Step 4: Evaluate paper quality (5 dimensions)
- [ ] Step 5: Select top papers and extract citations
- [ ] Step 6: Verify citations programmatically
```

**Step 1: Define Search Scope**

Identify specific research areas, methods, or applications:
- **Technique-focused**: `transformer architecture`, `graph neural networks`, `self-supervised learning`
- **Application-focused**: `medical image analysis`, `reinforcement learning for robotics`, `language model alignment`
- **Problem-focused**: `out-of-distribution generalization`, `continual learning`, `fairness in ML`

**Step 2: Search arXiv**

Use arXiv search with targeted keywords:
```
URL Pattern:
https://arxiv.org/search/?searchtype=all&query=KEYWORDS&abstracts=show&order=-announced_date_first

Example Searches:
- https://arxiv.org/search/?searchtype=all&query=graph+neural+networks&abstracts=show&order=-announced_date_first
- https://arxiv.org/search/?cat:cs.LG+AND+all:transformer&abstracts=show&order=-announced_date_first
```

**Tips:**
- Combine keywords with `+` for AND
- Filter by categories: `cs.LG`, `cs.AI`, `cs.CV`, `cs.CL`
- Sort by `announced_date_first` for recent papers
- Use Chrome MCP tools when available for automation

**Step 3: Screen Papers**

Quick screening by title and abstract:
- Relevance to research topic
- Novelty of contribution
- Venue/reputation of authors
- Code availability (check for GitHub links)

**Step 4: Evaluate Quality**

Use the 5-dimension quality criteria:

| Dimension | Weight | Evaluation Focus |
|-----------|--------|------------------|
| **Innovation** | 30% | Novelty and originality |
| **Method Completeness** | 25% | Clarity and reproducibility |
| **Experimental Thoroughness** | 25% | Validation depth |
| **Writing Quality** | 10% | Presentation clarity |
| **Relevance & Impact** | 10% | Domain importance |

**Scoring**: Rate each dimension 1-5, calculate weighted total

**Step 5: Select and Extract**

- Rank papers by total score
- Select top papers for detailed review
- Extract metadata: title, authors, arXiv ID, abstract
- Note code repository links

**Step 6: Verify Citations**

For selected papers, verify citations using Semantic Scholar API:
- Fetch BibTeX programmatically via DOI
- Mark unverified citations as `[CITATION NEEDED]`
- Store in bibliography with verification status

### When to Use Literature Research

Use this workflow when:
- **Starting a new project**: Find related work and baselines
- **Writing Related Work section**: Discover recent papers in your area
- **Staying updated**: Track recent publications in your field
- **Finding baselines**: Identify state-of-the-art methods for comparison
- **Literature review**: Comprehensive survey of research area

### Quality Thresholds

- **Excellent**: 4.0+ (include definitely)
- **Good**: 3.5-3.9 (include if relevant)
- **Fair**: 3.0-3.4 (include if highly relevant)
- **Poor**: <3.0 (exclude unless essential)

### Reference Files

For detailed literature research guidance:
- **`references/literature-research/arxiv-search-guide.md`** - arXiv search strategies and URL patterns
- **`references/literature-research/paper-quality-criteria.md`** - Detailed 5-dimension evaluation rubrics

---

## Knowledge Base: Paper-Miner Installed Writing Memory

This skill consumes the **active installed writing memory** maintained by `paper-miner`:

- `references/knowledge/paper-miner-writing-memory.md`

This memory belongs to the active installed skill home, not to the source checkout copy.

Even when `paper-miner` is invoked while working inside a specific repository, it still writes mined writing knowledge only into the active installed skill memory. It does **not** maintain project-local writing memory unless the user explicitly requests that.

### Canonical memory structure

The maintained memory contains these sections:

| Section | Purpose |
|----------|---------|
| `Writing patterns mined` | Reusable rhetorical and claim-evidence patterns |
| `Structure signals` | Section flow, paragraph progression, and paper organization signals |
| `Reusable phrasing` | Transition phrases, framing templates, and concise wording |
| `Venue-specific signals` | Visible venue-facing style and convention cues |
| `How this helps our writing` | Practical guidance for future drafts, reports, and rebuttals |
| `Source index` | Source attribution for mined papers |

### How the memory is maintained

The **paper-miner agent** reads papers and merges reusable writing knowledge into this one file:

```text
You: "Learn writing patterns from this paper: path/to/paper.pdf"
↓
paper-miner analyzes the paper
↓
Extracts reusable writing signals
↓
Updates paper-miner-writing-memory.md
↓
ml-paper-writing reuses that memory later
```

### When to use this memory

Use the active installed paper-miner memory when you need:
- structure inspiration for intros, methods, results, or discussion,
- reusable transition phrases or framing templates,
- venue-facing writing signals,
- rebuttal phrasing and response structure ideas,
- examples of how strong papers support and sequence claims.

### Default read order

When drafting or revising with `ml-paper-writing`, read this memory **before** writing if the task involves:
- introduction framing,
- related work organization,
- method exposition style,
- results narration,
- discussion framing,
- venue-facing polishing.

Use this read order:
1. `references/knowledge/paper-miner-writing-memory.md`
2. repo-local evidence and experiment artifacts
3. cited papers or notes if needed
4. venue template and formatting constraints

Read narrowly, not exhaustively:
- first scan `How this helps our writing`,
- then check `Writing patterns mined` and `Structure signals`,
- then inspect `Reusable phrasing` only for concrete wording help,
- use `Venue-specific signals` when targeting a known venue.

### Contribution rule

Every paper mined by `paper-miner` should improve the same active installed memory.

Do not scatter newly mined knowledge across multiple maintained files.
Do not create project-specific paper-miner memory.
Do not duplicate near-identical patterns from the same source.

See `references/knowledge/README.md` for the detailed knowledge-base contract.

## Balancing Proactivity and Collaboration

**Default: Be proactive. Deliver drafts, then iterate.**

| Confidence Level | Action |
|-----------------|--------|
| **High** (clear repo, obvious contribution) | Write full draft, deliver, iterate on feedback |
| **Medium** (some ambiguity) | Write draft with flagged uncertainties, continue |
| **Low** (major unknowns) | Ask 1-2 targeted questions, then draft |

**Draft first, ask with the draft** (not before):

| Section | Draft Autonomously | Flag With Draft |
|---------|-------------------|-----------------|
| Abstract | Yes | "Framed contribution as X—adjust if needed" |
| Introduction | Yes | "Emphasized problem Y—correct if wrong" |
| Methods | Yes | "Included details A, B, C—add missing pieces" |
| Experiments | Yes | "Highlighted results 1, 2, 3—reorder if needed" |
| Related Work | Yes | "Cited papers X, Y, Z—add any I missed" |

**Only block for input when:**
- Target venue is unclear (affects page limits, framing)
- Multiple contradictory framings seem equally valid
- Results seem incomplete or inconsistent
- Explicit request to review before continuing

**Don't block for:**
- Word choice decisions
- Section ordering
- Which specific results to show (make a choice, flag it)
- Citation completeness (draft with what you find, note gaps)

---

## The Narrative Principle

**The single most critical insight**: Your paper is not a collection of experiments—it's a story with one clear contribution supported by evidence.

Every successful ML paper centers on what Neel Nanda calls "the narrative": a short, rigorous, evidence-based technical story with a takeaway readers care about.

**Three Pillars (must be crystal clear by end of introduction):**

| Pillar | Description | Example |
|--------|-------------|---------|
| **The What** | 1-3 specific novel claims within cohesive theme | "We prove that X achieves Y under condition Z" |
| **The Why** | Rigorous empirical evidence supporting claims | Strong baselines, experiments distinguishing hypotheses |
| **The So What** | Why readers should care | Connection to recognized community problems |

**If you cannot state your contribution in one sentence, you don't yet have a paper.**

---

## Paper Structure Workflow

### Workflow 1: Writing a Complete Paper (Iterative)

Copy this checklist and track progress. **Each step involves drafting → feedback → revision:**

```
Paper Writing Progress:
- [ ] Step 1: Define the one-sentence contribution (with scientist)
- [ ] Step 2: Draft Figure 1 → get feedback → revise
- [ ] Step 3: Draft abstract → get feedback → revise
- [ ] Step 4: Draft introduction → get feedback → revise
- [ ] Step 5: Draft methods → get feedback → revise
- [ ] Step 6: Draft experiments → get feedback → revise
- [ ] Step 7: Draft related work → get feedback → revise
- [ ] Step 8: Draft limitations → get feedback → revise
- [ ] Step 9: Complete paper checklist (required)
- [ ] Step 10: Final review cycle and submission
```

**Step 1: Define the One-Sentence Contribution**

**This step requires explicit confirmation from the scientist.**

Before writing anything, articulate and verify:
- What is the single thing your paper contributes?
- What was not obvious or present before your work?

> "I propose framing the contribution as: '[one sentence]'. Does this capture
> what you see as the main takeaway? Should we adjust the emphasis?"

**Step 2: Draft Figure 1**

Figure 1 deserves special attention—many readers skip directly to it.
- Convey core idea, approach, or most compelling result
- Use vector graphics (PDF/EPS for plots)
- Write captions that stand alone without main text
- Ensure readability in black-and-white (8% of men have color vision deficiency)

**Step 3: Write Abstract (5-Sentence Formula)**

From Sebastian Farquhar (DeepMind):

```
1. What you achieved: "We introduce...", "We prove...", "We demonstrate..."
2. Why this is hard and important
3. How you do it (with specialist keywords for discoverability)
4. What evidence you have
5. Your most remarkable number/result
```

**Delete** generic openings like "Large language models have achieved remarkable success..."

**Step 4: Write Introduction (1-1.5 pages max)**

Must include:
- 2-4 bullet contribution list (max 1-2 lines each in two-column format)
- Clear problem statement
- Brief approach overview
- Methods should start by page 2-3 maximum

**Step 5: Methods Section**

Enable reimplementation:
- Conceptual outline or pseudocode
- All hyperparameters listed
- Architectural details sufficient for reproduction
- Present final design decisions; ablations go in experiments

**Step 6: Experiments Section**

For each experiment, explicitly state:
- What claim it supports
- How it connects to main contribution
- Experimental setting (details in appendix)
- What to observe: "the blue line shows X, which demonstrates Y"

Requirements:
- Error bars with methodology (standard deviation vs standard error)
- Hyperparameter search ranges
- Compute infrastructure (GPU type, total hours)
- Seed-setting methods

**Step 7: Related Work**

Organize methodologically, not paper-by-paper:

**Good:** "One line of work uses Floogledoodle's assumption [refs] whereas we use Doobersnoddle's assumption because..."

**Bad:** "Snap et al. introduced X while Crackle et al. introduced Y."

Cite generously—reviewers like