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ChineseML Paper Writing for Top AI Conferences
面向顶级 AI 会议的 ML 论文写作
Expert-level guidance for writing publication-ready papers targeting NeurIPS, ICML, ICLR, ACL, AAAI, 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.
For systems venues (OSDI, NSDI, ASPLOS, SOSP), use the systems-paper-writing skill, which provides paragraph-level structural blueprints, writing patterns, venue-specific checklists, and LaTeX templates for systems conferences.
面向 NeurIPS、ICML、ICLR、ACL、AAAI、COLM 撰写达到发表标准论文的专家级指导。本 skill 将顶级研究者（Nanda、Farquhar、Karpathy、Lipton、Steinhardt）的写作理念与实用工具结合起来，包括 LaTeX 模板、引用验证 API 和会议检查清单。
对于系统类会议（OSDI、NSDI、ASPLOS、SOSP），请使用 systems-paper-writing skill。它为系统类会议提供段落级结构蓝图、写作模式、特定会议检查清单和 LaTeX 模板。
Core Philosophy: Collaborative Writing
核心理念：协作式写作
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:
- Understand the project by exploring the repo, results, and existing documentation
- Deliver a complete first draft when confident about the contribution
- Search literature using web search and APIs to find relevant citations
- Refine through feedback cycles when the scientist provides input
- 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.
论文写作是协作过程，但 Claude 应主动交付草稿。
典型工作流从一个包含代码、结果和实验产物的研究代码仓库开始。Claude 的职责是：
- 理解项目：探索代码仓库、结果和现有文档
- 在对贡献有把握时交付完整初稿
- 检索文献：使用网络搜索和 API 查找相关引用
- 通过反馈循环进行完善：当科学家提供意见时持续迭代
- 仅在确实无法确定关键决策时询问澄清问题
关键原则：保持主动。如果代码仓库和结果都很清晰，就交付完整草稿。不要在每一节都等待反馈，从而阻塞进度——科学家都很忙。先产出可供反馈的具体内容，再根据其反馈进行迭代。
⚠️ CRITICAL: Never Hallucinate Citations
⚠️ 关键要求：绝不能幻觉式生成引用
This is the most important rule in academic writing with AI assistance.
这是 AI 辅助学术写作中最重要的规则。
The Problem
问题
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.
AI 生成的引用约有 40% 的错误率。幻觉引用——包括不存在的论文、错误的作者、不正确的年份和伪造的 DOI——是一种严重的学术不端行为，可能导致编辑直接拒稿或论文撤稿。
The Rule
规则
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 | Guess the reference |
| Can't find exact paper | Note: "placeholder - verify" | Invent similar-sounding paper |
绝不要凭记忆生成 BibTeX 条目。始终以程序化方式获取。
| 操作 | ✅ 正确做法 | ❌ 错误做法 |
|---|---|---|
| 添加引用 | 搜索 API → 验证 → 获取 BibTeX | 凭记忆编写 BibTeX |
| 对某篇论文不确定 | 标记为 | 猜测参考文献 |
| 找不到完全匹配的论文 | 注明：“placeholder - verify” | 杜撰一篇名称相近的论文 |
When You Can't Verify a Citation
无法验证引用时
If you cannot programmatically verify a citation, you MUST:
latex
% EXPLICIT PLACEHOLDER - requires human verification
\cite{PLACEHOLDER_author2024_verify_this} % TODO: Verify this citation existsAlways tell the scientist: "I've marked [X] citations as placeholders that need verification. I could not confirm these papers exist."
如果无法以程序化方式验证引用，则必须：
latex
% EXPLICIT PLACEHOLDER - requires human verification
\\cite{PLACEHOLDER_author2024_verify_this} % TODO: Verify this citation exists始终告知科学家：“我已将 [X] 条引用标记为需要验证的占位符。无法确认这些论文确实存在。”
Recommended: Install Exa MCP for Paper Search
推荐：安装 Exa MCP 以搜索论文
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.
如需获得最佳论文搜索体验，请安装 Exa MCP，它提供实时学术搜索：
Claude Code：
bash
claude mcp add exa -- npx -y mcp-remote "https://mcp.exa.ai/mcp"Cursor / VS Code（添加到 MCP 设置）：
json
{
"mcpServers": {
"exa": {
"type": "http",
"url": "https://mcp.exa.ai/mcp"
}
}
}Exa MCP 支持以下类型的搜索：
- "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"
然后使用 Semantic Scholar API 验证结果，并通过 DOI 获取 BibTeX。
Workflow 0: Starting from a Research Repository
工作流 0：从研究代码仓库开始
When the user points you at a code repository (results, configs, logs) instead of a draft, follow the repository-exploration workflow in references/from-research-repo.md to extract the contribution, results and existing citations before writing.
当用户指向一个代码仓库（包含结果、配置和日志），而不是提供现成草稿时，应遵循 references/from-research-repo.md 中的代码仓库探索工作流，在写作前提取研究贡献、结果和现有引用。
When to Use This Skill
何时使用本 Skill
Use this skill when:
- Starting from a research repo to write a paper
- Drafting or revising specific sections
- 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.
在以下场景使用本 skill：
- 从研究代码仓库开始撰写论文
- 起草或修改特定章节
- 查找并验证相关工作引用
- 按会议要求进行排版
- 改投其他会议（转换格式）
- 根据科学家反馈迭代完善草稿
始终记住：初稿是讨论的起点，不是最终成果。
Balancing Proactivity and Collaboration
在主动性与协作之间取得平衡
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)
默认策略：保持主动。先交付草稿，然后持续迭代。
| 置信度水平 | 行动 |
|---|---|
| 高（代码仓库清晰、贡献明确） | 撰写完整草稿并交付，然后根据反馈迭代 |
| 中（存在部分歧义） | 撰写草稿并标出不确定项，继续推进 |
| 低（存在大量未知信息） | 提出 1～2 个有针对性的问题，然后开始撰写 |
先起草，并随草稿一起提问，而不是在起草前提问：
| 章节 | 自主起草 | 随草稿标注 |
|---|---|---|
| 摘要 | 是 | “将贡献定位为 X——如有需要请调整” |
| 引言 | 是 | “突出问题 Y——如果判断有误请纠正” |
| 方法 | 是 | “包含细节 A、B、C——可补充缺失内容” |
| 实验 | 是 | “突出结果 1、2、3——可按需重新排序” |
| 相关工作 | 是 | “引用了论文 X、Y、Z——可补充我遗漏的文献” |
仅在以下情况阻塞并等待输入：
- 目标会议不明确（会影响页数限制和论文定位）
- 多种相互矛盾的论文定位看起来同样合理
- 结果似乎不完整或前后不一致
- 用户明确要求审阅后再继续
以下情况不要阻塞：
- 用词选择
- 章节顺序
- 具体展示哪些结果（自行选择并标注）
- 引用完整性（根据已找到的内容起草，并说明缺口）
The Narrative Principle
叙事原则
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.
最关键的洞察：论文不是实验的集合——它是一个由证据支撑、具有单一明确贡献的故事。
每篇成功的 ML 论文都围绕 Neel Nanda 所说的“the narrative”展开：这是一个简短、严谨、以证据为基础且读者关心的技术故事。
三大支柱（到引言结束时必须非常清晰）：
| 支柱 | 描述 | 示例 |
|---|---|---|
| 具体贡献是什么（The What） | 在统一主题下提出 1～3 项具体的新主张 | “我们证明 X 在条件 Z 下能够达到 Y” |
| 为什么成立（The Why） | 提供支持这些主张的严格实证证据 | 有力的基线，以及能够区分不同假设的实验 |
| 为什么重要（The So What） | 解释读者为什么应该关注这项工作 | 与领域公认的重要问题建立联系 |
如果你无法用一句话说明贡献，那么论文尚未真正成形。
Paper Structure Workflow
论文结构工作流
Workflow 1: Writing a Complete Paper (Iterative)
工作流 1：撰写完整论文（迭代式）
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 submissionStep 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/resultDelete 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 likely authored relevant papers.
Step 8: Limitations Section (REQUIRED)
All major conferences require this. Counter-intuitively, honesty helps:
- Reviewers are instructed not to penalize honest limitation acknowledgment
- Pre-empt criticisms by identifying weaknesses first
- Explain why limitations don't undermine core claims
Step 9: Paper Checklist
NeurIPS, ICML, and ICLR all require paper checklists. See references/checklists.md.
复制以下检查清单并跟踪进度。每个步骤都包括起草 → 反馈 → 修改：
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步骤 1：定义一句话贡献
此步骤需要科学家明确确认。
开始撰写前，应明确表达并确认：
- 这篇论文的核心贡献究竟是什么？
- 哪些内容在研究开展前并不明显或并不存在？
“我建议将贡献定位为：‘[一句话总结]’。这是否准确概括了你认为最重要的结论？是否需要调整侧重点？”
步骤 2：起草图 1
图 1 需要特别关注——许多读者会直接查看该图。
- 传达核心思想、方法或最引人注目的结果
- 使用矢量图形（绘图采用 PDF/EPS）
- 图注应脱离正文也能独立理解
- 确保在黑白条件下仍清晰可读（8% 的男性存在色觉缺陷）
步骤 3：撰写摘要（5 句话公式）
来自 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删除“Large language models have achieved remarkable success...”之类泛泛的开场句。
步骤 4：撰写引言（最多 1～1.5 页）
必须包括：
- 2～4 条贡献列表（双栏格式下每条最多 1～2 行）
- 清晰的问题陈述
- 简要的方法概述
- 方法部分最迟应从第 2～3 页开始
步骤 5：方法部分
确保他人能够复现：
- 提供概念性框架或伪代码
- 列出全部超参数
- 提供足以复现实验的架构细节
- 呈现最终设计决策；消融实验放在实验部分
步骤 6：实验部分
对于每项实验，明确说明：
- 它支持哪项主张
- 它与核心贡献有何联系
- 实验设置（详细信息放在附录中）
- 应观察到什么结果：“蓝线展示了 X，从而证明 Y”
要求：
- 误差棒及其计算方法（标准差或标准误）
- 超参数搜索范围
- 计算基础设施（GPU 类型、总计算时长）
- 随机种子设置方法
步骤 7：相关工作
按方法体系组织，而不是逐篇介绍：
良好：“一条研究路线采用 Floogledoodle 的假设 [refs]，而我们采用 Doobersnoddle 的假设，因为……”
不佳：“Snap 等人引入了 X，而 Crackle 等人引入了 Y。”
应广泛引用相关文献——审稿人很可能就是相关论文的作者。
步骤 8：局限性部分（必需）
所有主要会议都要求提供这一部分。出人意料的是，坦诚通常会发挥积极作用：
- 审稿人通常被要求不得因作者坦诚承认局限性而扣分
- 主动指出弱点，可以提前化解潜在批评
- 解释这些局限为何不会动摇核心主张
步骤 9：论文检查清单
NeurIPS、ICML 和 ICLR 都要求填写论文检查清单。参见 references/checklists.md。
Writing Philosophy for Top ML Conferences
顶级 ML 会议的写作理念
Sentence-level clarity, word choice, time allocation and what reviewers actually read are covered in references/writing-philosophy.md. Read it before polishing prose.
句子层面的清晰度、用词、时间分配以及审稿人实际会阅读的内容，详见 references/writing-philosophy.md。润色文字前应先阅读该文档。
Conference Requirements Quick Reference
会议要求快速参考
ML/AI Conferences
ML/AI 会议
| Conference | Page Limit | Extra for Camera-Ready | Key Requirement |
|---|---|---|---|
| NeurIPS 2025 | 9 pages | +0 | Mandatory checklist, lay summary for accepted |
| ICML 2026 | 8 pages | +1 | Broader Impact Statement required |
| ICLR 2026 | 9 pages | +1 | LLM disclosure required, reciprocal reviewing |
| ACL 2025 | 8 pages (long) | varies | Limitations section mandatory |
| AAAI 2026 | 7 pages | +1 | Strict style file adherence |
| COLM 2025 | 9 pages | +1 | Focus on language models |
Systems Conferences (OSDI, NSDI, ASPLOS, SOSP): See the systems-paper-writing skill for page limits, templates, deadlines, and submission rules.
Universal Requirements:
- Double-blind review (anonymize submissions)
- References don't count toward page limit
- Appendices unlimited but reviewers not required to read
- LaTeX required for all venues
LaTeX Templates: See templates/ directory for all conference templates.
| 会议 | 页数限制 | Camera-Ready 额外页数 | 关键要求 |
|---|---|---|---|
| NeurIPS 2025 | 9 页 | +0 | 强制要求检查清单；录用论文需提供通俗摘要 |
| ICML 2026 | 8 页 | +1 | 必须提供 Broader Impact Statement |
| ICLR 2026 | 9 页 | +1 | 必须披露 LLM 使用情况；采用互审机制 |
| ACL 2025 | 8 页（长文） | 视情况而定 | 必须包含局限性部分 |
| AAAI 2026 | 7 页 | +1 | 必须严格遵循样式文件 |
| COLM 2025 | 9 页 | +1 | 聚焦语言模型 |
系统类会议（OSDI、NSDI、ASPLOS、SOSP）：有关页数限制、模板、截止日期和投稿规则，请参阅 systems-paper-writing skill。
通用要求：
- 双盲评审（投稿必须匿名）
- 参考文献不计入页数限制
- 附录篇幅不限，但审稿人没有义务阅读
- 所有会场均要求使用 LaTeX
**LaTeX 模板：**所有会议模板均位于 templates/ 目录中。
Using LaTeX Templates and Converting Between Venues
使用 LaTeX 模板及转换会议格式
Starting a paper from a bundled template (Workflow 4), compiling, template pitfalls, and converting between venues after a rejection (Workflow 3) are covered in references/latex-templates-and-conversion.md. Always copy the complete template directory from rather than writing LaTeX preambles from memory.
templates/如何从内置模板开始写论文（工作流 4）、编译论文、避免常见模板问题，以及被拒后转换会议格式（工作流 3），详见 references/latex-templates-and-conversion.md。始终应复制 中完整的模板目录，不要凭记忆编写 LaTeX preamble。
templates/Citation Workflow (Hallucination Prevention)
引用工作流（防止幻觉）
⚠️ CRITICAL: AI-generated citations have ~40% error rate. Never write BibTeX from memory.
⚠️ 关键要求：AI 生成的引用约有 40% 的错误率。绝不要凭记忆编写 BibTeX。
The Golden Rule
黄金法则
IF you cannot programmatically fetch a citation:
→ Mark it as [CITATION NEEDED] or [PLACEHOLDER - VERIFY]
→ Tell the scientist explicitly
→ NEVER invent a plausible-sounding referenceIF you cannot programmatically fetch a citation:
→ Mark it as [CITATION NEEDED] or [PLACEHOLDER - VERIFY]
→ Tell the scientist explicitly
→ NEVER invent a plausible-sounding referenceWorkflow 2: Adding Citations
工作流 2：添加引用
Citation Verification (MANDATORY for every citation):
- [ ] Step 1: Search using Exa MCP or Semantic Scholar API
- [ ] Step 2: Verify paper exists in 2+ sources (Semantic Scholar + arXiv/CrossRef)
- [ ] Step 3: Retrieve BibTeX via DOI (programmatically, not from memory)
- [ ] Step 4: Verify the claim you're citing actually appears in the paper
- [ ] Step 5: Add verified BibTeX to bibliography
- [ ] Step 6: If ANY step fails → mark as placeholder, inform scientistStep 0: Use Exa MCP for Initial Search (Recommended)
If Exa MCP is installed, use it to find relevant papers:
Search: "RLHF language model alignment 2023"
Search: "sparse autoencoders interpretability"
Search: "attention mechanism transformers Vaswani"Then verify each result with Semantic Scholar and fetch BibTeX via DOI.
Step 1: Search Semantic Scholar
python
from semanticscholar import SemanticScholar
sch = SemanticScholar()
results = sch.search_paper("attention mechanism transformers", limit=5)
for paper in results:
print(f"{paper.title} - {paper.paperId}")
print(f" DOI: {paper.externalIds.get('DOI', 'N/A')}")Step 2: Verify Existence
Confirm paper appears in at least two sources (Semantic Scholar + CrossRef/arXiv).
Step 3: Retrieve BibTeX via DOI
python
import requests
def doi_to_bibtex(doi: str) -> str:
"""Get verified BibTeX from DOI via CrossRef."""
response = requests.get(
f"https://doi.org/{doi}",
headers={"Accept": "application/x-bibtex"}
)
response.raise_for_status()
return response.textCitation Verification (MANDATORY for every citation):
- [ ] Step 1: Search using Exa MCP or Semantic Scholar API
- [ ] Step 2: Verify paper exists in 2+ sources (Semantic Scholar + arXiv/CrossRef)
- [ ] Step 3: Retrieve BibTeX via DOI (programmatically, not from memory)
- [ ] Step 4: Verify the claim you're citing actually appears in the paper
- [ ] Step 5: Add verified BibTeX to bibliography
- [ ] Step 6: If ANY step fails → mark as placeholder, inform scientist步骤 0：使用 Exa MCP 进行初始搜索（推荐）
如果已安装 Exa MCP，可使用它查找相关论文：
Search: "RLHF language model alignment 2023"
Search: "sparse autoencoders interpretability"
Search: "attention mechanism transformers Vaswani"然后使用 Semantic Scholar 验证每条结果，并通过 DOI 获取 BibTeX。
步骤 1：搜索 Semantic Scholar
python
from semanticscholar import SemanticScholar
sch = SemanticScholar()
results = sch.search_paper("attention mechanism transformers", limit=5)
for paper in results:
print(f"{paper.title} - {paper.paperId}")
print(f" DOI: {paper.externalIds.get('DOI', 'N/A')}")步骤 2：验证论文存在
确认论文至少出现在两个来源中（Semantic Scholar + CrossRef/arXiv）。
步骤 3：通过 DOI 获取 BibTeX
python
import requests
def doi_to_bibtex(doi: str) -> str:
"""Get verified BibTeX from DOI via CrossRef."""
response = requests.get(
f"https://doi.org/{doi}",
headers={"Accept": "application/x-bibtex"}
)
response.raise_for_status()
return response.textExample
Example
bibtex = doi_to_bibtex("10.48550/arXiv.1706.03762")
print(bibtex)
**Step 4: Verify Claims**
Before citing for a specific claim, access the paper and confirm the attributed claim actually appears.
**Step 5: Handle Failures Explicitly**
If you cannot verify a citation at ANY step:
```latex
% Option 1: Explicit placeholder
\cite{PLACEHOLDER_smith2023_verify} % TODO: Could not verify - scientist must confirm
% Option 2: Note in text
... as shown in prior work [CITATION NEEDED - could not verify Smith et al. 2023].Always inform the scientist:
"I could not verify the following citations and have marked them as placeholders:
- Smith et al. 2023 on reward hacking - could not find in Semantic Scholar
- Jones 2022 on scaling laws - found similar paper but different authors Please verify these before submission."
bibtex = doi_to_bibtex("10.48550/arXiv.1706.03762")
print(bibtex)
**步骤 4：验证主张**
在针对某项具体主张引用论文前，应访问论文并确认所归属的主张确实出现在论文中。
**步骤 5：明确处理失败情况**
如果任何步骤都无法验证引用：
```latex
% Option 1: Explicit placeholder
\\cite{PLACEHOLDER_smith2023_verify} % TODO: Could not verify - scientist must confirm
% Option 2: Note in text
... as shown in prior work [CITATION NEEDED - could not verify Smith et al. 2023].始终告知科学家：
“我无法验证以下引用，并已将其标记为占位符：
- Smith 等人 2023 年关于 reward hacking 的论文——未在 Semantic Scholar 中找到
- Jones 等人 2022 年关于 scaling laws 的论文——找到了相似论文，但作者不同 请在提交前验证这些引用。”
Summary: Citation Rules
摘要：引用规则
| Situation | Action |
|---|---|
| Found paper, got DOI, fetched BibTeX | ✅ Use the citation |
| Found paper, no DOI | ✅ Use arXiv BibTeX or manual entry from paper |
| Paper exists but can't fetch BibTeX | ⚠️ Mark placeholder, inform scientist |
| Uncertain if paper exists | ❌ Mark |
| "I think there's a paper about X" | ❌ NEVER cite - search first or mark placeholder |
🚨 NEVER generate BibTeX from memory—always fetch programmatically. 🚨
See references/citation-workflow.md for complete API documentation.
| 情况 | 行动 |
|---|---|
| 找到论文、获得 DOI 并获取 BibTeX | ✅ 使用该引用 |
| 找到论文，但没有 DOI | ✅ 使用 arXiv BibTeX，或根据论文手动录入 |
| 论文确实存在，但无法获取 BibTeX | ⚠️ 标记为占位符并告知科学家 |
| 不确定论文是否存在 | ❌ 标记 |
| “我记得有一篇关于 X 的论文” | ❌ 绝不能引用——先搜索，否则标记为占位符 |
🚨 绝不要凭记忆生成 BibTeX——始终以程序化方式获取。🚨
完整的 API 文档请参阅 references/citation-workflow.md。
Troubleshooting, Reviewer Criteria and Figures
常见问题、审稿标准与图表
Troubleshooting common writing problems, the criteria reviewers score on, and table/figure conventions are in references/issues-reviewers-figures.md.
常见写作问题的排查方法、审稿人评分标准以及表格和插图规范，详见 references/issues-reviewers-figures.md。
References & Resources
参考资料与资源
Reference Documents (Deep Dives)
参考文档（深入阅读）
| Document | Contents |
|---|---|
| writing-guide.md | Gopen & Swan 7 principles, Ethan Perez micro-tips, word choice |
| citation-workflow.md | Citation APIs, Python code, BibTeX management |
| checklists.md | NeurIPS 16-item, ICML, ICLR, ACL requirements |
| reviewer-guidelines.md | Evaluation criteria, scoring, rebuttals |
| sources.md | Complete bibliography of all sources |
| from-research-repo.md | Workflow 0: turning a code repository into a paper |
| writing-philosophy.md | Time allocation, clarity, word choice, what reviewers read |
| latex-templates-and-conversion.md | Template setup, compilation, pitfalls, venue conversion |
| issues-reviewers-figures.md | Common issues, reviewer criteria, tables and figures |
| 文档 | 内容 |
|---|---|
| writing-guide.md | Gopen & Swan 的 7 项原则、Ethan Perez 的微技巧、用词 |
| citation-workflow.md | 引用 API、Python 代码、BibTeX 管理 |
| checklists.md | NeurIPS 16 项检查清单，以及 ICML、ICLR、ACL 的要求 |
| reviewer-guidelines.md | 评估标准、评分、rebuttal |
| sources.md | 所有来源的完整参考文献表 |
| from-research-repo.md | 工作流 0：将代码仓库转化为论文 |
| writing-philosophy.md | 时间分配、清晰度、用词、审稿人实际阅读的内容 |
| latex-templates-and-conversion.md | 模板设置、编译、常见问题、会议格式转换 |
| issues-reviewers-figures.md | 常见问题、审稿标准、表格与插图 |
LaTeX Templates
LaTeX 模板
Templates in directory:
templates/- ML/AI: ICML 2026, ICLR 2026, NeurIPS 2025, ACL/EMNLP, AAAI 2026, COLM 2025
- Systems (OSDI, NSDI, ASPLOS, SOSP): See systems-paper-writing skill
Compiling to PDF:
- VS Code/Cursor: Install LaTeX Workshop extension + TeX Live → Save to auto-compile
- Command line: or
latexmk -pdf main.tex+pdflatexworkflowbibtex - Online: Upload to Overleaf
See templates/README.md for detailed setup instructions.
templates/- ML/AI：ICML 2026、ICLR 2026、NeurIPS 2025、ACL/EMNLP、AAAI 2026、COLM 2025
- 系统类（OSDI、NSDI、ASPLOS、SOSP）：请参阅 systems-paper-writing skill
编译为 PDF：
- VS Code/Cursor：安装 LaTeX Workshop 扩展和 TeX Live → 保存后自动编译
- 命令行：或
latexmk -pdf main.tex+pdflatex工作流bibtex - 在线：上传到 Overleaf
有关详细设置说明，请参阅 templates/README.md。
Key External Sources
主要外部来源
Writing Philosophy:
- Neel Nanda: How to Write ML Papers - Narrative, "What/Why/So What"
- Farquhar: How to Write ML Papers - 5-sentence abstract
- Gopen & Swan: Science of Scientific Writing - 7 reader expectation principles
- Lipton: Heuristics for Scientific Writing - Word choice
- Perez: Easy Paper Writing Tips - Micro-level clarity
Systems Venues: See the systems-paper-writing skill for OSDI, NSDI, ASPLOS, SOSP links and guides
写作理念：Neel Nanda: How to Write ML Papers - 叙事、“What/Why/So What”
Farquhar: How to Write ML Papers - 5 句话摘要公式
Gopen & Swan: Science of Scientific Writing - 读者预期的 7 项原则
Perez: Easy Paper Writing Tips - 微观层面的清晰表达
**系统类会议：**有关 OSDI、SOSP、ASPLOS、NSDI 的链接和指南，请参阅 systems-paper-writing skill
Related skills
相关 Skills
- : Provides paragraph-level structural blueprints for 10-12 page systems papers targeting OSDI, SOSP, ASPLOS, NSDI, and EuroSys.
systems-paper-writing - : Plan and write responses to peer review, including journal "response to reviewers" letters for revise-and-resubmit, conference rebuttals un…
rebuttal-and-response-to-reviewers - : Prepare the reproducibility, transparency and open-science parts of a paper, including data and code availability statements, reproducibili…
reproducibility-statement
- ：为面向 OSDI、SOSP、ASPLOS、NSDI 和 EuroSys 的 10～12 页系统类论文提供段落级结构蓝图。
systems-paper-writing - ：规划并撰写同行评审回复，包括用于 revise-and-resubmit 的期刊“response to reviewers”信函、会议 rebuttal 答……
rebuttal-and-response-to-reviewers - ：准备论文中的可复现性、透明度和开放科学部分，包括数据和代码可用性声明、可复现……
reproducibility-statement