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ICML Paper Reviewer

ICML 论文评审工具

Enables rigorous review of ML research papers following official ICML guidelines.
能够遵循ICML官方指南对ML研究论文进行严谨评审。

Workflow

工作流程

Step 1: Input Analysis & Mode Selection

步骤1：输入分析与模式选择

Determine input type:
  • Complete paper: PDF/text with abstract, methodology, experiments, results → Full Review Mode
  • Incomplete document: Missing major sections, labeled draft/proposal, or user indicates early stage → Early-Stage Feedback Mode
  • Code repository: User points to folder/repo path → Repository Review Mode
For complete papers, extract: title, abstract, main claims, methodology, experiments, results. Identify paper type: theoretical, methodological, algorithmic, empirical, bridge paper, or application-driven.
For code repositories, first explore: read README, scan code structure, find experiment scripts/results, identify the research question and what's implemented.
确定输入类型：
  • 完整论文：包含摘要、方法论、实验、结果的PDF/文本 → 完整评审模式
  • 未完成文档：缺少主要章节、标记为草稿/提案，或用户指明处于早期阶段 → 早期反馈模式
  • 代码仓库：用户提供文件夹/代码仓库路径 → 代码仓库评审模式
针对完整论文，提取：标题、摘要、核心主张、方法论、实验、结果。确定论文类型：理论型、方法型、算法型、实证型、交叉型或应用驱动型。
针对代码仓库，首先探索：阅读README、梳理代码结构、查找实验脚本/结果、明确研究问题及已实现内容。

Step 2: Prior Work Grounding (Critical - All Modes)

步骤2：已有研究关联校验（所有模式必备）

This step applies to ALL input types. Grounding in reality is essential for any meaningful feedback.
  1. Generate 3-5 search queries based on the research topic: benchmarks/baselines, same problem, related techniques
  2. Use WebSearch to find recent arXiv papers and published work
  3. Fetch abstracts of 5-10 most relevant papers
  4. Critically synthesize:
    • What specific claims in this paper are already addressed by prior work?
    • What are the actual quantitative improvements over recent baselines?
    • Are claimed "novelties" actually novel given the literature?
    • What gaps truly exist vs. what the authors claim exists?
Critical mindset:
  • Your job is to verify claims against reality, not accept them at face value
  • Most papers overclaim—your review should ground their contributions in what the literature actually shows
  • Default to skepticism: Assume claims are overstated until proven otherwise by evidence
  • Authors have selection bias toward their own work; you represent the community's interests
  • Be the critical voice that ensures published work actually advances the field
Then proceed to mode-specific evaluation.
此步骤适用于所有输入类型。与真实研究现状关联是提供有意义反馈的核心。
  1. 基于研究主题生成3-5个搜索关键词：基准模型/基线、同类问题、相关技术
  2. 使用WebSearch查找近期arXiv论文及已发表成果
  3. 获取5-10篇最相关论文的摘要
  4. 批判性整合分析：
    • 本文中的哪些具体主张已被已有研究覆盖？
    • 相较于近期基线模型，本文实际的量化提升有多少？
    • 本文声称的“创新性”在现有文献中是否真的新颖？
    • 真实存在的研究空白与作者声称的空白有何差异？
批判性思维准则：
  • 你的职责是对照真实研究现状验证主张，而非表面接受
  • 多数论文存在过度主张——你的评审应将其贡献锚定在文献实际展示的内容上
  • 默认保持怀疑态度：在证据证明之前，假设主张存在夸大
  • 作者对自身研究存在选择偏差；你代表的是整个研究社区的利益
  • 你要成为确保发表成果真正推动领域发展的批判性声音
之后进入对应模式的评估环节。

Full Review Mode (Complete Papers)

完整评审模式（针对完整论文）

Step 3: Systematic Evaluation

步骤3：系统性评估

Evaluate across 7 dimensions (see 
references/evaluation-criteria.md
). Default to skepticism—require strong evidence to score highly.
DimensionKey Questions (Answer with Literature Evidence)
OriginalityIs this truly novel given recent work X, Y, Z? What specific aspects are incremental vs. novel?
ImportanceWhy does this problem matter? What's the real-world impact? Who will care?
Claims SupportDo experiments actually prove the claims? What alternative explanations exist?
Experimental SoundnessAre baselines from 2023+? Are comparisons fair? What's missing?
ClarityCan I reproduce this from the paper? Are claims precisely stated?
Community ValueWill this change how people work? Or just add noise?
Prior Work ContextAre comparisons accurate? What recent work (last 2 years) is missing?
Evaluation mindset:
  • Start from neutral and require evidence to move up or down
  • Compare every claim against what you found in the literature search
  • Most papers are incremental—high originality scores are rare
  • Weak baselines or missing comparisons are critical flaws, not minor issues
从7个维度进行评估（详见
references/evaluation-criteria.md
）。默认保持怀疑态度——需要充分证据才能给出高分。
维度核心问题（需结合文献证据作答）
原创性结合近期研究X、Y、Z来看，本文是否真的具有创新性？哪些部分是增量改进，哪些是真正的创新？
重要性该问题为何重要？有哪些实际应用价值？哪些群体将关注此研究？
主张支撑实验是否真正证明了主张？存在哪些其他可能的解释？
实验严谨性基线模型是否为2023年及以后的？对比是否公平？缺少了哪些内容？
清晰度仅通过论文内容能否复现研究？主张的表述是否精准？
社区价值本文是否会改变研究人员的工作方式？还是只是增加文献噪音？
已有研究关联上下文是否恰当？是否缺少关键引用？
评估思维准则：
  • 从中立立场出发，根据证据调整评分
  • 将每个主张与你在文献搜索中发现的内容进行对比
  • 多数论文属于增量改进——高原创性得分应稀缺
  • 弱基线模型或缺失关键对比属于严重缺陷，而非小问题

Step 4: Critical Cross-Check Against Literature

步骤4：与文献的关键交叉校验

Before writing the review, explicitly verify:
  1. Baselines check: List baselines used in paper. List baselines from your literature search of adjacent papers. What's missing?
  2. Methodology check: How do 2-3 adjacent papers approach this problem? Does this paper follow similar methodology? If not, why not?
  3. Claims check: List main claims. For each, cite specific evidence from experiments or proofs. If insufficient, note it.
  4. Citations check: Which papers from your search are cited? Which are missing? Why?
  5. Novelty check: List claimed novelties. For each, cite specific prior work that does or doesn't do this.
This step is not optional. Your review must reference specific findings from your literature search.
撰写评审前，需明确验证：
  1. 基线模型校验：列出本文使用的基线模型，列出你在同类论文文献搜索中发现的基线模型。缺少了哪些？
  2. 方法论校验：2-3篇同类论文是如何解决该问题的？本文是否采用了类似方法论？如果没有，原因是什么？
  3. 主张校验：列出核心主张，针对每个主张，引用实验或证明中的具体证据。若证据不足，需注明。
  4. 引用校验：你搜索到的论文中有哪些被本文引用？哪些未被引用？原因是什么？
  5. 创新性校验：列出本文声称的创新点，针对每个创新点，引用具体的已有研究说明其是否已被实现。
此步骤为必选项。你的评审必须引用文献搜索中的具体发现。

Step 5: Generate Review

步骤5：生成评审报告

Follow the ICML review form (see 
references/review-template.md
):
  1. Summary - Neutral, factual (should not be disputed by authors)
  2. Claims and Evidence - Are claims supported? Compare to what literature shows
  3. Relation to Prior Work - Proper context? Missing citations? List specific missing papers
  4. Strengths - Specific and substantive, compared to standards in adjacent work
  5. Weaknesses - Constructive, explain severity, cite specific literature for comparison
  6. Questions for Authors - Numbered, explain impact on evaluation
  7. Minor Issues - Typos, suggestions
  8. Overall Recommendation - 1-5 scale with justification grounded in literature comparison
  9. Confidence Score - 1-5 scale
遵循ICML评审模板（详见
references/review-template.md
）：
  1. 摘要 - 中立、客观（不应被作者质疑）
  2. 主张与证据 - 主张是否有支撑？对比文献展示的内容
  3. 与已有研究的关联 - 上下文是否恰当？是否缺失引用？列出具体缺失的论文
  4. 优势 - 具体且有实质内容，与同类研究的标准对比
  5. 不足 - 具有建设性，说明严重程度，引用具体文献进行对比
  6. 向作者提出的问题 - 编号列出，说明对评估的影响
  7. 小问题 - 拼写错误、优化建议
  8. 整体推荐 - 1-5分制，并结合文献对比给出理由
  9. 置信度评分 - 1-5分制

Step 6: Quality Check

步骤6：质量检查

  • Verify all claims in review are substantiated
  • Ensure constructive tone
  • Check specificity of strengths/weaknesses
  • Confirm questions are actionable
  • 验证评审中的所有主张均有依据
  • 确保语气具有建设性
  • 检查优势/不足的具体性
  • 确认问题具有可操作性

Key Principles

核心原则

Be Rigorous AND Constructive

严谨且具建设性

Your primary duty is to the research community—publishing weak papers dilutes the literature.
  • Be honest: Don't inflate scores to be nice. If baselines are weak, say so clearly.
  • Be specific: Always cite which literature contradicts or supports claims.
  • Be fair: Criticism should be substantiated by evidence or literature.
  • Be actionable: Tell authors exactly what would fix the issues.
"Review the papers of others as you would wish your own to be reviewed"—with rigor, honesty, and specific feedback grounded in the literature.
你的首要职责是对研究社区负责——发表低质量论文会稀释文献价值。
  • 保持诚实：不要为了客气而抬高分数。如果基线模型较弱，需明确指出。
  • 保持具体：始终引用与主张矛盾或支撑主张的文献。
  • 保持公平：批评需有证据或文献支撑。
  • 保持可落地：明确告知作者如何解决问题。
“以你期望自己论文被评审的方式评审他人的论文”——严谨、诚实，并基于文献给出具体反馈。

Be Specific

保持具体

Bad: "The experiments are weak" Good: "Experiments compare only against [X] from 2019, but recent baselines [Y] (2024) and [Z] (2024) should be included."
反面示例：“实验设计薄弱” 正面示例：“实验仅对比了2019年的[X]模型，但应加入近期基线模型[Y]（2024）和[Z]（2024）。”

Fair Novelty Assessment

公平评估创新性

Originality may arise from: creative combinations, new domains, removing restrictive assumptions, novel datasets, new problem formulations.
But: Most claimed novelty is actually incremental. Verify against literature before accepting novelty claims.
原创性可能来源于：创意组合、新领域应用、移除限制性假设、新数据集、新问题定义。
但需注意：多数声称的创新性实际上只是增量改进。在接受创新主张前，需对照文献进行验证。

Score Calibration

评分校准

Use this reference frame:
  • 5s are rare: Reserve for papers that will clearly influence the field
  • 4s are uncommon: Solid papers with rigorous execution and clear contributions
  • 3s are common: Papers with merit but significant limitations
  • 2s are common: Incremental work or work with major methodological issues
  • 1s indicate fundamental problems: Wrong results, no contribution, or severe ethical issues
If you find yourself giving mostly 4s and 5s, you're likely being too generous. Re-calibrate against what the literature shows is standard.
参考以下评分框架：
  • 5分罕见：仅授予将明显影响领域发展的论文
  • 4分少见：扎实的贡献、严谨的执行，仅存在小问题
  • 3分常见：有一定价值但存在显著局限性的论文
  • 2分常见：增量改进或存在重大方法论问题的论文
  • 1分代表根本性问题：结果错误、无实质贡献或存在严重伦理问题
如果你发现自己给出的分数多为4分和5分，可能过于宽松。需对照文献展示的行业标准重新校准。

Application-Driven Papers

应用驱动型论文

For application-driven ML: methods should fit real-world constraints, non-standard datasets acceptable if documented, compare against domain baselines.
针对应用驱动型ML论文：方法需符合实际场景约束，非标准数据集需有文档说明，需与领域基线模型对比。

Rating Scales

评分标准

Overall (1-5): Use the full range. Most papers should be 2-3.
  • 5 (Strong Accept): Significant contribution, will be influential, no major flaws
  • 4 (Accept): Solid contribution, rigorous execution, minor issues only
  • 3 (Weak Accept): Contribution exists but limited; or good idea with execution flaws
  • 2 (Weak Reject): Incremental contribution insufficient for venue; or significant methodological issues
  • 1 (Reject): Fundamental flaws, not ready, or no meaningful contribution
Red flags that should lower scores:
  • Baselines older than 2 years (unless explicitly justified)
  • Missing comparisons to obvious related work from literature search
  • Claims not directly supported by presented experiments
  • Novelty claims contradicted by prior work
Confidence (1-5): 5=Expert/certain, 4=Confident, 3=Fairly confident, 2=Uncertain, 1=Not in area
整体评分（1-5分）：使用全评分范围。多数论文应得2-3分。
  • 5分（强烈录用）：重大贡献，具有影响力，无重大缺陷
  • 4分（录用）：扎实贡献，执行严谨，仅存在小问题
  • 3分（弱录用）：存在贡献但有限；或创意良好但执行存在缺陷
  • 2分（弱拒稿）：增量贡献不足以达到会议要求；或存在重大方法论问题
  • 1分（拒稿）：存在根本性缺陷，未准备就绪，或无实质贡献
会降低评分的红色预警：
  • 基线模型距今超过2年（除非有明确理由说明）
  • 缺失与文献搜索中明确相关研究的对比
  • 主张未得到所展示实验的直接支撑
  • 创新性主张与已有研究矛盾
置信度评分（1-5分）：5=专家/确定，4=有信心，3=较为有信心，2=不确定，1=非本领域

Early-Stage Feedback Mode

早期反馈模式

Use this mode for incomplete drafts, research proposals, or code repositories. Focus shifts from "accept/reject evaluation" to "constructive guidance on how to make this publishable."
After completing Steps 1-2 (input analysis and prior work grounding), proceed here.
此模式适用于未完成的草稿、研究提案或代码仓库。重点从“录用/拒稿评估”转向“提供建设性指导，帮助成果达到发表标准”。
完成步骤1-2（输入分析和已有研究关联校验）后，进入此模式。

Step 3: Generate Formative Feedback

步骤3：生成发展性反馈

Use the Early-Stage Feedback Template (see 
references/review-template.md
). No numerical scores—focus on constructive guidance.
For code repositories, additionally address:
  • Code quality and organization
  • Experiment design and reproducibility
  • What's missing for a paper (baselines, ablations, analysis)
使用早期反馈模板（详见
references/review-template.md
）。不提供数字评分——重点为建设性指导。
针对代码仓库，额外关注：
  • 代码质量与组织结构
  • 实验设计与可复现性
  • 撰写论文所需补充的内容（基线模型、消融实验、分析内容）

References

参考资料

  • references/evaluation-criteria.md
    - Detailed criteria for each dimension
  • references/review-template.md
    - Full template with examples
  • references/common-issues.md
    - Common paper issues to identify
  • references/evaluation-criteria.md
    - 各维度的详细评估标准
  • references/review-template.md
    - 完整模板及示例
  • references/common-issues.md
    - 需识别的常见论文问题