icml-reviewer

# ICML Paper Reviewer

Enables rigorous review of ML research papers following official ICML guidelines.

## Workflow

### Step 1: Input Analysis & Mode Selection

**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.

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

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.

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## Full Review Mode (Complete Papers)

### Step 3: Systematic Evaluation
Evaluate across 7 dimensions (see `references/evaluation-criteria.md`). **Default to skepticism—require strong evidence to score highly.**

| Dimension | Key Questions (Answer with Literature Evidence) |
|-----------|---------------|
| Originality | Is this truly novel given recent work X, Y, Z? What specific aspects are incremental vs. novel? |
| Importance | Why does this problem matter? What's the real-world impact? Who will care? |
| Claims Support | Do experiments actually prove the claims? What alternative explanations exist? |
| Experimental Soundness | Are baselines from 2023+? Are comparisons fair? What's missing? |
| Clarity | Can I reproduce this from the paper? Are claims precisely stated? |
| Community Value | Will this change how people work? Or just add noise? |
| Prior Work Context | Are 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

### Step 4: Critical Cross-Check Against Literature

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.

### Step 5: Generate Review
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

### Step 6: Quality Check
- Verify all claims in review are substantiated
- Ensure constructive tone
- Check specificity of strengths/weaknesses
- Confirm questions are actionable

## Key Principles

### Be Rigorous AND Constructive
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.

### Be Specific
Bad: "The experiments are weak"
Good: "Experiments compare only against [X] from 2019, but recent baselines [Y] (2024) and [Z] (2024) should be included."

### Fair Novelty Assessment
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.

### Score Calibration

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.

### Application-Driven Papers
For application-driven ML: methods should fit real-world constraints, non-standard datasets acceptable if documented, compare against domain baselines.

## Rating Scales

**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

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## Early-Stage Feedback Mode

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.

### Step 3: Generate Formative Feedback

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)

## References

- `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