5.3 KiB
5.3 KiB
Paper Review
Goal
Use an adversarial, reviewer-style checklist to detect reject risks early and revise the paper before submission.
Core Principle
Pursue perfectionism in paper quality: assume reviewers will probe every weak point and proactively fix them.
Critical Rule (Do Not Violate)
Every major claim, especially in Abstract and Introduction, must be:
- technically correct, and
- explicitly supported by experimental evidence.
If a claim is not supported, either add evidence or weaken/remove the claim.
What Usually Gets a Paper Accepted
- Sufficient contribution (for example: novel task, novel pipeline, novel module, novel design choices, new experimental findings, or new insight).
- Better empirical performance than prior methods under fair comparisons.
- Sufficient comparison experiments and ablation studies.
Common Rejection Dimensions
| Rejection Dimension | Typical Failure Signals |
|---|---|
| 1. Insufficient contribution | 1.1 Targeted failure cases are too common. 1.2 Proposed technique is already well explored; expected gains are predictable/well-known. |
| 2. Unclear writing | 2.1 Missing technical details; work is not reproducible. 2.2 A method module lacks clear motivation. |
| 3. Weak empirical effect | 3.1 Improvement over prior methods is only marginal. 3.2 Even if better than previous methods, absolute performance is still not strong enough. |
| 4. Incomplete evaluation | 4.1 Missing ablation studies. 4.2 Missing important baselines or important evaluation metrics. 4.3 Datasets are too simple to prove the method truly works. |
| 5. Problematic method design | 5.1 Experimental setting is unrealistic. 5.2 Method has technical flaws and appears unreasonable. 5.3 Method is not robust and needs per-scenario hyperparameter tuning. 5.4 New design introduces stronger limitations than its benefits, leading to negative net value. |
End-of-Paper Self-Review Question List
Add this checklist near the end of the draft while revising. Use each question to trigger concrete edits before submission.
1. Contribution
- What new knowledge does this paper give to readers?
- Are we solving a truly meaningful failure case, not a trivial/common one?
- Is the technical idea genuinely non-obvious beyond well-explored practice?
- Is our gain surprising or insightful rather than a predictable improvement?
- Is there at least one clear novelty type (task/pipeline/module/design finding/insight)?
2. Writing Clarity
- Can a knowledgeable reader reproduce the method from the paper?
- Did we provide enough technical detail for each key module?
- Is the motivation of every module explicit and logically connected to a challenge?
- Are terms and notation consistent across sections?
- Does each paragraph carry one clear message with smooth transitions?
3. Experimental Strength
- Are improvements over strong baselines meaningful, not just statistically tiny?
- Is absolute performance competitive enough for the target venue?
- Are gains consistent across multiple datasets/settings/metrics?
- Do we report both strengths and failure cases honestly?
4. Evaluation Completeness
- Do we include ablations for all key design choices?
- Are all strong/recent baselines included under fair settings?
- Are evaluation metrics standard and sufficient for this task?
- Are datasets/scenarios challenging enough to validate real effectiveness?
- Are comparison and ablation protocols clearly documented?
5. Method Design Soundness
- Is the experimental setting realistic for practical use?
- Does the method have hidden technical defects or unreasonable assumptions?
- Is the method robust without heavy per-case hyperparameter retuning?
- Do benefits outweigh added complexity and new limitations?
- Could reviewers reasonably argue that the net benefit is negative?
Adversarial Writing Workflow
- Read the paper as a skeptical reviewer.
- Answer every question above with explicit evidence from the paper.
- Mark each item as
pass,needs revision, orneeds new experiment. - Revise claims, writing, experiments, or method scope accordingly.
- Repeat until no major rejection risk remains.