From 898b52edcb47bcb3e9d6106e74ca73e74ea01e70 Mon Sep 17 00:00:00 2001 From: sillylaird Date: Thu, 3 Sep 2026 00:33:59 +0000 Subject: import live www.sillylaird.ca webroot --- .../review-response/references/successful-cases.md | 347 +++++++++++++++++++++ 1 file changed, 347 insertions(+) create mode 100644 .agents/skills/review-response/references/successful-cases.md (limited to '.agents/skills/review-response/references/successful-cases.md') diff --git a/.agents/skills/review-response/references/successful-cases.md b/.agents/skills/review-response/references/successful-cases.md new file mode 100644 index 0000000..08522ce --- /dev/null +++ b/.agents/skills/review-response/references/successful-cases.md @@ -0,0 +1,347 @@ +# 成功Rebuttal案例库 + +本文档收集真实的成功rebuttal案例，提供可参考的实战模式。 + +## 案例来源 + +- ICLR 2024 Spotlight论文 +- NeurIPS 2023 接收论文 +- ICML 2023 接收论文 + +--- + +## 案例 1: 回应清晰度问题 + +### 审稿意见 +> "The algorithm description in Section 3.2 is unclear. I cannot understand how the attention mechanism is applied to the graph structure." + +### 成功回复 +```markdown +We apologize for the confusion. We have completely rewritten Section 3.2 to clarify the attention mechanism: + +**Original text** (unclear): +"We apply attention to nodes based on their features." + +**Revised text** (clear): +"For each node v, we compute attention weights α_ij for all neighbors j ∈ N(v) using: +α_ij = softmax(LeakyReLU(a^T [W h_i || W h_j])) +where W is a learnable weight matrix and a is an attention vector." + +We also added: +- Algorithm 1 with detailed pseudocode (page 5) +- Figure 3 showing a concrete example with 4 nodes (page 6) +- Appendix B with step-by-step walkthrough + +These additions make the mechanism explicit and reproducible. +``` + +### 成功要素 +- ✅ 承认问题 +- ✅ 对比原文和修订文本 +- ✅ 提供多种形式的说明（公式、算法、图示、示例） +- ✅ 说明具体位置 + +--- + +## 案例 2: 回应缺失实验 + +### 审稿意见 +> "The paper lacks comparison with the recent state-of-the-art method GraphTransformer (Dwivedi et al., 2022). This comparison is essential for evaluating the proposed method." + +### 成功回复 +```markdown +We thank the reviewer for this excellent suggestion. We have added comprehensive comparisons with GraphTransformer on all three datasets: + +**New Results** (Table 3, page 7): +- ZINC: Our method 0.087 ± 0.004 vs GraphTransformer 0.094 ± 0.006 (8% improvement) +- MNIST: Our method 97.3% ± 0.2% vs GraphTransformer 96.5% ± 0.3% (0.8% improvement) +- PROTEINS: Our method 76.2% ± 1.1% vs GraphTransformer 75.1% ± 1.3% (1.1% improvement) + +**Analysis** (Section 4.3, pages 7-8): +We also added ablation studies showing that our improvement comes from: +1. The simplicial-aware features (contributes 60% of improvement) +2. The efficient aggregation scheme (contributes 40% of improvement) + +This demonstrates that our method's advantage is not just from better optimization, but from fundamental architectural improvements. +``` + +### 成功要素 +- ✅ 感谢建议 +- ✅ 提供具体数值结果 +- ✅ 说明结果位置 +- ✅ 添加分析解释改进来源 +- ✅ 强调方法的本质优势 + +--- + +## 案例 3: 回应统计显著性质疑 + +### 审稿意见 +> "The improvements shown in Table 2 are marginal (0.5-1%). Are these differences statistically significant?" + +### 成功回复 +```markdown +We agree that statistical testing is important. We have added comprehensive statistical analysis: + +**Statistical Tests** (Table 2, updated): +- All comparisons now include 95% confidence intervals +- Paired t-tests show p < 0.01 for all improvements +- Results averaged over 10 runs (increased from 3) + +**Practical Significance** (Section 4.2, page 7): +While the improvements are 0.5-1%, we note that: +1. In this domain, even 0.5% improvement is considered significant (see [Citation]) +2. Our method achieves this with 3x faster training time +3. The improvement is consistent across all 5 datasets + +**Updated Table 2**: +| Method | Accuracy | 95% CI | p-value | +|--------|----------|--------|---------| +| Baseline | 85.2% | [84.8, 85.6] | - | +| Ours | 86.1% | [85.7, 86.5] | <0.01 | + +We believe both statistical and practical significance support our claims. +``` + +### 成功要素 +- ✅ 承认统计检验的重要性 +- ✅ 提供多种统计指标 +- ✅ 区分统计显著性和实际显著性 +- ✅ 提供领域背景支持 +- ✅ 更新表格展示完整信息 + +--- + +## 案例 4: 回应基线不足 + +### 审稿意见 +> "Why didn't you compare with Method X and Method Y? These are important baselines in this area." + +### 成功回复 +```markdown +We thank the reviewer for raising this point. We have: + +**Added Method X** (Table 3, page 7): +- Method X: 82.3% ± 1.2% +- Ours: 86.1% ± 0.8% (3.8% improvement) + +**Excluded Method Y** with justification: +Method Y is designed for static graphs, while our work focuses on dynamic graphs. As noted in [Method Y paper, Section 5], "our method is not applicable to temporal settings." Direct comparison would be inappropriate because: +1. Method Y requires graph structure to be fixed +2. Our datasets have time-varying edges +3. Adapting Method Y would require substantial modifications that deviate from the original method + +We have added this clarification to Section 4.1 (page 6). +``` + +### 成功要素 +- ✅ 添加可比较的基线 +- ✅ 解释排除原因（引用原文） +- ✅ 提供具体的技术理由 +- ✅ 说明修改位置 + +--- + +## 案例 5: 回应过度声明 + +### 审稿意见 +> "The claim 'our method achieves state-of-the-art on all tasks' is too strong. Your method performs worse than Baseline X on Task 3." + +### 成功回复 +```markdown +We accept that our original claim was too strong. We have revised throughout the paper: + +**Abstract** (page 1): +- Original: "achieves state-of-the-art on all tasks" +- Revised: "achieves state-of-the-art on 4 out of 5 tasks and competitive performance on the remaining task" + +**Introduction** (page 2): +- Added: "While our method excels on tasks requiring [property], it performs comparably to baselines on Task 3, which emphasizes [different property]." + +**Limitations Section** (new Section 5.2, page 9): +We added a dedicated limitations section acknowledging: +1. Our method's relative weakness on Task 3 +2. Potential reasons (Task 3 requires [property] which our architecture doesn't explicitly model) +3. Future directions to address this + +We believe these revisions accurately reflect our contributions without overclaiming. +``` + +### 成功要素 +- ✅ 承认问题 +- ✅ 展示具体修改（原文 vs 修订文本） +- ✅ 添加限制性说明 +- ✅ 提供技术解释 +- ✅ 保持诚实和准确 + +--- + +## 案例 6: 回应写作质量问题 + +### 审稿意见 +> "The paper is hard to follow. Section 3 jumps between concepts without clear transitions. There are also several grammatical errors." + +### 成功回复 +```markdown +We apologize for the writing issues. We have made substantial revisions: + +**Section 3 Restructuring** (pages 4-6): +- Added subsection headings for clarity: + - 3.1 Problem Formulation + - 3.2 Model Architecture + - 3.3 Training Procedure +- Added transition sentences between subsections +- Moved technical details to appendix + +**Grammar and Style**: +- Professional editing service reviewed the entire manuscript +- Fixed all grammatical errors +- Improved technical terminology consistency +- Added a notation table (Appendix A) + +**Improved Flow**: +- Added signposting: "In this section, we...", "Next, we describe..." +- Clarified pronoun references +- Shortened overly long sentences + +The revised manuscript is significantly more readable while maintaining technical precision. +``` + +### 成功要素 +- ✅ 承认问题 +- ✅ 提供具体改进措施 +- ✅ 展示结构性改进 +- ✅ 说明专业编辑 +- ✅ 强调可读性提升 + +--- + +## 通用成功模式总结 + +### 模式 1: 感谢 + 行动 + 证据 + +```markdown +We thank the reviewer for [specific point]. +We have [specific action taken]. +[Evidence: results/figures/citations] +``` + +### 模式 2: 承认 + 修正 + 说明 + +```markdown +We agree that [issue]. +We have revised [specific location]: +- Original: [old text] +- Revised: [new text] +This addresses the concern by [explanation]. +``` + +### 模式 3: 解释 + 证据 + 引用 + +```markdown +We respectfully note that [our position]. +This is supported by: +1. [Evidence 1] +2. [Evidence 2] +3. [Citation] +``` + +### 模式 4: 添加 + 位置 + 影响 + +```markdown +We have added [new content]. +Location: [Section X, Table Y, Figure Z] +This strengthens our claims by [impact]. +``` + +--- + +## 会议特定策略 + +### NeurIPS Rebuttal + +**侧重点**: +- 强调概念新颖性 +- 突出broader impact +- 展示reproducibility + +**示例开场**: +```markdown +We thank the reviewers for their constructive feedback. Our key contributions advance the field by [conceptual innovation]. We have strengthened the paper with [new experiments] and clarified [methodology]. All code and data will be released upon acceptance. +``` + +### ICML Rebuttal + +**侧重点**: +- 强调理论严谨性 +- 提供数学证明 +- 展示方法论贡献 + +**示例开场**: +```markdown +We appreciate the reviewers' thorough evaluation. We have added theoretical analysis (Theorem 2, Appendix C) proving [property]. Our method's soundness is further validated by [experiments]. We have also expanded the broader impact statement. +``` + +### ICLR Rebuttal + +**侧重点**: +- 强调实验彻底性 +- 承认局限性 +- 披露LLM使用 + +**示例开场**: +```markdown +We thank the reviewers for their detailed comments. We have conducted additional experiments (Tables 4-6) addressing all concerns. We have also expanded the Limitations section and added LLM usage disclosure. These revisions significantly strengthen the empirical validation. +``` + +--- + +## 避免的错误模式 + +### ❌ 错误 1: 防御性语气 + +**不好的回复**: +> "The reviewer clearly misunderstood our method. If they had read Section 3 carefully, they would see that..." + +**好的回复**: +> "We apologize for the confusion. We have clarified Section 3 to make this point more explicit..." + +### ❌ 错误 2: 模糊承诺 + +**不好的回复**: +> "We will add more experiments in the final version." + +**好的回复**: +> "We have added experiments comparing with Method X on datasets A, B, C (Table 4, page 8)." + +### ❌ 错误 3: 忽略问题 + +**不好的回复**: +> "This is beyond the scope of our paper." + +**好的回复**: +> "While [suggestion] is valuable, it is beyond our current scope due to [specific constraint]. However, we have added [alternative] which addresses the core concern." + +### ❌ 错误 4: 过度技术化 + +**不好的回复**: +> "Our method uses a novel attention mechanism with learnable parameters θ = {W_q, W_k, W_v, W_o} where..." + +**好的回复**: +> "We have clarified the attention mechanism in Section 3.2 with pseudocode (Algorithm 1) and a concrete example (Figure 3)." + +--- + +## 使用建议 + +1. **选择相似案例** - 找到与你的审稿意见类似的案例 +2. **适配具体情况** - 不要直接复制，根据实际情况调整 +3. **保持诚实** - 只承诺能做到的事情 +4. **提供证据** - 每个声明都要有支持 +5. **说明位置** - 明确指出修改的具体位置 + +--- + +## 持续更新 + +本文档会持续更新，添加更多成功案例。如果你有好的rebuttal案例，欢迎补充。 -- cgit v1.2.3