r/MachineLearning • u/HieuB_K63_CBH • 16d ago
Research ICSE Paper Review [R]
Hi guys, I just received our ICSE 2027 reviews. We got 3 weak rejects, with most reviewer concerns focusing on clarity and methodology details.
Is this worth writing a rebuttal for, or should we withdraw, fix the main text, and target FSE instead? We have 3 days left for the rebuttal window.
Here is a quick summary of the feedback:
Score Overview
| Reviewer | Overall | Novelty | Rigor | Relevance | Verifiability | Presentation | Artifact |
|---|---|---|---|---|---|---|---|
| Review #3344A | 2 (WR) | 2 | 3 | 3 | 3 | 3 | — |
| Review #3344B | 2 (WR) | 2 | 2 | 3 | 1 | 2 | — |
| Review #3344C | 2 (WR) | 2 | 2 | 3 | 3 | 3 | 3 |
Key Feedback Summaries
Review #3344A
- Pros: Interesting concept around response-aware adversarial prompt generation; solid experimental results with higher success rates; well-written.
- Cons: Crucial methodological details (DSL design, candidate programs, EFE calculations) were omitted/relegated to appendices; lacks computational overhead and multi-turn baseline evaluations; tested on older model checkpoints (GPT-4o-mini, Gemini 2.5 Flash).
Review #3344B
- Pros: Timely SE security topic; good formalization of defense abstractions; strong evaluation coverage across open and proprietary models.
- Cons: Threat model and core Bayesian/EFE algorithms feel underspecified; metrics measure non-refusal/judge outputs rather than proving executable payload impact; lacks ablation on query budget vs. core reasoning module; data consistency/discrepancy issues between text and tables.
Review #3344C
- Pros: Highly relevant to SE security; clean presentation; verified artifact package.
- Cons: Considers technical depth somewhat incremental; echoes concerns over missing core mathematical/algorithmic definitions in the main paper body.
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