让AI审稿从挑毛病升级到给改法
现在的AI审稿只会说“这里有问题”，但不说怎么改。这篇把审稿拆成两步：先诊断问题，再给出具体的修改建议。关键思路很妙——作者在回复审稿人时写的反驳，其实就是“这个问题可以怎么解决”的现成答案。研究者从OpenReview上真实的审稿-回复对话中提取了4万条数据，让AI学会把“审稿人指出的弱点”和“作者实际采取的补救动作”对应起来，再定位到论文的具体段落。训练出的模型在给出可操作的修改建议上超过了此前的专用审稿模型，和强提示词的大模型打平。但它不是明天就能用的工具：人类评估显示它在技术准确性上仍有明显差距，而且它只能基于论文已有内容提建议，不会帮你做实验补数据。它的价值在于让AI审稿从“挑刺”走向“指路”，这是审稿AI的一个真实方向。
📄 原文摘要(英文)
As LLMs are increasingly used for pre-submission self-review, there is growing demand for feedback that not only identifies weaknesses but also guides authors toward concrete revisions. We study this as Actionable Peer-review Generation and decompose it into two subtasks: diagnostic claim generation and revision suggestion generation. We introduce ActReview, a rebuttal-guided post-training framework that connects paper-specific diagnoses to concrete, grounded revision plans. Our central insight is that author rebuttals reveal plausible actions for addressing reviewer concerns and can therefore provide latent supervision for revision-oriented feedback. From real review-rebuttal threads on OpenReview, we construct ActReview-40K by aligning reviewer weaknesses with author responses and grounding the resulting feedback in localized paper evidence. We post-train Qwen3-8B-Base with multi-task supervised fine-tuning followed by GRPO using candidate-aware, weakness-specific rubric rewards. We also introduce ActReview-Bench, a human-curated benchmark of 1,000 instances for evaluating diagnostic quality and revision usefulness. Experiments show that ActReview outperforms prior specialized review-generation models on actionability and grounding while remaining competitive with strong prompt-based LLMs. Human evaluation confirms improved revision usefulness while revealing a remaining gap in technical accuracy, and additional analyses support generalization to held-out papers and robustness across independent judges.