AI RESEARCH
RbtAct: Rebuttal as Supervision for Actionable Review Feedback Generation
arXiv CS.AI
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ArXi:2603.09723v1 Announce Type: cross Large language models (LLMs) are increasingly used across the scientific workflow, including to draft peer-review reports. However, many AI-generated reviews are superficial and insufficiently actionable, leaving authors without concrete, implementable guidance and motivating the gap this work addresses. We propose RbtAct, which targets actionable review feedback generation and places existing peer review rebuttal at the center of learning. Rebuttals show which reviewer comments led to concrete revisions or specific plans, and which were only defended.