Review Response

Turns reviewer comments into structured rebuttals by parsing feedback, classifying it as major/minor/misunderstanding, then generating professional responses with the right tone. It enforces an evidence anchor rule so you can't claim "we added experiments" without naming the actual table or figure. Integrates with ml-paper-writing memory if installed to learn phrasing from successful papers. Built from ICLR spotlight analysis, so it knows to acknowledge strengths first, justify your experimental choices, and flag ethical considerations proactively. Use it when you need to respond to peer review and want to avoid the common mistakes that tank rebuttals. Includes reference docs for classification criteria and response templates.