"Feedback-to-Rubrics: Can We Extract Expert Criteria from Inline Comments?" will be presented at the Workshop on Human-AI Co-Creativity: Advances, Opportunities, and Challenges on July 11 at #ICML2026 Paper: arxiv.org/abs/2605.29857 (Extended full-paper version) LLMs are increasingly used for writing and review support, but their usefulness depends on context-dependent criteria, such as expert preferences or organization-specific conventions, that are often tacit, undocumented, and difficult to elicit directly. We propose a problem setting for learning reusable natural-language rubrics from accumulated inline comments on artifacts such as human-written or LLM-generated drafts.

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