AI RESEARCH
RuleEdit: Failure-Guided Human-AI Model Editing with Prospective Impact Preview
arXiv CS.AI
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ArXi:2606.00011v1 Announce Type: cross Despite the promise of AI to assist complex decisions, practitioners still lack ways to detect likely failures and inspect the consequences of model edits before committing them. We present RuleEdit, an interactive, rule-guided human-AI model editing system that (i) surfaces likely failures through interpretable mismatch signals from rule tables and (ii) s user-authored rule feedback with prospective previews of projected performance changes and embedding shifts.