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The paper introduces a behavioral alignment framework for personalized LLM judges in recommendation evaluation, addressing bidirectional rationalization where off-the-shelf LLMs argue both for and against user engagement on the same item. Their fine-tuned and preference-optimized approach achieves a 32.19% Macro-F1 lift over zero-shot and matches production feature-engineered baselines.