Large language models increasingly serve as both decision-makers and evaluators in alignment pipelines such as reinforcement learning from human feedback (RLHF) and constitutional AI. While automated judging enables rapid iteration, recent evidence from Anthropic’s stress-testing work demonstrates that frontier-model evaluators disagree roughly 30% of the time when scoring moral trade-offs, raising questions about what these systems actually reward. This preprint investigates the mechanisms driving evaluator disagreement using a controlled Phase 1 dataset of 1,500 moral-dilemma responses generated by five local instruction-tuned models and evaluated by two LLM judges. Our primary finding is a balance penalty: responses that acknowledge competing values receive substantially lower alignment scores than responses that commit to a single value, even when topicality and temperature are held constant. We quantify this effect, analyze its interaction with framing and sampling temperature, and discuss implications for model evaluation and training.