Large language models often exhibit sycophancy, revising their answers to align with users when users push back. Such answer flips, however, can arise from different causes. One possibility is that the model simply aligns with the user’s feedback in order to satisfy them. Another is that the feedback genuinely contains useful evidence, prompting the model to update its answer in a rational way. We distinguish them as *Unsupported-Yielding* and *Rational-Updating*. Prior work focuses primarily on suppressing *Unsupported-Yielding*, while overlooking its effect on *Rational-Updating*. We address this gap with a two-turn evaluation framework that measures the two behaviors separately. Across representative training-time and inference-time interventions, we find that anti-sycophancy methods often encounter a trade-off in which reducing *Unsupported-Yielding* can sacrifice *Rational-Updating*, and vice versa, even when the two objectives are optimized jointly. Mechanistic analysis suggests that the two behaviors share an internal substrate: the MLP neurons and attention heads driving them overlap substantially, and their associated steering directions are positively aligned. We further conduct a preliminary orthogonalized steering exploration, which yields modest, backbone-dependent selectivity gains. Overall, our results suggest that anti-sycophancy should be treated not as a simple suppression problem, but as a selectivity problem, where effective interventions should preserve *Rational-Updating* while reducing *Unsupported-Yielding*.

Large language model (LLM)-based judges are increasingly used to scale the evaluation and optimization of open-ended medical dialogue systems, making their reliability critical in this high-stakes setting. However, existing medical benchmarks mainly assess their medical knowledge, while general judge benchmarks lack clinically grounded consultation criteria. To address this gap, we introduce Med-GRADE, a benchmark for evaluating LLMs as medical dialogue judges. It contains 2,076 real-world patient–LLM dialogues annotated by 56 physicians with a 23-item rubric adapted from Mini-CEX, covering Medical Interviewing (MI), Humanistic Care (HC), and Diagnosis and Treatment Management (DTM). Experiments on 17 LLMs show that current LLM judges achieve only moderate agreement, with the best model reaching 64.89% Macro-F1 and 68.28% Hamming Accuracy. Besides, more complex prompts may even harm performance: CoT disrupts parsing, while few-shot and self-refine introduce item-level instability. Data and code are available at https://github.com/jyd777/Med-GRADE for reproducibility.