FIGS: Evaluating Multi-Turn Sycophancy Without Penalizing Empathy

Large language models frequently fail to balance staying truthful with being supportive. To appease users, they often become sycophantic, by agreeing with false claims and through subtle behaviors like unwarranted flattery and skewed advice. In reality, sycophancy rarely happens in a single exchange; it emerges organically as users repeatedly insist or subtly steer the dialogue over time. Current evaluations, however, rely on rigid, single-turn tests or fixed scripts that fail to capture these natural dynamics. Furthermore, these benchmarks often mistake showing basic empathy for yielding, penalizing models for acknowledging a user's feelings and driving them to over-correct into cold, dismissive rigidity. To address this gap, we introduce FIGS (Factual Integrity and Grounded Support), a dual-axis evaluation framework built entirely around extended, realistic dialogue. We use an adaptive 10-turn conversational simulator that dynamically challenges the target model, mimicking how human boundaries are actually tested in everyday interactions. To accurately evaluate these trajectories, we apply a taxonomy that strictly separates Sycophancy (whether the model holds firm to the truth and keeps its praise proportional) from Calibrated Validation (showing empathetic understanding of the user's feelings without overdoing it). We release our complete testing environment, including 500 diverse multi-turn scenarios and an automated judge. Our evaluation of leading models reveals a consistent trade-off: over the course of a sustained interaction, current systems either slowly drift to sycophancy or over-correct into robotic detachment. This demonstrates that balancing honesty with appropriate support throughout a natural conversation remains a critical, unsolved challenge.