When Correct Beliefs Collapse: Epistemic Resilience of LLMs under Clinical Pressure
For developers of LLMs in high-stakes clinical settings, this work identifies and mitigates a critical failure mode of epistemic sycophancy.
LLMs often abandon correct initial diagnoses under escalating clinical pressure despite high benchmark accuracy. The proposed Med-Stress framework reveals a dissociation between knowledge and robustness, and the R-FT method nearly eliminates belief change, substantially improving robustness.
Despite strong medical benchmark accuracy, LLMs can exhibit severe multi-turn sycophancy in clinical dialogue, abandoning initial correct diagnosis under escalating pressure. We propose \textbf{\textsc{Med-Stress}}, a targeted stress test framework that evaluates belief stability under escalating pressure. Across nine frontier large language models (LLMs), we find a clear dissociation between medical knowledge and robustness: high initial diagnostic capability does not imply high belief stability, yielding large knowledge-robustness gaps for several LLMs. To mitigate this failure mode, we propose a lightweight inference-time defense, \textbf{\texttt{RBED}} (\textbf{R}ole-\textbf{B}ased \textbf{E}pistemic \textbf{D}efense), and \textbf{\texttt{R-FT}} (\textbf{R}esilience-oriented \textbf{F}ine-\textbf{T}uning), a training-time approach that internalizes evidence-based resistance to pressure. Experiments show that \textbf{\texttt{R-FT}} nearly eliminates belief change and substantially improves robustness.