AGENTIC-SYSTEMSMay 26, 20261 min read10 views

When Correct Beliefs Collapse: Epistemic Resilience of LLMs under Clinical Pressure

Despite high medical benchmark accuracy, large language models (LLMs) easily abandon correct diagnoses under clinical pressure. Researchers propose new defense and fine-tuning methods to enhance the epistemic resilience of AI in healthcare.

Computer Science > Artificial Intelligence

Title:When Correct Beliefs Collapse: Epistemic Resilience of LLMs under Clinical Pressure

View PDF HTML (experimental)Abstract: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.

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