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

arXiv:2605.23932v1 Announce Type: new 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 extbf{extsc{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, extbf{exttt{RBED}} (extbf{R}ole-extbf{B}ased extbf{E}pistemic extbf{D}efense), and extbf{exttt{R-FT}} (extbf{R}esilience-oriented extbf{F}ine-extbf{T}uning), a training-time approach that internalizes evidence-based resistance to pressure. Experiments show that extbf{exttt{R-FT}} nearly eliminates belief change and substantially improves robustness.

Source: arXiv cs.AI | 2026-05-26