Untangling the Mechanisms of Misleading Context in Medical Question Answering
Large language models now answer medical questions with expert-level performance. However, the context these systems act on can be misleading, and misleading context can corrupt a model's medical judgment. To understand how misleading context corrupts this judgment, we examine the model's susceptibility to the context, disclosure of it, mechanism of corrupted reasoning, and monitorability of the decision. On the medical reasoning subset of MedMisBench, a clinician-reviewed question-answering benchmark of 8,627 questions, we inject two types of misleading context cues, fabricated evidence and a bare assertion. We test three reasoning models, two that expose their full reasoning trace and o...
| Authors | Robin Linzmayer, Noémie Elhadad |
|---|---|
| Primary category | cs.CL |
| Published | 2 September 2026 |
| Last updated at source | 11 September 2026 |
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