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Untangling the Mechanisms of Misleading Context in Medical Question Answering

Paper recorded by Signals 4 on 2026-09-02 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-02 on arXiv · recorded by Signals 4 on 2026-09-03

Category: cs.AI · 人工智能 · first seen 2026-09-03

Abstract

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 d

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#211 most recent of 300 cs.AI papers we have recorded · ↑ newer: Measurement-Driven Sub-Network Selection for On-Premise Retrieval-Augm · ↓ older: Bilevel Coordinated Reflection: A Game-Theoretic Approach to Multi-Age
Cite this page: Untangling the Mechanisms of Misleading Context in Medical Question Answering: the #211 most recent of 300 cs.AI papers we have recorded (as of 2026-09-02). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/untangling-the-mechanisms-of-misleading-context-in-medical-question-answering.html
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