When Derived Measurements Mislead: Quantifying and Mitigating LLM Over-Trust with Privileged-Modality Reliability Evidence

TL;DR - This paper formalizes derived-feature over-trust, where LLMs treat uncertain sensor-derived measurements as direct facts, and proposes metrics and reliability evidence to evaluate and mitigate it in physiological sensing.

• Tests over-trust using PPG-derived heart rhythms checked against privileged offline ECG references never shown to the LLM.
• Introduces five metrics covering conflicting evidence, context-induced errors, error repair, evidence specificity, and unnecessary verification.
• Evaluates privileged ECG-to-PPG distillation on 50,000 paired records and a protocol-locked 187-patient test set.
• The baseline improved four repair and specificity endpoints by 1.82–6.69 percentage points; verification-related harm rose by 0.67 points, with its confidence interval spanning zero.