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

Research Medical/Healthcare AI

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Merged summary

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.

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

arXiv cs.AI Zongheng Guo, Tao Chen, Tianli Li, Mingzhe Cui, Yang Jiao, Lei Xie, Yi Pan, Xiao Hu, Manuela Ferrario 2026-07-30 arXiv:2607.28421
Public signals Semantic Scholar citations 0 · Semantic Scholar influential citations 0
Providers: Hugging Face · N/A OpenAlex · N/A Publisher · N/A Semantic Scholar · Citations 0 · Influential citations 0 X · N/A Fetched 2026-08-14 14:22:27.383096 UTC

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.
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