Automated Detection and Classification of Delusion-related Content in Naturalistic Audio Diaries Using Multi-Agent Language Models
A new study presents an automated pipeline using multi-agent language models to detect and classify delusion-related content in audio diaries. The research demonstrates that detailed diagnostic prompts can reduce false positives in delusional theme classification. The findings suggest that majority voting among agents improves accuracy in identifying clinically ambiguous text.
- ▪The study focuses on analyzing speech monologues recorded in naturalistic settings to characterize mental illness phenomenology.
- ▪An ensemble of three foundation models was evaluated, showing that complex conversational debate can diminish accuracy.
- ▪The automated pipeline achieved a Micro F1 score of 0.872 for delusion detection and 0.779 for classification.
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Computer Science > Artificial Intelligence arXiv:2605.24755 (cs) [Submitted on 23 May 2026] Title:Automated Detection and Classification of Delusion-related Content in Naturalistic Audio Diaries Using Multi-Agent Language Models Authors:Feng Chen, Justin Tauscher, Changye Li, Meliha Yetisgen, Alex Cohen, Adam Kuczynski, Angelina Pei-Tzu Tsai, Benjamin Buck, Dror Ben-Zeev, Trevor Cohen View a PDF of the paper titled Automated Detection and Classification of Delusion-related Content in Naturalistic Audio Diaries Using Multi-Agent Language Models, by Feng Chen and 9 other authors View PDF HTML (experimental) Abstract:Speech monologues recorded in naturalistic settings provide opportunities to characterize mental illness phenomenology and detect symptom exacerbation.
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