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Surfacing Suicidal Risk Through Simulated Social Interaction: Per-Person Language Model Agents as Communicative
Medrxiv : the Preprint Server for Health Sciences
|June 12, 2026
Summary
Suicidal risk signals in digital text can be detected by training AI language models on individual communication. Simulated social interactions reveal these latent signals, aiding in suicide risk assessment.
Area of Science:
- Artificial Intelligence
- Computational Psychiatry
- Digital Phenotyping
Background:
- Suicidal risk is challenging to detect in routine digital communication.
- Latent signals of suicidal ideation may exist within individuals' authored text.
- Existing digital phenotyping methods may not capture subtle communication patterns.
Purpose of the Study:
- To develop and validate a method for surfacing latent suicidal risk signals in digital communication.
- To assess the efficacy of personalized language model agents in simulated social interactions.
- To bridge generative AI, digital phenotyping, and suicide theory.
Main Methods:
- Trained personalized language model agents (LoRA adapters on Qwen3-8B) on authored text from 79 adults with recent suicidal ideation.
- Placed agents in simulated social interactions (communicative stress tests) with probe personas.
- Correlated agent-generated risk language with Ecological Momentary Assessment (EMA)-measured suicidal ideation.
Main Results:
- Agent-generated risk language significantly correlated with EMA-measured suicidal ideation (r = .576, p < .001).
- A single neutral small-talk probe showed strong performance (r = .551).
- The signal was confirmed as person-specific, unlike general smartphone activity data.
Conclusions:
- Simulated social interaction can amplify latent vulnerability signals in digital communication.
- This approach offers a novel method for suicide risk detection by combining generative AI and digital phenotyping.
- The findings support the potential of AI-driven communicative stress tests for mental health assessment.
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