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MyMentorLLM: A psychotherapy GenAI environment with multimodal voice/text patients, trainees and experts for deliberate practice

arXiv 2607.25667 ↗First posted 28 Jul 2026cs.CL cs.AIcited by 0

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Abstract (arXiv)

Psychotherapists need repeated training and supervision; however, scalability is problematic. We present MyMentorLLM, a multimodal voice- and text-based deliberate-practice environment with 2,100 complete Cognitive Behavioural Therapy (CBT) sessions. Each session links a DSM-5-TR-grounded LLM patient (with major depressive, generalised anxiety or borderline personality disorder), an LLM therapist-in-training and an LLM expert supervisor (powered by Gemma-4, Gemini-3.1-Flash-Live and Qwen-3.6). Sessions were analysed for emotional dynamics, therapeutic competence and diagnostic accuracy against human psychotherapy data. Simulated patients expressed disorder-congruent emotional profiles, which therapists mirrored as in human counselling. LLM trainee competence was rated above human levels in most conditions, while native speech-to-speech was closest to human scores. Supervisor feedback improved diagnostic accuracy in 5 of 7 LLM conditions, whereas symptom identification accuracy increased with model size. This work shows deliberate practice can be simulated for CBT training, although patient fidelity, supervisor calibration and harmful feedback require evaluation via a complex systems perspective.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 13 of its 52 references that are in the map. First posted on arXiv 28 Jul 2026.

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References · 13

  1. 2023Language Models Don't Always Say What They Think: Unfaithful Explanations in Chain-of-Thought Promptingcited by 1,122
  2. 2024Large Language Model based Multi-Agents: A Survey of Progress and Challengescited by 769
  3. 2022Out of One, Many: Using Language Models to Simulate Human Samplescited by 752
  4. 2024The Instruction Hierarchy: Training LLMs to Prioritize Privileged Instructionscited by 493
  5. 2013Crowdsourcing a Word-Emotion Association Lexiconcited by 196
  6. 2024Quantifying the Persona Effect in LLM Simulationscited by 169
  7. 2024Large language models that replace human participants can harmfully misportray and flatten identity groupscited by 144
  8. 2021Speaker and Time-aware Joint Contextual Learning for Dialogue-act Classification in Counselling Conversationscited by 31
  9. 2026OmniVoice: Towards Omnilingual Zero-Shot Text-to-Speech with Diffusion Language Modelscited by 25
  10. 2021DASentimental: Detecting depression, anxiety and stress in texts via emotional recall, cognitive networks and machine learningcited by 6
  11. 2026Decomposing Factual Sycophancy in Language Models: How Size and Instruction Tuning Shape Robustnesscited by 4
  12. 2026LLMs can persuade only psychologically susceptible humans on societal issues, via trust in AI and emotional appeals, amid logical fallaciescited by 3
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  1. 2026Mapping how LLMs debate societal issues when shadowing human personality traits, sociodemographics and social media behaviorcited by 2

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