arXiv:2606.03132v1 Announce Type: new Abstract: Large language models (LLMs) have shown growing potential for Cognitive Behavioral Therapy (CBT) counseling. However, most existing approaches still formulate counseling as a local response generation problem, focusing on empathetic replies within short, text-only, or single-session interactions. We argue that this formulation fundamentally mismatches the nature of real psychotherapy. In clinical CBT, therapy is a longitudinal process in which therapists continuously infer, update, and intervene on evolving therapeutic states across sessions. Rea
The increasing sophistication of large language models is enabling more complex applications beyond simple response generation, pushing the boundary of AI in sensitive domains like therapy.
This development indicates a fundamental shift in how AI can be applied to mental health, moving towards more integrated and longitudinally effective therapeutic interventions.
AI-powered therapeutic tools are evolving from single-session chatbots to systems capable of modeling and adapting to a patient's therapeutic state over extended periods.
- · AI developers
- · Mental healthcare providers
- · Patients seeking therapy
- · Digital health platforms
- · Traditional therapy models
AI becomes a more integrated and effective component of long-term mental health treatment.
Increased access to personalized and continuous mental health support, potentially reducing the burden on human therapists.
Ethical and regulatory frameworks will need to rapidly evolve to govern AI's role in therapeutic decision-making and patient data."longitudinal therapeutic state.".
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