A cognitive layer architecture to support large-language model performance in psychotherapy interactions

Clinician-patient conversations form the cornerstone of mental healthcare. Large language models (LLMs) could hold promise for this domain but their effectiveness in patient-facing interactions remains largely unproven. Here we introduce a cognitive layer architecture that enhances general-purpose LLMs with specialized clinical psychotherapeutic reasoning capabilities. In a randomized, double-blind evaluation, 227 human participants generated naturalistic mental well-being session transcripts by interacting with different therapy agents. A consortium of 22 expert clinicians assessed these transcripts, finding that LLMs augmented with this architecture consistently outperformed both standalone state-of-the-art LLMs and human clinicians across key clinical competencies required for delivering high-quality cognitive-behavioral therapy. We validated these results in an analysis of 19,674 transcripts from a large-scale, real-world deployment where an LLM embedded within this cognitive layer architecture was used as part of healthcare delivery to support 8,920 users seeking mental well-being assistance. Increased cognitive layer activation was associated with greater symptom improvement and a higher likelihood of long-term clinical recovery (~10 weeks). Our findings demonstrate that a cognitive layer architecture can enable LLMs to deliver high-quality cognitive-behavioral therapy interactions, with continued research warranted into mechanisms and clinical efficacy of AI-assisted therapeutics.