{
  "annotation_id": "1a63861e-3603-4631-9c61-42a35b0b171b",
  "date_created": "2026-02-17T05:53:24.221000Z",
  "date_modified": "2026-02-17T05:53:24.221000Z",
  "file_hash": "30df2984bbc3c33964549f8a723826618d6a548ff99dc300a4d9e1ae44e22078",
  "private": false,
  "record": {
    "abstract": "Access to mental healthcare is increasingly strained by workforce shortages and rising demand, motivating the development of intelligent systems that can support mental healthcare experts. We introduce coTherapist, a unified framework utilizing a small language model to emulate core therapeutic competencies through domain-specific fine-tuning, retrieval augmentation, and agentic reasoning. Evaluation on clinical queries demonstrates that coTherapist generates more relevant and clinically grounded responses than contemporary baselines. Using our novel T-BARS rubric and psychometric profiling, we confirm coTherapist exhibits high empathy and therapist-consistent personality traits. Furthermore, human evaluation by domain experts validates that coTherapist delivers accurate, trustworthy, and safe responses. coTherapist was deployed and tested by clinical experts. Collectively, these findings demonstrate that small models can be engineered to exhibit expert-like behavior, offering a scalable pathway for digital mental health tools.",
    "arxiv_id": "2601.10246",
    "authors": [
      "Prottay Kumar Adhikary",
      "Reena Rawat",
      "Tanmoy Chakraborty"
    ],
    "categories": [
      "cs.CL"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "title": "coTherapist: A Behavior-Aligned Small Language Model to Support Mental Healthcare Experts",
    "url": "https://arxiv.org/abs/2601.10246",
    "version": "v2"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "99624458-4646-43b5-a11d-e23fd4afcb5f",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}