{
  "id": 432,
  "url": "https://arxiv.org/abs/2606.30887v1",
  "title": "Training Therapeutic Judges and Multi-Agent Systems for Human-Aligned Mental Health Support",
  "summary": "Large language models show promise for mental health support, yet therapeutic quality improves only when evaluation functions as an actionable control signal rather than a passive metric. We introduce a framework that formulates therapeutic response generation as a decision-refinement problem driven by multi-dimensional, human-aligned evaluation. In Stage I, we introduce TheraJudge, an open-source therapeutic evaluator trained via preference-based optimization on human-annotated data to produce ",
  "authors": "Mizanur Rahman, Abeer Badawi, Elahe Rahimi, Laleh Seyyed-Kalantari, Frank Rudzicz, Enamul Hoque et al.",
  "category": "research",
  "topics": "healthcare,agents-autonomy",
  "orgs": null,
  "regions": null,
  "published_at": "2026-06-29T20:22:25.000Z",
  "fetched_at": "2026-07-14T14:14:32.646Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/432",
  "original_url": "https://arxiv.org/abs/2606.30887v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}