MoSE-Counseling: A Mixture of Safety Experts Framework for Large Language Model-Based Psychological Counseling

Large language models (LLMs) create new opportunities for scalable psychological counseling, yet general-purpose LLMs lack specialized safety mechanisms: they frequently miss implicit crisis signals such as suicidal ideation and respond with trivializing or boundary-crossing replies, undermining reliability in high-stakes settings. We propose MoSECounseling, a two-stage Mixture of Safety Experts framework. Stage 1 builds a structured, evidence-grounded client profile (demographics and four severity-rated psychological dimensions) using a constrained prompting scheme designed to limit hallucination. Stage 2 routes the dialogue through specialized LoRA experts—risk detection, ethical guidance, safe response, and a coordination expert—using a profileconditioned, risk-monotone router with a profile-independent safety floor. The floor prevents an imperfect profile from overriding a crisis signal that has already been detected, but does not eliminate screening false negatives. Across SOS-HL-1K, PsyQA, and $D^4$ , MoSE-Counseling achieves consistent, statistically significant improvements in safety and professionalism compared to strong fine-tuned and domain-specific baselines. We deliberately frame these as benchmark improvements rather than evidence of clinical equivalence: our results do not establish real-world safety for crisis intervention, and high-risk detection remains imperfect. We therefore report controlled baselines that isolate the design’s contribution from prompting and fine-tuning, a safety-specific evaluation of missed crises and harmful responses, expanded ablations and robustness analysis, a detailed professional-evaluation protocol, and an ethics discussion. MoSE-Counseling is intended as a supplementary screening and drafting aid under human oversight, not a replacement for licensed professionals.