A Self-Evolving LLM Agent Framework for Role-Based Norm Compliance in Healthcare

Large language models (LLMs) are increasingly proposed as conversational agents in healthcare (Mehandru et al., 2024 ), yet many existing systems treat roles as static prompts and rely on one-shot safety filters. In such designs, it can be difficult to enforce longhorizon responsibilities, stable role identity, and realistic communication behavior. We propose a Self-Evolving LLM Agent that learns from role-based social experience and explicitly models communicator-level individual traits informed by prior communication questionnaires and clinical literature. The agent integrates (i) perception and action conditioned on both hard role responsibility norms and soft trait-conditioned style preferences, (ii) structured memory storing norm-annotated trajectories and identity states, (iii) dual-layer reflection that combines short-term responsibility diagnosis with long-term identity drift detection via trait consistency and trait-norm compatibility checks, and (iv) self-evolution that updates system prompts and identity parameters through preference-style optimization with AI feedback (Fernando et al., 2023; Jiang et al., 2024) . We instantiate the framework in a multi-role healthcare sandbox and evaluate outpatient medication review, emergency triage, and discharge planning. Across our simulated tasks, self-evolution is associated with lower severity-weighted norm risk, more stable role-identity signals, and improved socialembeddedness metrics (including trust-like signals) (Saffarizadeh et al., 2024) relative to strong static baselines.