Mixture-of-Experts (MoE) models route each token to a subset of expert networks, increasing capacity while keeping per-token computation sparse. Many deployed MoEs use a fixed number of active experts across layers and tasks, despite evidence that layer roles, expert redundancy, and required capacity vary with depth and input difficulty. Existing layer-wise methods usually determine allocations offline and reuse them across tasks, while token-level methods rely on local routing signals without task-level context. We propose MetaNet, a support-set controller that predicts an expert-retention threshold and a bounded routing bias for each layer while keeping the backbone, experts, and original router frozen. On DeepSeek-MoE-16B-Chat, MetaNet provides a tunable accuracy–expert-activation trade-off. Relative to fixed k=6, a conservative setting activates 3.61 experts on average (40% fewer) with comparable MMLU accuracy (0.489 versus 0.474); an aggressive setting activates 2.28 experts on average (62% fewer) at 0.438 accuracy. Trained on MMLU, MetaNet transfers to C-Eval without retraining, activating 2.90 experts on average (52% fewer than fixed k=6) at 0.386 accuracy.

The critical therapist shortage demands scalable training solutions. Standardized Patients, the gold standard, are scarce and costly. Current LLM-based approaches focus on patient simulation for conversational realism but lack pedagogical rigor as Virtual Standardized Patients, lacking faithful reactions to clinical errors and explainable feedback. To bridge this gap, we propose PUPPET, the first neural-symbolic Virtual Standardized Patient governed by an OBSERVE-THINK-BEHAVE architecture. PUPPET externalizes LLM reasoning into a symbolic system where experts implant causal associations between intervention logic (propositional logic) and patient mental states (state machine). This allows PUPPET to behave coherently with controllable and explainable psychological dynamics: intervention logic (OBSERVE) → state transition (THINK) → response (BEHAVE). Our PUPPET-TRAINER further leverages this chain to educate trainees about intervention consequences, standardizing and scaling mental health training. Experiments across three clinical scenarios confirm that PUPPET outperforms baselines in clinical faithfulness and pedagogical value.