用LLM诊断+规则控制分离，让养老政策既智能又可审计
Separating Diagnosis from Control: Auditable Policy Adaptation in Agent-Based Simulations with LLM-Based Diagnostics
- LLM只负责评估风险，不直接发指令
- 规则引擎根据评估结果调整参数，提升11.7%效果
- 适合需透明决策的医疗/养老政策系统
缓解老年人孤独感需要兼具适应性与可审计性的政策干预。现有方法难以兼顾：传统基于代理的模型僵化死板，而直接使用大语言模型（LLM）控制器则缺乏可追溯性。本文提出三层框架，将诊断与控制分离以同时实现两者。LLM仅作为诊断工具，评估群体状态并生成结构化风险评估；确定性公式结合显式边界，将评估结果转化为可追踪的参数更新。该分离机制确保每项政策决策均可归因于可检查规则，同时保持对突发需求的自适应响应。我们在老年护理模拟中通过五种实验条件进行系统消融验证。结果显示，显式控制规则比端到端黑箱LLM方法提升11.7%，且完全保持可审计性，证实透明性无需牺牲自适应性能。
原文摘要 · Abstract (English)
Mitigating elderly loneliness requires policy interventions that achieve both adaptability and auditability. Existing methods struggle to reconcile these objectives: traditional agent-based models suffer from static rigidity, while direct large language model (LLM) controllers lack essential traceability. This work proposes a three-layer framework that separates diagnosis from control to achieve both properties simultaneously. LLMs operate strictly as diagnostic instruments that assess population state and generate structured risk evaluations, while deterministic formulas with explicit bounds translate these assessments into traceable parameter updates. This separation ensures that every policy decision can be attributed to inspectable rules while maintaining adaptive response to emergent needs. We validate the framework through systematic ablation across five experimental conditions in elderly care simulation. Results demonstrate that explicit control rules outperform end-to-end black-box LLM approaches by 11.7\% while preserving full auditability, confirming that transparency need not compromise adaptive performance.
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