让大模型在受监管流程中自动合规，用结构化规则构建安全决策框架。
Neuro-Symbolic Agents for Regulated Process Automation: Challenges and Research Agenda
- 将法规、流程模型等符号规则融入智能体架构，作为核心决策基础。
- 提出'合规即构建'新范式，从源头防止流程违规，比事后监控更有效。
- 面向医疗、金融等高风险领域，适合关注可信AI与自动化合规的研究者。
基于大语言模型的智能体正进入需要严格监管的行业，自动化高判断性质量管理工作。我们认为，这些领域中已存在的符号结构——包括法规、类型化流程模型和合规约束——不应仅作为外部监控工具，而应作为塑造智能体决策与行为的核心架构组件。我们提出“合规即构建”作为对“护栏式监控”的补充范式：通过结构性设计预防控制流违规，而护栏仍用于捕捉语义错误。我们系统梳理了神经符号研究在基础与能力层面的一系列挑战，并表明联合解决这些问题可实现真正的合规即构建。我们呼吁神经符号社区关注受监管流程自动化这一高影响力研究方向。
原文摘要 · Abstract (English)
LLM-based agents are entering regulated industries where they automate judgment intensive quality management processes. We argue that symbolic structures already embedded in these domains, including regulations, typed process models, and compliance constraints, should be treated not merely as external monitoring mechanisms but as core architectural components that shape the agent's decision-making and behavior. We propose compliance-by-construction as a complementary paradigm to guardrail-based monitoring: a structural foundation that prevents control-flow violations, while guardrails remain essential for catching semantic errors. We identify a structured set of neuro-symbolic research challenges on foundational and capability level and show that addressing them jointly enables compliance-by-construction. We call on the neuro-symbolic community to engage with regulated process automation as a high impact research domain.
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