Paper提出PolicyGuard，能把公司政策转成可执行的规则，用LLM和符号逻辑做合规审查，比纯端到端更透明、好维护。
PolicyGuard是一个神经符号框架，用于将组织政策转化为可执行的审查引擎。它使用类型化的关系逻辑规则和原子级提取问题，在NDA合规审查中通过LLM回答局部问题并检测不合规。该框架在NDA合规审查任务上进行了实例化与评估，使文档审查过程更明确、可维护且可系统测试。
PolicyGuard: From Organizational Policies to Neuro-SymbolicCompliance Review Engines
Policy-grounded document review requires determining whether a target document complies with organization-specific policies, guidelines, or playbooks. While large language models can assist with policy interpretation and document analysis, end-to-end prompting leaves the applied policy logic implicit, making compliance decisions difficult to inspect, update, and test. We present PolicyGuard, a neuro-symbolic framework for policy-grounded document compliance review. PolicyGuard converts organizational policy guidance into an executable review engine consisting of typed relational logic rules and atom-level extraction questions. During review, LLMs answer these local questions using retrieved document evidence, and a symbolic evaluator applies the formal rules to detect non-compliance. We instantiate and evaluate PolicyGuard on company-specific NDA compliance review, where contract clauses must be checked against organization-specific negotiation policies. By separating policy formalization, local document interpretation, and symbolic compliance evaluation, PolicyGuard makes document review more explicit, maintainable, and systematically testable.