PolicyGuard: From Organizational Policies to Neuro-SymbolicCompliance Review Engines
PolicyGuard addresses a critical gap in LLM-assisted compliance workflows by making policy logic explicit and auditable. The framework converts organizational policies into formal relational rules and extraction tasks, then uses LLMs to answer targeted questions against document evidence before a symbolic evaluator applies compliance logic. This neuro-symbolic approach matters because it shifts compliance review from opaque end-to-end prompting to inspectable, testable, and updatable decision pipelines. For enterprises deploying LLMs in regulated domains, the ability to separate extraction from reasoning and maintain an audit trail of policy application could reshape how organizations validate AI-assisted document review at scale.
Modelwire context
ExplainerThe paper's deeper contribution is less about compliance specifically and more about a general template for constraining where LLMs operate in a pipeline: they handle evidence retrieval, while a deterministic layer owns the logic. That division of labor is the architectural bet worth scrutinizing.
This connects directly to the interpretability concerns raised in 'Surrogate Fidelity: When Can Open LLMs Explain Closed Ones?' from late June. That study showed prediction agreement between models masks divergence in internal reasoning, which is precisely the failure mode PolicyGuard's symbolic layer is designed to contain. If you cannot trust that an LLM's correct output reflects correct reasoning, then letting LLMs own compliance logic end-to-end is structurally risky regardless of accuracy metrics. PolicyGuard's approach of restricting LLMs to extraction tasks and handing logic to an auditable evaluator is a practical response to that same trust gap, applied in a regulated-document context rather than a model-analysis context.
Watch whether any enterprise compliance vendors (Thomson Reuters, Wolters Kluwer) publish benchmarks or pilot results against neuro-symbolic pipelines like PolicyGuard within the next two quarters. Adoption signals from that tier would confirm the architecture is production-viable, not just academically tidy.
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MentionsPolicyGuard · LLM
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