The Daopology Engine: A Proposal for a Correctable Structural Intelligence Architecture in Data Governance

Contemporary data governance faces a structural dilemma: traditional ETL pipelines are precise but prohibitively expensive to maintain for the explosive growth of unstructured data, while pure Large Language Model (LLM) approaches, despite their semantic flexibility, suffer from a "fluent yet operationally invalid" failure mode—generated mappings read correctly but break the moment data flows through them. Recent neuro-symbolic methods have significantly improved mapping accuracy under constraint guidance (F1 increasing from 0.08 to 0.66), yet their constraints remain at the level of hypothesis-space operators, lacking the capacity to audit and locally revise post-generation states. This paper proposes the Daopology Engine—a four-layer neuro-symbolic architecture based on Correctable Structural Intelligence (CSI): LLMs extract facts, discrete structural search retrieves candidate kernels, a symbolic system executes hard constraints, and the CSI layer performs local graph rewriting rather than full recomputation when a state-change intent is detected. We employ school safety assessment as a minimal qualitative validation ground, demonstrating the feasibility of the architecture across three behaviors: zero-hallucination interception, state transition, and multi-role payload distribution. We position this work as an architectural proposal: the next competitive frontier in data governance lies not in larger generative models, but in post-generation structural audit and correctability.