From Plausibility to Permissibility: A Layered Neuro-Symbolic Runtime for Reliable and Auditable AI Interpretation

Recent generative AI systems can produce highly plausible interpretations of linguistic and operational inputs, yet plausibility alone does not guarantee execution admissibility. In real-world settings, a fluent or high-confidence output may still be semantically ill-typed, structurally inconsistent, policy-violating, or insufficiently justified for action. This paper presents a layered neuro-symbolic interpretation runtime that explicitly separates candidate generation from execution eligibility. The proposed framework combines candidate proposal, typed semantic role filtering, well-formedness constraints, policy arbitration, action gating, and audit tracing. Candidate interpretations are generated by a proposal layer, filtered by semantic and structural admissibility, and assigned one of three operational outcomes: ACCEPT, REVIEW, or REJECT. Hard constraints are applied prior to score-based ranking, preventing high-confidence but invalid candidates from being selected. On an internal calibration set (20 cases), the runtime achieved 20/20 decision-match accuracy with explicit trace outputs preserved for every decision.