High-confidence model outputs can be stably wrong. We present BPL/OA (Bayesian Persistent Landscapes with Operational Assurance), an auditable uncertainty-routing sidecar designed to prevent high-confidence false reassurance by separating structural confidence from semantic consistency. Instead of outputting a single confidence score, BPL/OA maps each output to an operational route—auto-accept, monitor, review, fallback, or abstain—with a structured audit record. It does not replace the underlying decision system; it functions as a sidecar QA layer that enforces a semantic guardrail. validate BPL/OA on two settings. In a WM-811K wafer defect map study (14, 665 samples), the framework achieves zero protocol-defined conflict leakage under the full routing policy. Ablation reveals 41. 3%–63. 8% overall conflict leakage and 95. 2%–100. 0% high-margin conflict leakage when any component is removed. At operating points nearest to the BPL/OA non-auto-accept burden of 67. 9%, confidence-threshold, semantic-threshold, and conformal-style baselines capture only 52. 2%, 84. 1%, and 56. 5% of protocol-defined conflicts, respectively, compared with 100. 0% for BPL/OA. a secondary V3 shadow replay study, the same routing abstraction transfers to LLM benchmark outputs and produces clear strategy-dependent route separation. A stratified feature-based consistency audit on 309 wafer rows and 98 V3 records found no protocol-level routing errors under the adapter rules.
Tao Rui (Fri,) studied this question.