Native unknown Outputs as a Scheduling Signal for Model Arrays: Small-Model Gatekeeping, Queryable Knowledge Maps, and the Failure Boundary of Same-Family Weighted Voting (English v1.0)

Companion short paper to "A 774M-Parameter Model Trained from Scratch Says 'I Don't Know'" (10.5281/zenodo.22191510). Moving the uncertainty signal from post-hoc estimation to generation time: unknown as a first-class output category becomes a metadata bit that a model array amplifies into three system capabilities — gatekeeping (37× leakage suppression; out-of-domain perception holds on all 55 concepts of a three-gradient probe set, with root shortcuts inert outside the boundary band), a queryable knowledge map (cross-seed consistency 0.946, disagreement self-localizing to the known/unknown critical band), and union-intersection two-expert routing (3,000-step unit expansion, zero forgetting). A clean negative result bounds the signal's value: within a same-family array, equal/quality/oracle weighting and two further fusion operations all degenerate to identical decisions (0.976) — overriding power requires out-of-array information sources. A six-route industry survey (MoE / cascades / ensembles / guardmodels / uncertainty estimation / industrial self-routing) finds no counterpart for the native-metadata-bit + small-model-gatekeeper + state-judgment-array combination; neuroscience offers functional-level precedents (single-cell confidence coding; dopamine pause signals). All 28 references verified one-by-one.