§02e Jupiter — AIGP & Pre-Invocation — Pre-invocation signals and the divergence signal
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6. Pre-invocation signals and the divergence signal
A pre-invocation signal from a source whose witness class is W1 or W2, whose output vocabulary is deterministic (tri-state or discrete), and whose order-typing covers one or more orders also covered by probabilistic battery methods is a natural participant in the cross-method corroboration mechanism (§02a §4).
Cross-phase corroboration. Where a deterministic pre-invocation signal and a probabilistic battery method both evaluate the same conformance question — the same order-typed input elements under the same governing-specification predicates — their agreement or disagreement is a cross-phase corroboration signal. This is a species of the cross-method divergence signal (§02a §4) operating across the pre-invocation / post-invocation boundary rather than within the post-invocation battery alone.
Cross-phase corroboration is recorded in the analysis battery record as a named signal type: pre-invocation-cross-phase. Its structure is identical to the within-battery divergence signal: the deterministic pre-invocation verdict and the probabilistic battery result over the same conformance question, their agreement measure, and a contribution weight declared at deployment registration.
Conditionality. Cross-phase corroboration arises only when both conditions are met: (a) a registered pre-invocation source with W1 or W2 witness class, deterministic output vocabulary, and order-typing covering one or more orders also evaluated by the probabilistic battery method in condition (b) evaluated the input; and (b) a probabilistic battery method in the §02a battery evaluated the same input elements under the same governing-specification predicates. Where either condition is absent, cross-phase corroboration is vacuously satisfied and its absence is recorded in the battery record without constituting a conformance defect.
Inter-AI pre-invocation sources. Where one or more registered pre-invocation sources are themselves AI systems — AI-based admissibility evaluators, AI classifiers, or equivalent — the per-source attribution and divergence-recording requirements of this section apply without modification. Each AI source’s signal element carries the source identity field (§3) attributing the finding to that AI system. Where two or more AI sources evaluate the same order-typed question and diverge, the divergence is recorded in the analysis battery record under the pre-invocation-cross-phase entry on the same terms as any other source divergence: the individual attributed contributions and the inter-source divergence measure are carried together, each independently recomputable against the retained witnesses. The inter-AI divergence at the pre-invocation layer is not a distinct architecture from the cross-phase corroboration mechanism — it is the same order-governed, per-source-attributed, recomputable divergence-as-forensic-signal structure instantiated where the sources happen to be AI systems. An implementation that records per-AI-source attribution and inter-AI divergence at the pre-invocation layer but not under the §02e witness chain and registry is structurally incomplete at the pre-invocation boundary.
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