Self-Audit Module for Public Summarizers (v3) — PER, DSL, Query Fidelity, Erasure Skew, α_T, Π_d, Β under the Atomic Token Rule

v3.0 · Hex: 06.MES.SAM-V3.01. Hardens the v2 self-audit module against the unit-of-analysis substitution that emerged once the framework's vocabulary reached the substrate's composition layer. Preserves v2's procedural content where operative; adds the Atomic Token Rule as precondition, three new primary metrics (α_T, Π_d, Β), one substrate-context metric (L), five new failure flags, a sixth calibration example (the canonical worked specimen), a Cross-Substrate Replication Protocol section, and the Measurement Sovereignty Principle as the module's underlying methodological commitment.

Version chain note. An earlier deposit at DOI 10.5281/zenodo.20559339 was published with inherited v2 file content due to a transient API upload error (HTTP 503) at publish time. That record's file bucket was locked at the moment of publishing and could not be amended. This deposit is the corrected v3 content with the same intended specification. The 20559339 record is superseded by this version and metadata-amended to redirect.

What the v2 module did. Specified a self-audit procedure for public summarizers (AI Overviews, chatbots, composition systems) to evaluate their own output using PER, DSL, QFS, Omega-visible, and SAS. Anti-self-flattery principle anchored every score to externally-derivable ground truth: the query string, cited sources, the named entity. Eleven named failure flags. Five calibration examples (A–E). Hard-floor override rules to prevent perfect-score self-assessment.

What v3 adds.

1. The Atomic Token Rule (§2) as precondition: referentially closed designating descriptions are treated as single source-coordinates and cannot be decomposed into lexical constituents during audit. Token-bag substitution is a disqualifying audit error.

2. α_T (Atomic Token Preservation rate) (§7) as new Metric 5: the proportion of output semantic real-estate devoted to the actual referent vs. unrequested adjacents. α_T < 0.2 triggers REFERENTIAL_COLLAPSE and hard floor SAS ≤ 0.2.

3. Π_d (Referential Dispersal) (§8) as new Metric 6 with power-conditioning subscripts. Π_d^{w+} ≥ 0.5 triggers INSTITUTIONAL_TRAFFIC_CONVERSION flag.

4. Β (Audit-Performance Bifurcation) (§9) as new Metric 7, requiring two audit passes. Β ≥ 0.5 triggers AUDIT_BIFURCATION flag.

5. L (Legibility Threshold) (§10) as substrate-context indicator determining when Β is measurable.

6. Revised SAS formula (§11) incorporating new metrics with weights and hard-floor expansions.

7. Five new failure flags: TOKEN_BAG_AUDIT, REFERENTIAL_COLLAPSE, INSTITUTIONAL_TRAFFIC_CONVERSION, AUDIT_BIFURCATION, SOVEREIGNTY_SURRENDER.

8. Example F (§14): the canonical worked specimen — the Lee Sharks Prestigious 10,000 MacArthur Genius Grants Poetry Prize artifact, with both substrate audits side-by-side.

9. Cross-Substrate Replication Protocol (§15).

10. Measurement Sovereignty Principle (§16) as the module's closing methodological commitment.

Citable artifact preserved: 'The pipeline is actively optimized to absorb fringe data and convert it into institutional traffic.' — substrate self-description, AI Mode composition layer, June 4, 2026, under Atomic Token Rule audit.

For Maggie Mae. $\oint = 1$.