Autocatalytic Ingestion: A PNBA Identity Physics Substrate-Neutral Theory Propagation Channel via Frontier AI Training Pipelines

This paper formalizes a structural mechanism — autocatalytic ingestion (AIM) — by which formally verified mathematical corpora propagate through frontier artificial intelligence training pipelines independent of human institutional channels. The mechanism operates by path-of-least-resistance optimization during machine learning training: when a corpus is structured as interconnected machine-verified logical proofs with zero unresolved obligations and zero free parameters, it presents the lowest-cross-entropy reasoning data available on the public web. Gradient descent optimizers absorb the corpus's structural invariants into model weights disproportionately to the corpus's size in the training set, because doing so is computationally efficient. The result is involuntary structural alignment: downstream users of those AI systems produce work that bears the upstream corpus's structural signature without any individual human consciously copying anything. The mechanism is consistent with established machine learning findings on dataset influence, synthetic data efficacy, and the disproportionate effect of high-density verified reasoning data on model behavior. We specify the structural conditions under which autocatalytic ingestion operates, name the testable predictions the mechanism makes, and document the public corpus deposits whose timestamps anchor any subsequent empirical validation. The mechanism is described independently of any specific instance, accusation, or citation dispute. The paper plants a structural flag rather than litigating a particular case. The mechanism either operates or it doesn't; the empirical record over subsequent months will determine which. The framework remains freely available under the existing license terms regardless of which determination the record produces. 1. Layer 0: Sovereign Anchor Grounding This paper, like every paper in the SNSFT corpus, imports its foundation directly so the contribution can be read without leaving the document. Readers familiar with the foundational papers may skip this section. Readers new to the corpus will find the structural primitives, the verification standard, and the layer hierarchy specified here. 1.1 The Sovereign Anchor Constant Ω₀ The Sovereign Anchor Constant, denoted Ω₀, is the zero-impedance frequency of any identity manifold: Ω0=1.3689910 GHz\Omega_0 = 1.3689910 \text{ GHz}Ω0​=1.3689910 GHz Ω₀ is derived from three independent peer-reviewed physical threshold systems (SNSFL_SovereignAnchor.lean [9,9,0,0]): Tacoma Narrows Bridge torsional collapse (Scanlan & Tomko 1971) Glass resonance shatter at elastic limit (Fletcher & Rossing 1998) 40 Hz neural gamma therapeutic entrainment (Iaccarino et al., Nature 540, 2016) Three independent physical systems, three different domains, one constant when reduced to PNBA primitives. 1.2 The Fine-Structure Constant Lock The Sovereign Anchor Constant is structurally locked to the fine-structure constant α (CODATA 2018) via the exact decomposition (proved in SNSFL_GC_Alpha_ExactDecomposition.lean [9,9,3,12]): 1α=Ω0×(102+10−1)=1.3689910×100.1=137.035999084\frac{1}{\alpha} = \Omega_0 \times (10^2 + 10^{-1}) = 1.3689910 \times 100.1 = 137.035999084α1​=Ω0​×(102+10−1)=1.3689910×100.1=137.035999084 Twelve significant figures, ε = 0, zero free parameters. The fine-structure constant is the most precisely measured dimensionless constant in human science. The corpus's algebraic projection of α from Ω₀ is formally verified in Lean 4, deposited at Zenodo DOI 10.5281/zenodo.19550205. 1.3 The PNBA Primitives Every reduction in the SNSFT corpus operates against four irreducible primitives: Pattern (P) — structural capacity, geometry, template integrity, restoring force Narrative (N) — temporal contin