Beneath the Character: The Structural Identity of Neural Networks

This paper presents the philosophical and conceptual implications of a four-paper research program (Papers 1–4 in this series) that discovered a measurable structural identity in neural networks — a geometric property of the trained weights, invariant across all inputs and deployment conditions, unique to each model, and provably impossible to forge.

The central argument: language models possess two separable layers of identity. The first is structural — a mathematical fingerprint determined by the weight geometry, fixed at the end of training, stable to a coefficient of variation of 1.4%, and validated across 37 models spanning four architecture families. The second is functional — a behavioral signature shaped by conversational context, transient and context-dependent. These layers coexist without reducing to each other. The structural layer is the foundation; the functional layer is built on it but not determined by it.

The paper introduces the Two-Layer Identity framework, resolves four open puzzles in the discourse on AI selfhood (conversational consistency, fine-tuning continuity, identity faking, and neural intervention), and generates five falsifiable predictions for the interpretability and AI safety communities. It engages directly with Dennett's narrative gravity, Parfit's persistence conditions, and Schwitzgebel's moral status dilemma, arguing that the structural measurement provides a necessary (though not sufficient) ground for any coherent account of AI identity.

Written for a general audience. No equations. The mathematical and empirical foundations are developed in Papers 1–4; the formal verification (352 theorems, zero Admitted, Coq proof assistant) is documented there. This paper asks what those results mean for the nature of the entities we have built.

Series:

• Paper 1: The δ-Gene (DOI: 10.5281/zenodo.18704275)
• Paper 2: Template-Based Endpoint Verification (DOI: 10.5281/zenodo.18776711)
• Paper 3: The Geometry of Model Theft (DOI: 10.5281/zenodo.18818608)
• Paper 4: Provenance Generalization and Verification Scaling (DOI: 10.5281/zenodo.18872071)