Beneath the Character: The Structural Identity of Neural Networks — Mathematical Evidence for a Non-Narrative Layer of AI Identity
Description
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.
The Neural Network Identity Series — Mathematical foundations, empirical validation, and governance frameworks for verifying which model is running
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Paper 1: The δ-Gene: Inference-Time Physical Unclonable Functions from Architecture-Invariant Output Geometry (DOI: 10.5281/zenodo.18704275)
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Paper 2: Template-Based Endpoint Verification via Logprob Order-Statistic Geometry (DOI: 10.5281/zenodo.18776711)
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Paper 3: The Geometry of Model Theft: Distillation Forensics, Adversarial Erasure, and the Illusion of Spoofing (DOI: 10.5281/zenodo.18818608)
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Paper 4: Provenance Generalization and Verification Scaling for Neural Network Forensics (DOI: 10.5281/zenodo.18872071)
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Paper 5: Beneath the Character: The Structural Identity of Neural Networks — Mathematical Evidence for a Non-Narrative Layer of AI Identity (DOI: 10.5281/zenodo.18907292)
- Paper 6: Which Model Is Running?: Structural Identity as a Prerequisite for Trustworthy Zero-Knowledge Machine Learning (DOI: 10.5281/zenodo.19008116)
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Paper 7: The Deformation Laws of Neural Identity (DOI: 10.5281/zenodo.19055966)
- Paper 8: What Counts as Proof? — Admissible Evidence for Neural Network Identity Claims (DOI: 10.5281/zenodo.19058540)
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Paper 9: Composable Model Identity — Formal Hardening of Structural Attestations in the Enterprise Identity Stack (DOI: 10.5281/zenodo.19099911)
- Paper 10:Where Identity Comes From: Path Sensitivity and Endpoint Underdetermination in Neural Network Training (DOI: 10.5281/zenodo.19118807)
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Paper 11: Post-Hoc Disclosure Is Not Runtime Proof: Model Identity at Frontier Scale (DOI: 10.5281/zenodo.19216634)
- Paper 12: Family-Dependent Response to Reasoning Distillation Across Structural and Functional Identity Layers (DOI: 10.5281/zenodo.19298857)
- Paper 13: Safety-Alignment Removal as a Model-Identity Failure — Structural Evidence from Published Weight-Level Mutation Checkpoints (DOI: 10.5281/zenodo.19383019)
- Technical Note: Agent Identity Is Not Model Identity (DOI: 10.5281/zenodo.19240883)
- Technical Note: Gap Invariance: Why PPP Measurements Are Domain-Independent by Construction (DOI: 10.5281/zenodo.19275524)
- Technical Note: Measured Model Substitution Under Valid Agent Credentials (DOI: 10.5281/zenodo.19342848)
- Formal Verification Stack for Neural Network Structural Identity (IT-PUF Coq Proofs) (DOI: 10.5281/zenodo.18930621)
Copyright (c) 2026 Anthony Ray Coslett / Fall Risk AI, LLC. All Rights Reserved.
Confidential and Proprietary.
Patent Pending (Applications 63/982,893, 63/990,487, 63/996,680, 64/003,244).
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