fallrisk-ai
Published March 25, 2026 | Version v1
Preprint Open

Post-Hoc Disclosure Is Not Runtime Proof: Model Identity at Frontier Scale

Authors/Creators

  • 1. Fall Risk Research

Description

Current AI deployment stacks authenticate agents, workloads, and credentials but do not verify which neural network is computing at inference time. Recent incidents — including the undisclosed use of an open-weight foundation model inside a commercial product, industrial-scale distillation campaigns, and emerging agent identity standards that authenticate software without authenticating models — show that this gap has practical consequences. Post-hoc disclosure resolved these incidents; runtime proof would have made the model identity question answerable at inference time.
 
This paper asks whether runtime model identity is technically feasible at frontier scale. We present three results. First, we enrolled and verified five open-weight transformer models spanning 8 billion to 72.7 billion parameters across three families, with zero false acceptances in all pairwise comparisons and self-verification within the acceptance threshold for all models. A thermodynamic observable predicted by extreme value theory remained within two percent of its predicted value across the full range, with no statistically significant scale-dependent correction detected across more than two orders of magnitude in parameter count. Second, we tested structural separability on three declared-lineage distillation pairs spanning 8 billion to 70 billion parameters — each derivative sharing identical architecture with its base — and measured separations ranging from 2,858 to 4,583 times the acceptance threshold, increasing monotonically with model scale across two base-model families. All derivatives self-verified within the acceptance threshold. Third, we demonstrate a frontier-scale software attestation path — including signed JWT issuance and downstream policy consumption — and situate it within a previously formalized attestation architecture that composes with enterprise identity infrastructure, complementing rather than replacing current agent identity frameworks.
 
These results demonstrate that runtime model identity is measurable and separable across the tested range of open-weight instruct-tuned transformers from 8B to 72.7B, with a frontier-validated software attestation path and an inherited route to stronger hardware-backed and proof-backed assurance.
 

The Neural Network Identity Series — Mathematical foundations, empirical validation, and governance frameworks for verifying which model is running

Newest addition:

Technical Note: The Disappearing Window — AI Logprob Access Withdrawal and the Structural Verifiability of Frontier Model Contracts (DOI: 10.5281/zenodo.20362098)

  1. Paper 1: The δ-Gene: Inference-Time Physical Unclonable Functions from Architecture-Invariant Output Geometry (DOI: 10.5281/zenodo.18704275)

  2. Paper 2: Template-Based Endpoint Verification via Logprob Order-Statistic Geometry (DOI: 10.5281/zenodo.18776711)

  3. Paper 3: The Geometry of Model Theft: Distillation Forensics, Adversarial Erasure, and the Illusion of Spoofing (DOI: 10.5281/zenodo.18818608)

  4. Paper 4: Provenance Generalization and Verification Scaling for Neural Network Forensics (DOI: 10.5281/zenodo.18872071)

  5. 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)

  6. Paper 6: Which Model Is Running?: Structural Identity as a Prerequisite for Trustworthy Zero-Knowledge Machine Learning (DOI: 10.5281/zenodo.19008116)

  7. Paper 7: The Deformation Laws of Neural Identity (DOI: 10.5281/zenodo.19055966) 

  8. Paper 8: What Counts as Proof? — Admissible Evidence for Neural Network Identity Claims (DOI: 10.5281/zenodo.19058540)

  9. Paper 9: Composable Model Identity — Formal Hardening of Structural Attestations in the Enterprise Identity Stack (DOI: 10.5281/zenodo.19099911) 

  10. Paper 10:Where Identity Comes From: Path Sensitivity and Endpoint Underdetermination in Neural Network Training (DOI: 10.5281/zenodo.19118807)

  11. Paper 11: Post-Hoc Disclosure Is Not Runtime Proof: Model Identity at Frontier Scale (DOI: 10.5281/zenodo.19216634)

  12. Paper 12: Family-Dependent Response to Reasoning Distillation Across Structural and Functional Identity Layers (DOI: 10.5281/zenodo.19298857)
  13. Paper 13: Safety-Alignment Removal as a Model-Identity Failure — Structural Evidence from Published Weight-Level Mutation Checkpoints (DOI: 10.5281/zenodo.19383019)

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).

Files

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Additional details

Identifiers

URL
https://fallrisk.ai

Continues
Preprint: 10.5281/zenodo.18704275 (DOI)
Preprint: 10.5281/zenodo.18818608 (DOI)
Preprint: 10.5281/zenodo.18872071 (DOI)
Preprint: 10.5281/zenodo.18907293 (DOI)
Preprint: 10.5281/zenodo.19008116 (DOI)
Preprint: 10.5281/zenodo.19055966 (DOI)
Preprint: 10.5281/zenodo.19058540 (DOI)
Preprint: 10.5281/zenodo.19099911 (DOI)
Preprint: 10.5281/zenodo.19118807 (DOI)
Preprint: 10.5281/zenodo.18776711 (DOI)
Is continued by
Preprint: 10.5281/zenodo.19298857 (DOI)
Technical note: 10.5281/zenodo.19342848 (DOI)
Preprint: 10.5281/zenodo.19383019 (DOI)
Is supplemented by
Software: 10.5281/zenodo.18930621 (DOI)
Technical note: 10.5281/zenodo.19240883 (DOI)
Technical note: 10.5281/zenodo.19275524 (DOI)
Technical note: 10.5281/zenodo.20019127 (DOI)

Software

Repository URL
https://github.com/fallrisk-ai/trustfall-lite
Development Status
Active