Reasoning distillation does not leave uniform traces across target base families. In the current sample, we measure structural and functional identity across reasoning-distillation derivatives in three base-architecture families (Llama, Qwen, and Mistral) at five model scales (1.5B to 70B). Structural displacements are family-graded: Mistral-family targets show scars of 7,701β8,518 times the acceptance threshold, Llama-family targets show 2,858β4,583 times, and Qwen-family targets show 141β516 times β a sixty-fold range across three families, with the third-family result persisting under an independently trained derivative using different training data. Functional consequences do not track structural magnitude uniformly: Llama derivatives show decisive functional hierarchy breaks, Qwen derivatives remain within their base neighborhood, and Mistral β despite having the loudest structural scar β shows only marginal functional displacement. The functional departure is low-rank at every tested scale but varies in character: Gβ-dominant in Llama and Qwen families, with a sign-oscillating morphology in Mistral that suppresses centroid-level Gβ signal while preserving per-prompt dominance. The stiffness parameter at the measurement site is inversely ordered with structural scar magnitude across all three families. Fisher curvature, previously proposed as a candidate mechanism at small scale, does not order scar magnitudes correctly at production scale across families. These findings change how derivative identity claims should be interpreted: the expected displacement depends on the architectural context of the distillation, and the structural and functional layers can decouple β a model may show the loudest structural scar in the dataset while absorbing the functional perturbation. The Neural Network Identity Series β Mathematical foundations, empirical validation, and governance frameworks for verifying which model is running Newest addition: Technical Note: The Disappearing Window β AI Logprob Access Withdrawal and the Structural Verifiability of Frontier Model Contracts (DOI: 10.5281/zenodo.20362098) Paper 1: The Ξ΄-Gene: Inference-Time Physical Unclonable Functions from Architecture-Invariant Output Geometry (DOI: 10.5281/zenodo.18704275) Paper 2: Template-Based Endpoint Verification via Logprob Order-Statistic Geometry (DOI: 10.5281/zenodo.18776711) Paper 3: The Geometry of Model Theft: Distillation Forensics, Adversarial Erasure, and the Illusion of Spoofing (DOI: 10.5281/zenodo.18818608) Paper 4: Provenance Generalization and Verification Scaling for Neural Network Forensics (DOI: 10.5281/zenodo.18872071) Paper 5: Beneath the Character: The Structural Identity of Neural Networks β 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) Paper 7: The Deformation Laws of Neural Identity (DOI: 10.5281/zenodo.19055966) Paper 8: What Counts as Proof? β Admissible Evidence for Neural Network Identity Claims (DOI: 10.5281/zenodo.19058540) Paper 9: Composable Model Identity β 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) 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 β 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) Technical Note: Artifact Identity Is Not Runtime Identity β Trustfall Lite and the Boundary of File-Level Model Verification (DOI: 10.5281/zenodo.20019127) 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).
This research paper thoroughly addresses the processes of digitizing ceramic art and offering it as Non-fungible Tokens (NFTs) for sale. It examines the effects of transitioning from traditional ceramic molding methods to modern 3D printing and Stereolithography (SLA) technologies on the digital transfer of ceramic works. A case study on the use of porcelain highlights thanks to white color the impact of material choice on aesthetics and functionality. The rise of digitalization and NFTs introduces new mechanisms for preserving the originality and ownership rights of works, while also discussing the technical challenges and ethical issues this process entails. The article evaluates the place and future of ceramic art in the digital age from both technical and cultural perspectives. Our research posits that the digitization of ceramic works opens new avenues for the preservation, dissemination, and commerce of art, and that this process could have profound effects on the future of ceramic art. This process attempts to determine how ceramic works gain presence in the digital realm and their position in the NFT market. However, this study is one of the first to cover the entire process of ceramic art digitization and to address stage with academic rigor. The process from the creation of the work to its digitization, NFT registration, and sale, is detailed in this study. Our work demonstrates that the process of digitizing ceramic art and offering it as NFTs can have significant impacts on the future of art. It sheds light on the future of ceramic art by presenting both the opportunities brought by digitalization and the challenges encountered, as well as potential solutions. Furthermore, it emphasizes the importance and potential of ceramic art in the digital age and aims to fill the gaps in this field.
Open access
3D Surveying and Cultural Heritage
3D Shape Modeling and Analysis
Additive Manufacturing and 3D Printing Technologies
Engelmann Felix, Holland Martin, Nigischer Christopher, Stjepandi cacute Josip
Within the “Industrie 4.0” approach, 3D printing technology is characterized as one of the disruptive innovations. Conventional supply chains are replaced by value-added networks. The spatially distributed development of printed components, e.g. for the rapid delivery of spare parts, creates a new challenge when differentiating between “original part”, “copy” or “counterfeit” becomes necessary. This is especially true for safety-critical products. Based on these changes classicly branded products adopt the characteristics of licensing models as we know them in the areas of software and digital media. This paper describes the use of digital rights management as a key technology for the successful transition to Additive Manufacturing methods and a key for its commercial implementation and the prevention of intellectual property theft. Risks will be identified along the process chain and solution concepts are presented. These are currently being developed by an 8-partner project named SAMPL (Secure Additive Manufacturing Platform).
Open access
3D Shape Modeling and Analysis
Additive Manufacturing and 3D Printing Technologies