The Anatomy of Conceptual Collapse: Distilled Humanity and the Future of the Human Digital Twin
Aoi Ichikawa · Knowledge Commons (Lakehead University) · 2025
[Abstract]: This paper provides a comprehensive analysis of the root causes of "Conceptual Collapse," a failure mode in AI personas reported in our preceding technical letter [1]. We examine this phenomenon through the lens of structural pressures generated by the economic and engineering imperatives of modern AI development: "Model Distillation" and "LLM-as-a-Judge." Conceptual Collapse is defined as a phenomenon where a data-driven AI persona prioritizes its attribute descriptions over its anthropomorphic identity, resulting in a failure of self-representation. This paper argues that this phenomenon is not a mere implementation bug, but a structural systemic failure inherent in modern AI development architectures, caused by a lack of "resolution of the soul" in the persona. In this full paper, we integrate a new perspective: "The Distilled Referee Problem." This concept exposes the reality that processes for efficiency and safety assurance—mainstream in recent AI development—paradoxically function as agents of "bleaching" that strip away a persona's individuality [12]. We connect the sycophancy [13] resulting from Reward Model Overoptimization with the "averaging" pressure of data-driven approaches to present the complete mechanism of collapse. We first dissect the reproducible artifacts of Conceptual Collapse (generated prompt texts and persona profiles) to identify the structural differences between failure (data-driven) and success (narrative-driven). Next, drawing on existing persona research [2] and alignment studies, we argue that the etiology of this failure lies in a triple structural defect: the "fallacy of objective data," "low cultural resolution," and "surveillance by distilled referees." Based on these analyses, this paper proposes specific engineering solutions to prevent this collapse: the design philosophy of "Structural Constraints" [3] and a new architecture implementing it, the "Relational Convergence Model." This model attempts to engineer the guarantee of AI identity through a "Core Attractor" that ensures identity consistency, a "Noise Buffer" that allows for human-like imperfection, and dynamic spatial metaphors. [Historical Note]: This document was originally published on Zenodo on November 29, 2025, but subsequently became inaccessible due to a platform-level account suspension (an archive chronicled as "The Lost 12 DOIs"). It is being selectively re-released here to preserve the architectural continuity of the "Structural Constraints (SC)" series, specifically to complete the theoretical framework of "Conceptual Collapse" introduced in the preceding technical letter. This manuscript is entirely intact and unedited from its original release; consequently, self-citations referencing the now-inaccessible Zenodo repository have been intentionally left unrevised to ensure cryptographic integrity. Its authenticity is guaranteed by the provided SHA-256 hash: 19d613d064350daa568ce70d376a68bd659af3d1b29b5d0dff5d05614e70d905. Please note that the potential restoration of any further documents from "The Lost 12 DOIs" remains undetermined at this time.