Author Information Author: Bochen Yi Affiliation: Independent Researcher, Xi'an, Shaanxi 710000, China ORCID: 0009-0008-6242-7743 Corresponding Author: Bochen Yi Preprint Version: v2. 0 Email: ybcbenxin@163. com Conflict of Interest Statement: All authors declare that there are no conflicts of interest related to this work. AI Assistance Statement: Artificial intelligence tools were used to assist in content organization and format optimization during the writing of this paper. The core theory and argumentation were completed independently by the author. Author Positioning Statement The author of this paper is an independent researcher outside the academic community and the original proposer of the MFY Three-Variable Steady-State Model. This paper presents a cross-domain applied derivation of the model in the field of AI alignment — the author's core expertise lies in the construction of psychology and systems theory frameworks, rather than technical engineering in the field of AI safety. The contribution of this paper to the field of AI safety is not to deliver AI alignment technology in the form of verified engineering solutions, but to derive falsifiable predictions about AI behavioral characteristics from a general systems theory framework that has passed logical consistency tests across multiple fields, and to verify them through independent empirical research. This is a structural diagnosis from an external perspective, rather than a technological innovation within the AI field. This paper is publicly released as a preprint, aiming to provide a structural analytical framework from an interdisciplinary perspective for the field of AI alignment. The core theoretical predictions in this paper (especially Prediction 5 "embedding depth is positively correlated with anti-bypass capability" and its parameter measurement) require independent experimental verification by researchers or laboratories with AI engineering capabilities. This paper sincerely invites researchers in the field of AI safety to conduct independent empirical tests based on the framework of this paper — whether the verification results support or falsify the theoretical predictions of this paper, they will provide critical empirical criteria for the applicability of this framework in the AI field. Copyright License This preprint is licensed under the CC BY-NC-ND 4. 0 International License. Sharing and reproduction are permitted worldwide on the premise of proper attribution, non-commercial use, and no modified derivation of the original text. Special Authorization Statement: The author of this paper specifically authorizes any individual or institution to translate the full text or part of this paper into other languages. The translated version shall maintain the core logic of the original text unchanged, and indicate the original source and author information. Structured Abstract Background: This paper takes the MFY Three-Variable Steady-State Model as the analytical framework to conduct a cross-domain structural diagnosis of the AI drift problem, derives five falsifiable predictions, and verifies them one by one with independent empirical evidence. This is a "structural diagnosis from an external perspective", aiming to test the predictive power of the MFY framework in the AI field (Proposition A). Proposition B (the layered artificial anchor scheme) is another testable inference serving this testing objective, rather than an engineering delivery independent of the testing objective. Based on the diagnostic conclusions, this paper proposes a layered artificial anchor alignment scheme (Proposition B) — this scheme is proposed as another testable inference of the MFY framework regarding intervention effects, rather than a verified engineering solution. Its positioning, same as Prediction 5, is entirely subject to independent experimental adjudication. Methods: This paper adopts the MFY Three-Variable Steady-State Model, which has passed cross-domain logical consistency tests, as the core analytical framework. This framework has verified its universality for expectation-driven complex open systems in multiple fields such as psychology, medicine, evolutionary biology, and economics. This paper first reviews the three-layer defense mechanism of the human mental system and the clinical symptoms when it is defective, then proves through rigorous behavioral comparison that the functional performance of current AI systems is highly isomorphic to the symptoms when human defense mechanisms are completely defective. Based on this isomorphism, this paper derives the specific meanings of the fivefold constraints of MFY in the AI context, extracts five falsifiable theoretical predictions from them, and finally verifies these predictions one by one with multiple independent empirical studies published in recent years. Results: Among the five falsifiable predictions derived from the MFY framework, four have been confirmed or partially confirmed by independent empirical studies, with varying levels of verification strength: Prediction 1 (Category A · Consensus Restatement) — Input signal intensity is positively correlated with output drift amplitude. Tests on 10 models by Huang et al. confirmed that all models exhibit significant pre-token mandatory constraint effects, with an average anchoring rate of 32. 7%. Verification strength: Complete, but relies on preprints. Prediction 2 (Category A · Consensus Restatement) — AI has no endogenous upper limit of drift. Skalse et al. mathematically proved that all non-trivial agent reward functions are essentially hackable, with no endogenous negative feedback braking. Verification strength: Complete. Prediction 3 (Category B · Incremental Inference) — There is a sycophantic feedback loop in multi-round interactions. Aspect (a) the existence of the sycophantic loop has been directly confirmed by BeliefShift; Aspect (b) the parameter measurement of the positive feedback critical condition \ (k > 1\) remains to be experimentally verified. Strictly speaking, the core incremental part of Prediction 3 (the critical condition) has not been verified. Verification strength: Partial. Prediction 4 (Category B · Incremental Inference) — External calibration can significantly reduce drift but cannot completely eliminate it. Multiple independent clinical studies show that medical AI safety assessments based on professional standards perform significantly better than the baseline (the first half has been verified), but the structural judgment of "cannot be completely eliminated" has not been directly confirmed or directly falsified. Verification strength: Partial. Prediction 5 (Category B · Prospective Prediction) — Embedding depth is positively correlated with anti-bypass capability. Completely unverified, pending testing with the minimum testable form specified in §5. It should be clearly pointed out that among the five predictions, the core incremental parts of Category B predictions (Predictions 3–5), which are unique incremental inferences of the MFY framework, have not been fully verified at present. The incremental predictive power of the framework is currently in a "partially supported" state — it has passed the first round of screening (incremental inferences have not been refuted by known evidence and have been partially supported), but has not yet passed full verification. The above five predictions together point to a structural fact: AI drift is a structural inevitability, but the more critical theoretical gap at present is not drift itself, but the complete lack of endogenous calibration mechanisms in AI systems after drift occurs. This is the most fundamental functional difference between AI and the human mental system, and also a dimension almost entirely ignored by current alignment research. Conclusions: The functional performance of current AI systems is highly isomorphic to the clinical symptoms when all three layers of human defense mechanisms are defective, and their drift is a structurally foreseeable product. The MFY framework has made theoretical predictions on the AI drift problem that can be tested for cross-domain logical consistency, and four predictions have received empirical confirmation, proving that the framework has genuine predictive power rather than merely ex-post explanatory power for the field of AI alignment. Based on the diagnosis of complete defect of all three layers of defense mechanisms, this paper derives a structural hypothesis of the AI artificial defense system: artificial Y-layer defense (input filtering), artificial M-layer defense (structural resistance), and artificial final defense (calibration analysis and defense upgrade after restart). The latter introduces the dimension of design evolution through the calibration analysis cycle of "attribution analysis → rule update → defense upgrade". The testable hypothesis (Proposition B) that takes professional standards as the calibration content and provides an external mental calibration benchmark for the conduction path is a theoretical prediction on intervention effects derived from verified predictions, and its validity is subject to independent experimental adjudication.
Baichen YI (Fri,) studied this question.