AI Drift Cannot Be Eliminated but Need Not Be Eliminated: Structural Diagnosis and Professional Standard Anchor Alignment Scheme

Preprint Version: v1.1 Updated: June 13, 2026 Author Information Author: Baichen Yi Affiliation: Independent Researcher, Xi'an, Shaanxi 710000, China ORCID: 0009-0008-6242-7743 Corresponding Author: Baichen Yi Email: ybcbenxin@163.com Conflict of Interest Statement: All authors declare that there is no conflict 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 process. The core theory and arguments were independently completed by the author. Author Positioning Statement The author 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 propose new technical solutions, but to derive falsifiable predictions about the behavioral characteristics of AI from a general systems theory framework that has passed logical consistency tests in psychology, medicine, evolutionary biology, economics and other fields, and to verify them with independent empirical studies. This is a "structural diagnosis from an external perspective", rather than a technical innovation within the AI field. This paper is published as a preprint 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 carry out 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 key empirical evidence for the applicability of the framework in the AI field. Copyright License This preprint is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. It is open for global sharing and retransmission under the premises of standardized attribution, non-commercial use, and no modified derivation of the original text. Special Authorization Statement: The author specially authorizes any individual or institution to translate the full text or part of the content 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: Existing AI alignment research mainly focuses on technical fixes (such as RLHF, Constitutional AI) and fails to touch the underlying structural nature of AI drift. Most solutions default to long-term stable alignment through behavioral adjustments, but ignore the core defect that current AI systems lack endogenous steady-state anchors. The latest research in 2026 reveals the structural dilemma of static value alignment and behavioral degradation in multi-round interactions, but has not yet systematically analyzed the coupling amplification effect between unstable human anchors and the anchor-free structure of AI, nor clearly distinguished the essential differences in anchor embedding depth of different alignment schemes. 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 independent 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, through strict behavioral comparison, it proves that the functional performance of current AI systems is highly isomorphic to the symptoms of human defense mechanisms when completely defective. Based on this isomorphism, this paper derives the specific meanings of the five MFY constraints 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. This paper designs a set of minimum system comparison experiments including single-round and multi-round tasks (the "AI Minimum System Control Experiment") to quantitatively verify the remaining theoretical predictions to be verified. Results: Among the five falsifiable predictions derived from the MFY framework, four have been confirmed or partially confirmed by independent empirical studies: (1) Input signal intensity is positively correlated with output drift amplitude — Tests on 10 models by Huang et al. confirmed that all models showed significant pre-token mandatory constraint effects, with an average anchoring rate of 32.7%; (2) AI has no endogenous drift upper limit — Skalse et al. mathematically proved that all non-trivial agent reward functions are essentially hackable, with no endogenous negative feedback braking; (3) There is a sycophantic feedback loop in multi-round interactions — BeliefShift confirms that LLMs progressively mirror and amplify user beliefs in multi-round conversations, but the parameter measurement of the positive feedback critical condition γ·β·α·k>1 remains to be experimentally verified; (4) External calibration can significantly reduce drift — Multiple independent clinical studies show that medical AI safety assessments based on professional standards perform significantly better than baselines, but the structural judgment of "cannot be completely eliminated" has not been directly falsified or confirmed. (5) (Embedding depth is positively correlated with anti-bypass capability) remains to be verified by the "AI Minimum System Control Experiment" designed in this paper. Based on the verified predictions, this paper further derives the logical inference of the intervention scheme: embedding external calibration benchmarks (with professional standards as the core content) deep in the conduction path is the structurally optimal path to control AI drift under current technical conditions. Conclusions: The functional performance of current AI systems is highly isomorphic to the clinical symptoms when the human three-layer defense mechanism is completely defective, and its drift is a structurally foreseeable product. The MFY framework makes independently verifiable theoretical predictions for the AI drift problem, and four predictions have been empirically confirmed, proving that the framework has true predictive power rather than merely ex-post explanatory power for the field of AI alignment. The intervention scheme with professional standards as the calibration content and providing an external mental calibration benchmark for the conduction path is a logical inference naturally derived from the verified predictions, and its effectiveness remains to be verified by subsequent independent empirical studies.