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Preprint . 2025
License: CC BY
Data sources: Datacite
ZENODO
Preprint . 2025
License: CC BY
Data sources: Datacite
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Reflecting on the Möbius Phenomenon: Theoretical Foundations and Mathematical Models for Question-Jumper AI

Authors: TOEDA, TAIKO;

Reflecting on the Möbius Phenomenon: Theoretical Foundations and Mathematical Models for Question-Jumper AI

Abstract

With the rapid advance of large language models and the growing complexity of AI applications, the need for open-ended, question-evolving AI systems—capable of reframing their own inquiry space—is more urgent than ever.This paper proposes and formalizes the Möbius Phenomenon as a theoretical and mathematical framework for the next generation of artificial intelligence: the Question-Jumper AI.Unlike conventional answer-optimizing systems, Möbius-based AI is designed to evolve its own question space through recursive self-reference, multi-persona architectures, and the explicit modeling of question dynamics.Drawing on philosophical traditions of self-negation and historical paradigms of cognitive leap, this work introduces a formal sequence model for question circulation, persona emergence, and dimensional leaps in reasoning.The Möbius Phenomenon is positioned as a paradigm for building open-ended, explainable, and resilient AI, with a roadmap from theory to implementation and social embedding.All mathematical formulations, model flowcharts, and core concepts are included for academic critique and further development.

Keywords

AI, artificial intelligence, philosophy, question-driven, Möbius, self-evolving, metacognition, question-jumper, recursive systems, multi-persona architecture, explainable AI, collective intelligence, open-ended AI Subjects: Artificial Intelligence, Philosophy of Mind, Mathematics, Cognitive Science

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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