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November 28, 20250 citationsOpen Access

A Cobordism–Branch–Valence Framework for the ABC Conjecture

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BWBailey, William

Key Points

  • The proposed framework reveals potential dynamics in measure spaces related to the ABC conjecture.
  • Key structures like cobordism and geometric flows interact to explore significant arithmetic properties.
  • Analytical techniques highlight behaviors under stochastic fluctuations, clarifying the conjecture's complexities.
  • The study suggests innovative paths for future research on the ABC conjecture via advanced geometric mechanisms.

Abstract

This manuscript develops a cobordism–inspired branch–valence framework for the ABC correspondence, reframing primitive triples (a, b, c) (a, b, c) (a, b, c) with a+b=ca+b=ca+b=c through geometric, analytic, and probabilistic structures. The construction encodes coarse prime data using a height-type valence functional and studies its behavior under residue-filtered geometric flows. A three-part Spiro inequality is introduced on local patches, and a residue-filtered gluing mechanism promotes patchwise control to a global normalized potential F~FF. The real part of F~FF governs the monotonicity and central envelope Φ0₀Φ0, yielding Szpiro-type inequalities along cobordism-smoothing flows. A complementary reformulation reparameterizes triples as probability measures μa, b, c₀, ₁, ₂μa, b, c on the primes, placing ABC arithmetic inside a compact weak-∗*∗ simplex. This compactness viewpoint suggests the possibility of “ABC flows’’ on measure spaces whose expected evolution of height-type functionals exhibits ABC-like behavior. A martingale-based soft-switching heuristic is proposed to reconcile gradient dynamics with stochastic fluctuations in prime support. The paper does not claim a proof of the ABC conjecture. Instead, it proposes a unifying geometric mechanism that narrows exceptional loci and clarifies how cobordism, height theory, residue filtration, and measure-theoretic compactness may interact in future approaches to the conjecture. The author gratefully acknowledges the conceptual, analytic, and expository assistance of the OpenAI large language model ChatGPT.

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Cite This Study

Bailey, William (2025) studied this question.

synapsesocial.com/papers/6928f113a65b730b9ea79f2bhttps://doi.org/10.5281/zenodo.17685694
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