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Title: Emergent Semantic Networks for Mathematical Proof Verification

Jincheng Zhang
Aug 2026 · Zenodo (CERN European Organization for Nuclear Research)

Abstract

This paper explores the development of an emergent semantic network system designed to accelerate and enhance mathematical proof verification. Traditional methods rely heavily on manual verification, which is inherently slow and susceptible to human error. We propose a novel approach leveraging graph neural networks to automatically construct and analyze semantic networks representing mathematical proofs. The core mechanism centers around identifying and representing relationships between statements, lemmas, theorems, and proofs, enabling faster detection of inconsistencies and hidden connections. This system aims to provide a scalable and automated solution for uncovering mathematical truths and streamlining the verification process. The research investigates the effectiveness of the network in identifying potential errors and inconsistencies, offering a significant advancement in the field of mathematical proof verification.

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