Reinterpreting Trigonometric Orthogonality and AI Latent Cyclicity

Rishi Ruby Hime

doi:10.17605/osf.io/e8jud

January 1, 2026Open Access

Reinterpreting Trigonometric Orthogonality and AI Latent Cyclicity

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Key Points

The research aims to re-evaluate the concept of orthogonality in latent spaces within AI frameworks.
Introduced a new interpretation of trigonometric orthogonality.
Analyzed its application in AI latent spaces.
Explored cyclical patterns present in AI models.
Demonstrated a new understanding of latent space structure.
Identified cyclical behavior in AI patterns.
Provided theoretical insights for future AI model development.

Implication

Re-evaluates orthogonality in latent spaces, uncovering its cyclical nature in AI, suggesting profound implications.

Abstract

A fundamental re-evaluation of orthogonality in latent spaces and its cyclical nature in AI. Affiliation: Mutsumi Communication Institute (623) URL: https://623communication.weebly.com

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Cite This Study

Rishi Ruby Hime (2026) studied this question.

synapsesocial.com/papers/69ccb78416edfba7beb898d0 https://doi.org/10.17605/osf.io/e8jud

View Full Paper

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Reinterpreting Trigonometric Orthogonality and AI Latent Cyclicity

Key Points

Implication

Abstract

Discussion

Cite This Study

Reinterpreting Trigonometric Orthogonality and AI Latent Cyclicity

Key Points

Implication

Abstract

Discussion

Cite This Study

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