Persistent Homology as a Theory of Emergent Structure

Abstract:Why do some macroscopic structures remain identifiable even though their microscopic constituents continually change? Vortices persist while fluid parcels turn over, neural memories persist while spikes and synapses fluctuate, and institutions persist while individuals enter and leave. We propose a scale-relative answer: an emergent property is a persistent nontrivial homology class [z]\in H_p=\ker\partial_p/\im\partial_{p+1}, a macro-feature that is closed but not exact across a filtration of descriptions. This identification turns emergence into a \emph{measurement} problem. Persistent bars detect stable macro-features, and we introduce a contractive-similarity (CS) graph operator to supply scaffold spectral gaps that predict robustness. Hodge decomposition separates harmonic macro-scaffold from exact and co-exact micro-flow; and functorial condensation explains when one level's emergent class becomes a unit for the next. The resulting scaffold-flow framework expresses six familiar signatures of emergence (i.e., inevitability, coherence, irreducibility, complementarity, robustness, and hierarchy) within one mathematical language. It also yields falsifiable predictions across atmospheric, neural, and social systems: genuine emergent structures should persist across filtrations, remain spectrally stable, respond disproportionately to harmonic interventions, and require timescale separation for hierarchical autonomy. We will also discuss the implications of structural persistence for AGI and ASI.

Machine Learning (cs.LG)

arXiv:2507.03065 [cs.LG]

(or arXiv:2507.03065v3 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2507.03065 Focus to learn more arXiv-issued DOI via DataCite

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