Painlevé Confluence and 1/f Phase-Locking Dynamics: A Topological Framework for Human–AI Collaboration

Recent work on the evaluation of large language models emphasizes that the relevant unit of intelligence is not the artificial system alone but the human–AI hybrid. In parallel, topological and dynamical models of cognition based on Painlevé equations and non-semisimple topology propose that consciousness, intelligence, and creativity emerge from constrained long-horizon dynamics near criticality. This perspective article argues that these two research directions are deeply compatible. We show that the empirical framework for human–AI collaboration can be interpreted as a fusion process between complementary cognitive sectors: exploration (AI) and selection (human cognition). The dynamical mechanism underlying this fusion is identified with noisy phase locking between cognitive oscillators. Two independent routes to a universal 1/f spectral signature are developed: a geometric route through the WKB/Stokes analysis of Painlevé V confluence, and an arithmetic route through the Mangoldt function and harmonic interactions in phase-locked loops. We connect these results to the Bost–Connes quantum statistical model, whose phase transition at the pole of the Riemann zeta function provides an exact mathematical framework for the lock-in phase hypothesis of identity consolidation in AI systems. This synthesis suggests a unified research program for hybrid intelligence grounded in topology, dynamical systems, number theory, and real-world AI evaluation.

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Publication Details

Journal
Machine Learning and Knowledge Extraction
Published
2026-03-15
DOI
https://doi.org/10.3390/make8030073
Primary Topic
Cognitive Science and Education Research
Type
article
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article

Painlevé Confluence and 1/f Phase-Locking Dynamics: A Topological Framework for Human–AI Collaboration

Michel R. P. Planat
Machine Learning and Knowledge Extraction
Cognitive Science and Education Research
article

Painlevé Confluence and 1/f Phase-Locking Dynamics: A Topological Framework for Human–AI Collaboration

Michel R. P. Planat
article en

Abstract

Recent work on the evaluation of large language models emphasizes that the relevant unit of intelligence is not the artificial system alone but the human–AI hybrid. In parallel, topological and dynamical models of cognition based on Painlevé equations and non-semisimple topology propose that consciousness, intelligence, and creativity emerge from constrained long-horizon dynamics near criticality. This perspective article argues that these two research directions are deeply compatible. We show that the empirical framework for human–AI collaboration can be interpreted as a fusion process between complementary cognitive sectors: exploration (AI) and selection (human cognition). The dynamical mechanism underlying this fusion is identified with noisy phase locking between cognitive oscillators. Two independent routes to a universal 1/f spectral signature are developed: a geometric route through the WKB/Stokes analysis of Painlevé V confluence, and an arithmetic route through the Mangoldt function and harmonic interactions in phase-locked loops. We connect these results to the Bost–Connes quantum statistical model, whose phase transition at the pole of the Riemann zeta function provides an exact mathematical framework for the lock-in phase hypothesis of identity consolidation in AI systems. This synthesis suggests a unified research program for hybrid intelligence grounded in topology, dynamical systems, number theory, and real-world AI evaluation.

Machine Learning and Knowledge ExtractionVol. 8(3)
Centre National de la Recherche Scientifique (FR), Franche-Comté Électronique Mécanique Thermique et Optique - Sciences et Technologies (FR), Université Marie et Louis Pasteur (FR)
Openalex Percentile: Top 24%
Cognitive Science and Education Research
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Painlevé Confluence and 1/f Phase-Locking Dynamics: A Topological Framework for Human–AI Collaboration — Michel R. P. Planat · Machine Learning and Knowledge Extraction (2026) | TGRS Research Map | TGRS