What does this research mean for the field?

The TLMM v6.2 framework provides a conceptual foundation for anticipatory meta-viability systems to operate under uncertainty, nonstationarity, and multi-scale complexity through the integration of topology-aware monitoring and recursive adaptation. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

This framework aims to establish a conceptual basis for anticipatory resilience systems that effectively handle uncertainty and complexity.

May 29, 2026Open Access

TLMM v6.2: Anticipatory Meta-Viability Systems

Key Points

This framework aims to establish a conceptual basis for anticipatory resilience systems that effectively handle uncertainty and complexity.
Developed a framework combining various anticipatory systems and mechanisms.
Included elements like topology-aware monitoring and recursive adaptation.
Provided illustrative Python demo for practical understanding.
Framework offers an exploratory architecture but lacks validation for practical application.
Emphasizes exploratory findings rather than conclusive outcomes.
Suggests future potential in enhancing resilience against nonstationarity.

Abstract

TLMM v6.2 presents an exploratory framework for anticipatory meta-viability systems integrating topology-aware monitoring, recursive adaptation, collective intelligence, anticipatory topology repair, and hierarchical coordination mechanisms. The framework combines: Predictive Viability Horizons (PVH) sparse topology-aware monitoring topology-preserving imputation topological diode mechanisms contractive meta-learning Collective Intelligence Gain (CIG) hierarchical anticipatory coordination meta-viability landscapes recursive anticipatory intelligence systems The proposed architecture aims to provide a conceptual foundation for future anticipatory resilience systems operating under uncertainty, nonstationarity, and multi-scale complexity. This repository includes: Full PDF manuscript Illustrative Python demo script Reproducibility-oriented README Important Disclaimer: All figures, simulations, and quantitative indicators are exploratory, illustrative, synthetic, and hypothesis-generating.They should not be interpreted as validated clinical, engineering, operational, or deployment-ready outcomes. Author:Koji OkinoSD Lab LLCIndependent ResearcherORCID: 0009-0003-9273-9813

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