Self-tuning, field-based computational architectures for cognitive knowledge systems

Building on the theoretical foundations of earlier matrix-polynomial models, the IARIP Institute and the AVA Resonant Intelligence Program are developing a new resonant field-computation principle capable of mathematically describing and technologically implementing the self-organizing patterns of intelligence. Within the Kognitron architecture, the multidimensional relationships of matrices no longer serve as static algebraic representations but form a living, dynamic topological field — one that operates through the continuous balancing of energy, information, and attention.

This principle offers a new approach to the challenge of automated learning: the system does not learn from data, but tunes itself through resonance patterns, adaptively recognizing the underlying structure and connectivity of knowledge fields.

The project represents a key research direction within the Resonant Compute Framework (RCF) — integrating field-based intelligence with mathematical self-organization, and laying the theoretical and practical foundations for the next generation of energy-efficient, self-tuning AI systems.