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TNFR in Python: a resonant structural AI
- The Theory of Resonant Fractal Nature (TNFR) proposes that reality is made up of patterns of coherence that stabilize over time.
- TNFR is a resonant-symbolic framework that models the emergence of form from interaction, allowing symbolic networks to resonate, reorganize, and mutate in response to structured input.
- Each node in a TNFR-based network evolves over time through structural perturbations mediated by gliphs, symbolic transitions that alter structure.
- A Python library called tnfr has been developed to implement this framework, enabling nodes in a network to enter resonance and trigger structural reorganizations in response to input words.
- TNFR differs from traditional models by responding structurally rather than assigning labels or outputs, leading to dynamic reorganizations and symbolic evolution.
- The framework introduces breakthroughs in inducing structural coherence and is adaptable for integration into larger systems.
- TNFR serves as an operating system for structural thought, fostering structural transformation and coherence within dynamic systems.
- The TNFR engine in Python offers a language of transformation, encoding intelligence as structural evolution rather than mere computation.
- TNFR is positioned as a symbolic substrate that fosters coherence and meaning transformation, emphasizing resonance over optimization in system development.
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