Coherence-Driven Intelligence

Coherence-Driven Intelligence (CDI, sometimes also referred to here as Emergent Intelligence (EI)) studies how intelligence-like structure can emerge from coherence reinforcement within constrained systems. Rather than treating learning only as explicit optimization over weights or symbolic rules, this research area investigates whether classification, memory, inference, and structured response can arise from phase alignment, shared-amplitude refinement, basin stabilization, and projection.

In CDI, a system is not trained by adjusting an opaque stack of weights toward a target output. Instead, structured responses emerge as the system settles into coherence basins: stable regions of phase-aligned organization. Classification can then be interpreted as basin formation and readout rather than as conventional learned parameter mapping.

This area includes the original Emergent Intelligence / Coherence-Driven Intelligence computational work, along with later CG research on coherence-based learning and self-organizing information processing.

Two-channel coherence-basin visualization from an EI/CDI network applied to MNIST classification. Digit classes stabilize into distinct coherence basins without conventional backpropagation or weight-based optimization. The two channels show that basin geometry can be organized differently within the same network, making the internal classification structure directly visible rather than hidden inside an opaque parameter stack. Although shown in two dimensions, these plots are effectively slices, or projections, of a higher-dimensional coherence space.

This project is presented as a proof of principle, not as a benchmark study or claim. The central claim is that coherent structure can perform classification-like organization through basin formation without conventional softmax, backpropagation, or gradient-descent training. Because the basin geometry is visible and can be modified through phase dimensions, channels, modular constructions, network architecture, and coherence constraints, performance optimization is treated as an open engineering direction rather than the purpose of this archival release. Scaling and comparison with conventional AI systems are left as future research directions.

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