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Behind the build: the DNA Helix model explores state weighting and bounded updates.

DNA Helix · state & change

A small equation. A clear response.

Φ(x) = 1 / (1 + |x − 1|)

The weighting peaks at x = 1 and decreases with distance from that reference. In the historical Helix implementation, x comes from the mean of an embedding mapped into [0, 1].

Explore the weighting

0 x = 1
Weight Φ(x)0.7692

A visual interpretation
A higher weight brightens the village and its reflections. Move x toward 1 to see the weight rise. These scene effects are illustrative, not AI telemetry.

Go deeper: energy & bounded updates
E = wφΦ · (1 + wHH) · (1 + wT|τ|)

The model combines this weighting with normalized embedding entropy H and embedding spread τ. Its driver follows a clipped negative energy gradient with external input, momentum, and a bounded energy change.

Here τ denotes the source’s tensor/spread quantity; kinetic energy uses a separate T. The default weights are 1.

Source: Joseph’s AI_Brain Helix and FGTP implementation (November 2025). Read the equation provenance.

“DNA” names a computational model, not a biological process. This formula illustrates state weighting; it does not by itself implement agent coordination. Business workflows are separately engineered and tested.

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Explore the scene

An idea comes to life.

A business example

A visual illustration of the idea.

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