aikenkyu001/PKGF_Worm_1: PKGF‑Worm v1.0 — Release Notes

This release introduces PKGF‑Worm, the first deterministic connectome‑based dynamical model built on the Parallel Key Geometric Flow (PKGF) framework. The system maps the full 302‑neuron C. elegans connectome onto a 32‑dimensional geometric manifold, enabling the study of autonomous dynamics driven purely by differential‑geometric causality.

Key Features

• Deterministic PKGF Engine
  Fully deterministic manifold flow with no stochastic components, implemented in both Python and Fortran for cross‑verification.

• 302‑Node Connectome Integration
  Neural interactions encoded via asymmetric affinity matrices and metric‑warped interference.

• Spontaneous Symmetry Breaking & Awakening
  Observation of kinetic initiation events triggered by internal tension thresholds.

• Sustained Non‑equilibrium Attractors
  Emergence of undulatory, goal‑directed trajectories under geometric constraints.

• Intelligence Metric (𝓘)
  A quantitative measure of goal‑directed flow based on integrated distance to target.

• Deterministic Divergence Analysis
  Python–Fortran trajectory comparisons reveal Lyapunov‑like sensitivity under finite precision.

Included

• Full PKGF‑Worm source code (Python + Fortran)
• Experimental logs and reproducibility tests
• Mathematical documentation and theory reference
• Example trajectories and analysis scripts

If you'd like, I can also generate a CHANGELOG, tagline, or short/long GitHub description to accompany the release.