⚖️ seeking an endorsement to publish a formal paper on this architecture

I am seeking an endorsement to publish a formal paper on this architecture to arXiv (CS.ML). I believe these results demonstrate that "Living Cell" resonance models can outperform Attention in parameter-constrained and latency-sensitive environments. If you are a researcher willing to endorse or review the mathematical formalization, please contact me via GitHub.

⚔️ Takeover Battle: Baseline Summary

The Experiment: A competition between Hierarchical Depth and Parallel Resonance.

1. STD (Standard Transformer)

• Strategy: Deep Logic.
• Structure: A 9-layer vertical stack of Transformer blocks.
• Mechanism: Relies on sequential Self-Attention to "reason" through text patterns.
• Trade-off: High computational cost on CPU due to layer depth ($O(L \cdot N^2)$).

2. GEO (Geometric Manifold)

• Strategy: Broad Resonance.
• Structure: A single-layer manifold with 6 parallel competitive cells.
• Mechanism:
  • Bio-Pulse: Uses Sine-wave activations (torch.sin) to mimic neural firing.
  • Inhibition: Parallel cells compete via an inhibition matrix to reduce redundancy.
  • Prototypes: High-dimensional similarity routing instead of fixed sequential paths.
• Trade-off: Extremely fast; replaces serial depth with spatial width and rhythmic "rhythm" detection.

📊 Structural Comparison

Feature	STD (Standard)	GEO (Geometric)
Philosophy	Deep Sequential	Broad Parallel
Logic	Self-Attention	Competitive Resonance
Activation	GELU (Standard)	Sine-Pulse (Bio-inspired)
CPU Speed	Slower (Serial)	Faster (Parallel)

Goal: Determine if a broad, competitive manifold can achieve a lower loss than a traditional deep stack by treating language as a resonant rhythm rather than a logical sequence.

Results

⚔️ ByteFight

The Experiment: A head-to-head architectural battle between two "brains" processing the same raw byte stream.

1. STD (Standard Transformer)

• Philosophy: Deep Logic.
• Mechanism: Global Attention—every byte looks at every other byte.
• Performance: Highly capable but computationally heavy. It suffers from Quadratic Complexity ($O(N^2)$), leading to slower CPU speeds (150ms–240ms).

2. SGR (Sovereign)

• Philosophy: Broad Manifold.
• Mechanism: Local Convolution + Parallel Expert Cells. It mimics biological local connectivity.
• Performance: Highly efficient. It operates with Linear Complexity ($O(N)$), running consistently 3x faster (~55ms) than the standard model.

🏆 The Result

The SGR (Sovereign) model is currently winning on speed and hardware efficiency. It proves that for byte-level logic, a wide, parallel spatial map can outperform a deep, sequential stack while using significantly fewer computational resources.

Results

🧬 Sovereign Geometric Routing (SGR): The "Living Cell" Alternative

The battle

1. The Core Philosophy

Traditional AI architectures (Transformers) act as a "Black Box"—a brain with no inherent structure, relying on a massive soup of statistical signals to calculate global attention ($O(n^2)$). This is non-biological and computationally wasteful.

SGR (Sovereign Geometric Routing) proposes the Living Cell theory:

• The Soma: Every token embedding is a physical neuron body fixed in a high-dimensional territory.
• The Pulse: Each cell maintains an internal temporal memory (the "path").
• Synaptic Recruitment: Instead of firing every connection, the cell performs Calculated Recruitment. It only activates the specific synapses it needs to reach the next logical state.

Results