GeoSeed: A Geometric Neural Architecture with 6 Sacred Tongue Origin Nodes

Model Description

GeoSeed is a novel neural network architecture where 6 origin nodes spawn icosahedral sphere grids in Cl(6,0) Clifford algebra space. Each grid represents a semantic domain (Intent, Context, Policy, Execution, Security, Attestation) weighted by the golden ratio, creating agent-dependent geometry for AI governance.

Unlike standard transformer architectures, GeoSeed operates on a Poincare ball where the metric tensor is modified by the agent's "tongue profile" — meaning different agents see different shortest paths through the same information space.

Architecture

Algebra: Cl(6,0) — 64-dimensional Clifford algebra with 15 bivector channels
Grid: Icosahedral sphere with 642 vertices at resolution 3 (3,852 total nodes across 6 grids)
Embedding: Poincare ball model of hyperbolic geometry
Composition: Product manifold composition with 21D canonical state averaging
Dressing: Full 14-layer SCBE pipeline traversal (SHA-256 hash + 21D state at each layer)

Key Innovation: Agent-Dependent Geometry

The tongue-weighted metric tensor:

g_ij(x, agent) = (4/(1-|x|^2)^2) * T_ij(agent)

Where T_ij encodes the agent's personality across 6 sacred tongue dimensions:

Tongue	Weight	Function
KO (Kor'aelin)	1.000	Intent, initiation
AV (Avali)	1.618	Attention, context
RU (Runethic)	2.618	Memory, policy
CA (Cassisivadan)	4.236	Execution, action
UM (Umbroth)	6.854	Suppression, security
DR (Draumric)	11.090	Lock, attestation

A "scout" agent with high KO/AV weights sees fast paths through information space. An "auditor" with high RU/UM/DR weights sees secure paths. Same graph, different geometry.

Cross-Tongue Convolution

Signals propagate between sphere grids through cross-tongue edges weighted by phi-ratio compatibility:

def cross_tongue_convolve(signal_source, signal_target, edge_weight):
    # Project through shared bivector basis
    shared = project_to_bivectors(signal_source)  # 15 channels
    # Weight by phi-ratio between source and target tongues
    weighted = shared * edge_weight * phi_ratio(source_tongue, target_tongue)
    # Parallel transport on Poincare ball
    return poincare_transport(weighted, source_point, target_point)

Training Data

14,654 supervised fine-tuning pairs available at: issdandavis/scbe-aethermoore-training-data

Sources include:

Governance decisions from 14-layer pipeline
Browser agent action traces with tongue routing
Combat blockchain data from game simulation
Sacred Eggs genesis protocol traces
Multi-model deliberation transcripts

Usage

from geoseed.model import GeoSeedModelNumpy

model = GeoSeedModelNumpy(
    n_tongues=6,
    grid_resolution=3,
    signal_dim=64,
    n_layers=14
)

# Process an event through the mesh
result = model.forward(event_vector, agent_profile='scout')
# result.decision: ALLOW | QUARANTINE | ESCALATE | DENY
# result.cost: float (harmonic wall value)
# result.tongue_activations: dict[str, float]

Tests

62 tests passing covering:

Sphere grid generation and icosahedral topology
Cl(6,0) algebra operations (geometric product, inner/outer product)
Poincare ball distance and transport
Cross-tongue convolution
14-layer dressing pipeline
Product manifold composition
Full forward pass

Part of SCBE-AETHERMOORE

GeoSeed is the M6 component of the broader SCBE-AETHERMOORE framework — a 14-layer AI governance pipeline with post-quantum cryptography (USPTO #63/961,403).

Full codebase: https://github.com/issdandavis/SCBE-AETHERMOORE

Citation

@software{davis2026geoseed,
  author = {Davis, Issac Daniel},
  title = {GeoSeed: Geometric Neural Architecture with Sacred Tongue Origin Nodes},
  year = {2026},
  url = {https://github.com/issdandavis/SCBE-AETHERMOORE},
  note = {Part of SCBE-AETHERMOORE AI Governance Framework}
}

License

MIT

Downloads last month: -; Downloads are not tracked for this model. How to track

issdandavis
/

geoseed-network