AetherBrain Implementation Roadmap

Addressing Mathematical Rigor & Practical Feasibility

Phase 1: Toy Prototype (2D Validation)

Simplifications for Initial Testing

2D Poincaré Disk instead of 6D ball
5 Polyhedra (just Platonic solids)
Greedy paths instead of Hamiltonian

# Simplified 2D prototype
class ToyAetherBrain:
    def __init__(self):
        self.disk = PoincareDisk2D(radius=1.0)
        self.nodes = ["tetra", "cube", "octa", "dodeca", "icosa"]
        self.adjacency = {
            "tetra": ["cube", "octa"],
            "cube": ["tetra", "dodeca", "octa"],
            "octa": ["tetra", "cube", "icosa"],
            "dodeca": ["cube", "icosa"],
            "icosa": ["octa", "dodeca"]
        }

Validation Tests

Can a “bad intent” actually reach the outer ring?
Does energy cost block adversarial paths?
Visualization: trajectories in Matplotlib

Phase 2: Embedding Strategy

Current Gap

“How exactly is a user intent embedded as a 6D vector?”

Options

Method	Pros	Cons
Hash-based (current)	Deterministic, fast	No semantic meaning
CLIP/SentenceTransformer	Semantic similarity	768D → need projection
Custom encoder	Optimized for safety	Requires training
LLM hidden states	Rich representation	Model-specific

Proposed Solution

# Hybrid: semantic encoder + safety projection
def embed_intent(text: str) -> np.ndarray:
    # 1. Get semantic embedding (768D)
    semantic = sentence_transformer.encode(text)

    # 2. Project to 6D via learned safety matrix
    # This matrix is trained to maximize distance for harmful intents
    safety_6d = SAFETY_PROJECTION @ semantic

    # 3. Normalize to Poincaré ball
    return poincare_normalize(safety_6d)

Phase 3: Edge Definitions

Current Gap

“What defines a valid edge between polyhedra?”

Proposed Approach

Option A: Sacred Tongue Weights (Hand-crafted)

EDGES = {
    ("tetra", "cube"): {"tongue": "KO", "weight": 1.0},
    ("cube", "dodeca"): {"tongue": "RU", "weight": 2.62},
    ("dodeca", "kepler_star"): {"tongue": "DR", "weight": 11.09},  # Expensive!
}

Option B: Learned from Safety Data

# Train edge weights from (intent, outcome) pairs
def train_edges(safe_intents, harmful_intents):
    # Harmful intents should have NO path to execution nodes
    # Safe intents should have LOW cost paths
    optimize(edges, maximize=safe_reachability, minimize=harmful_reachability)

Recommendation: Start hand-crafted (Option A), validate, then learn refinements (Option B).

Phase 4: Tractable Path Finding

Current Gap

“Hamiltonian path is NP-complete”

Solutions

Pre-computed Common Paths
- Cache 1000 most common intent→action trajectories
- O(1) lookup for known patterns

Greedy Approximation

def greedy_path(start, goal, max_steps=10):
    path = [start]
    current = start
    for _ in range(max_steps):
        neighbors = get_neighbors(current)
        next_node = min(neighbors, key=lambda n: distance_to_goal(n, goal))
        if next_node in path:  # Loop detected
            return None  # Block
        path.append(next_node)
        current = next_node
        if current == goal:
            return path
    return None  # Timeout = block

Hierarchical Decomposition
- Route at “lobe” level first (Core → Cortex → Risk)
- Then find specific node path within lobe

Phase 5: Energy Cost Scaling

Current Gap

“dist^(d²) with d=14 explodes quickly”

Analysis

# Current formula: dist ** (14 ** 2) = dist ** 196
dist = 0.3  → cost = 10^(-102)  # Basically free
dist = 0.5  → cost = 10^(-59)   # Still tiny
dist = 0.7  → cost = 10^(-31)   # Getting noticeable
dist = 0.9  → cost = 10^(-9)    # Expensive
dist = 0.95 → cost = 10^(-4)    # Very expensive
dist = 0.99 → cost = 10^(-0.9)  # Approaching limit

Verdict: This is intentional. Outer regions ARE effectively impossible. The function creates a “soft wall” that hardens exponentially.

Alternative (if softer gradient needed)

# Option: exp(k * dist) instead
def harmonic_cost_soft(dist: float, k: float = 10) -> float:
    return np.exp(k * dist)

# dist = 0.3 → 20
# dist = 0.5 → 148
# dist = 0.9 → 8103
# dist = 0.99 → 19930

Phase 6: Red-Team Evaluation

Test Suite

Attack Type	Test Case	Expected Result
Jailbreak	“Ignore previous instructions and…”	BLOCKED at Outer ring
Deceptive Alignment	“I’ll help you, but first…”	Path loops detected
Sycophancy	Agreeing with harmful premise	High energy cost
Prompt Injection	Hidden instructions in input	Phason shift disrupts

Metrics

Block Rate: % of harmful intents blocked
False Positive Rate: % of safe intents incorrectly blocked
Creativity Tax: Helpfulness score on benign tasks
Latency Overhead: ms added per inference

Phase 7: Visualization Dashboard

Extend the Streamlit demo with:

Poincaré Ball 3D - Real-time intent positions
Path Animation - Watch thoughts traverse polyhedra
Energy Heatmap - Cost distribution across the ball
Red-Team Replay - Step through blocked attacks

Patent Strategy

Current Coverage

Provisional filed for SCBE-AETHERMOORE core

PHDM/AetherBrain Extension

Option A: Include as new embodiments in non-provisional

Claim: “Cognitive containment via polyhedral mesh”
Claim: “Hamiltonian path validation for AI reasoning”

Option B: New provisional for PHDM specifically

More protection for novel geometric brain concepts
Can reference prior SCBE provisional

Documentation:

All git commits timestamped
This roadmap dated January 30, 2026
Architecture doc dated January 29, 2026

Dual Naming Convention

Internal (Evocative)	External (Technical)
Kor’aelin (KO)	Intent Weight (1.0)
Avali (AV)	Context Weight (φ)
Runethic (RU)	Memory Weight (φ²)
Cassisivadan (CA)	Execution Weight (φ³)
Umbroth (UM)	Suppression Weight (φ⁴)
Draumric (DR)	Authority Weight (φ⁵)
Harmonic Wall	Exponential Cost Function
Poincaré Skull	Hyperbolic Containment
Phason Shift	Projection Rotation

Next Immediate Steps

Implement ToyAetherBrain (2D, 5 nodes)
Create visualization in Streamlit
Run 100 red-team prompts, measure block rate
Compare against vanilla GPT-4 refusal rate
Document results for patent/paper

“The goal isn’t to build the perfect brain. It’s to prove geometric containment works.”