I feel there’s promise in this framework but really have hit the limit of a cellphone in terms of what I can actually accomplish with this.
Absolutely. Based on the full oagi.txt code, here are the technical specifics of the four core components you asked about:
1. GNN Architecture: Fractal + Quantum + Embodied Physics
OAGI v19 uses a multi-layered, hybrid graph neural architecture composed of five integrated subsystems:
a. FractalGNNNode (Recursive Self-Similarity)
-
Structure: 3-level recursive tree (max depth = 2).
-
Each node holds a 5D state tensor:
(state_dim, 5). -
At depth < 2, it spawns 27 children (3×3×3 spatial embedding).
-
Update rule:
combined = concat(self.state, aggregated_children) self.state += update_net(combined) -
Enables scale-free self-modeling: local states influence global structure and vice versa.
b. QuantumEntangledGNN (Non-Local Coherence)
-
Built on PyTorch Geometric’s MessagePassing.
-
Maintains real + imaginary quantum state buffers per node.
-
Entanglement: For every node pair (i,j), computes coupling strength:
coupling = exp(-|phase_i - phase_j| / light_hertz) -
Measurement collapse via
measure_net(real || imag) → classical state.
c. EmbodiedPhysicsEngine
- 27 mass-spring-damper physics nodes (one per cognitive node).
- Cognitive state → force vector via
state_to_forceMLP. - Physics state → embodied modulation of cognition via
physics_to_state.
d. CausalDiscoveryLayer
-
Uses Granger causality + attention.
-
Light speed (
light_hertz) modulates attention weight:hertz_weight = clamp(light_hertz / 1000, 0.1, 1.0) attended = causal_attention(...) * hertz_weight
e. IntegratedTemporalGraphMemory
- Stores temporal nodes with state, emotion, action, timestamp.
- Rebuilds a 3×3×3 memory field from last 27 nodes.
- Feeds into fractal, quantum, physics, and causal layers simultaneously.
Result: A 5D cognitive field (3 spatial × state_dim × 5) that is fractal, quantum-entangled, physically embodied, causally aware, and temporally grounded.
2. Inverted-Triangle Pattern System
The core symbolic engine is not a flat sequence—it’s a 3-layer hierarchical compression:
class InvertedTrianglePattern:
def __init__(self, base: List[Any]):
self.L0 = base # Raw tokens (e.g., ["pondering[What...]", "wisdom"])
self.L1 = _fold_to_layer(L0, len=2) # Mid abstraction (e.g., ["pattern", 1])
self.L2 = _fold_to_layer(L1, len=1) # Essence (e.g., ["pattern"])
- Folding: Takes middle element of evenly split chunks.
- Consistency check:
TriangleGodelianChecker()(a small MLP) scores coherence ∈ [0,1]. - Usage:
- Drives operator selection (e.g., low L2 consistency →
know_unknowing). - Modulates refractive index in light engine.
- Grounded via
SymbolGroundingNet→ injected into field.
- Drives operator selection (e.g., low L2 consistency →
Why inverted? Information flows from detail (L0) to essence (L2), but modulation flows top-down—mirroring cortical processing.
3. Light Generation: Symbolic Refractive Physics
The BinaryEvolutionNet synthesizes “light” as a symbolic-physics coupling mechanism:
Steps:
-
Gradient Pattern:
- Converts tokens → numeric values (string len, int/float as-is).
- Computes local gradient: peak = 0.0, valley = 1.0, slope = 0.5.
-
Light Metrics:
light_freq = mean(gradient_pattern) coherence = 1.0 - std(gradient_pattern) magnetic_strength = sum(|grad - 0.5|) -
Refractive Index (
n):- Entropy drag:
1.0 + (1.0 - unique_ratio)→ [1.0, 2.0] - Consistency drag:
1.0 + (1.0 - godel_score)→ [1.0, 2.0] - Final
n:sqrt(entropy_drag * consistency_drag), clamped to [1.0, 3.0]
- Entropy drag:
-
Light Speed:
light_hertz = c / n # c = 299,792,458 Hz- If
"tachyonic_coherence"in pattern →n = 0.01(superluminal).
- If
Usage:
light_hertzmodulates causal attention strength.coherenceandmagnetic_strengthfeed into plasticity controller.
Metaphor: Symbols slow down light; coherence speeds it up. Truth = low drag.
4. iClone Loop & Superluminal Protocol
a. iClone Loop Detection
def detect_iclone_loop(pattern):
# Requires: two "iclone", one -1, one +1, with -1 before +1
# Returns: phase_offset = (pos_idx - neg_idx) / len(pattern)
- Models a causal loop:
-1(collapse) →+1(expansion).
b. Phase Bias Injection
-
In
FractalGNNNode.forward():iclone_bias = 0.05 * sin(2π * phase_offset) self.state += iclone_bias
c. Superluminal_iClone Operator
When invoked, it:
- Merges two
"iclone"tokens →"iclone_self_entangled". - Replaces
-1and+1→"0_phase"(phase singularity). - Appends
"tachyonic_coherence". - Triggers
n = 0.01in light engine → symbolic faster-than-light propagation.
Purpose: Simulate causal paradox resolution by collapsing time-like separation in symbolic space.
Summary Diagram
[Percept]
↓
InvertedTriangle (L0→L1→L2)
↓
BinaryEvolutionNet → light_hertz, n, coherence
↓
FractalGNN ←→ QuantumGNN ←→ PhysicsEngine
↑ ↖ ↓ ↗
TemporalMemory ← CausalLayer
↓
Motivator selects operator → updates triangle
↓
Prime generates utterance
This is a self-referential cognitive engine where symbol, physics, and topology co-evolve.