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Coherent Generative Intelligence.

Non-hallucinatory AI architecture with real-time neural plasticity and forward-only coherent propagation. Replacing backpropagation with causal consistency.

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Four-Layer Triadic Substrate

CGI organizes computation across four distinct layers to ensure structural coherence and operational reality.

LAYER 1

Perception

Raw Feature Registration. Ingests uncurated input states (X_t) and converts stimuli into multi-dimensional coordinate vectors without subjective bias.

LAYER 2

Artificial Neural Plasticity

Dynamic Plasticity. Grants the network real-time ownership of its weights (W_t) and biases (B_t). Parameters are fluid variables that adapt on every forward pass.

LAYER 3

Invariance & Comprehension

State Engine. Maintains internal state (S_t) and measures "Contradiction Energy" (E(S_t)) against invariant laws. Produces Intent Vector (Γ) to guide Layer 2.

LAYER 4

Base Ground

Operational Reality. Validates execution against physical/logical invariants. Functional outputs are confirmed; failures trigger instant reorganization in Layer 2.

Coherent Propagation

Forward-only parameter adaptation replaces backward-pass gradient descent. Memory efficiency: O(1) vs O(n).

Key Innovations

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Artificial Neural Plasticity (ANP)

Unlike traditional networks with frozen parameters during inference, CGI grants the network ownership of its weights and biases in real-time. Every forward pass adapts W_t and B_t based on contradiction energy feedback.

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Reinforcement Learning from Causal Coherence (RLCC)

Replaces human-centric RLHF with binary coherence-decoherence loop. Outputs are tested against fundamental operational transformations (Φ). Coherence = +1 reward, Decoherence = -1 penalty + immediate ΔW_t update.

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Coherent Propagation (CP)

No backpropagation tape required. Memory allocation remains O(1) relative to sequence length. Parameters adapt at each time step through forward-moving causal sequence, not reverse gradient calculation.

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Static vs Generative Mathematics

Distinguishes between identity operations (indexing, spatial mapping) and additive duplication operations (state emergence, continuous growth). Resolves singularities like division-by-zero that corrupt standard neural networks.

Defensive Publication — Available Now

CGI architecture documentation released for prior art establishment. White paper includes full mathematical formalism, implementation specifications, and reference architecture diagrams.