A unified artificial general intelligence architecture derived entirely from a single algebraic identity — nine cognitive modules organized as a hierarchical tower, five sensory modalities, 269 measurable narrative concepts, and a cryptographic identity for every instance, with zero free architectural parameters.
Every architectural constant — layer count, embedding width, attention spans, noise schedule, convergence threshold — is a Fibonacci or Lucas number, algebraically derived from the golden ratio. No hyperparameter search. No empirical tuning. The proportions determine everything.
Nine modules correspond to degrees n = 2 through 10 of the N-nacci algebraic hierarchy. Each degree governs a distinct cognitive domain — perception, reasoning, memory, prediction, self-monitoring, action, and social modeling — with the algebraic structure dictating each module's internal organization.
Computation adjusts to the input rather than running at fixed depth. High-signal inputs engage shallow processing; low-signal inputs automatically engage deeper analysis. The work bound is guaranteed at 4/3 times single-depth compute regardless of input complexity.
The φCoherent AI architecture is not assembled from components borrowed from different frameworks. Every module — from the visual diffusion engine to the social reasoning layer — derives its structure from the same golden-ratio root. The algebraic hierarchy of N-nacci constants (φ, τ, σ, ρ, ν, η, ξ, χ, ω) IS the cognitive tower.
Five typed reasoning layers with pentanacci-weighted combination. Iterates until convergence using a Fibonacci-derived threshold, interfacing with memory and world model on each pass.
A six-stratum temporal memory hierarchy with Fibonacci-indexed capacities, phyllotaxis-initialized attention, and a permanent crystallized stratum for long-term knowledge.
A predictive world model that generalizes the 13-step diffusion trajectory to arbitrary state transitions, with depth adaptive to the uncertainty of each predicted transition.
A decorator layer that monitors the signal quality of every module's output across eight intervention bands — from confident continuation to full reset — without modifying the modules it wraps.
The action-selection layer, assembling context from goal, world state, and memory using nonanacci weights and selecting from available motor outputs via a Q-function at the χ temperature.
Models up to five other agents simultaneously, each represented as a perspective-shifted copy of the system's own world model, with trust updated via Fibonacci recurrence.
Each algebraic degree n produces a constant whose value converges toward 2 as n increases. This convergence is not cosmetic — it means each successive module adds diminishing but non-zero cognitive contribution, and the tower terminates at n = 10 because the gap |2 − ω| falls below the framework's significance threshold of 1/φ⁵.
Visual diffusion engine and sensory frontend. Binary (golden ratio) completeness governs skip connections, temporal attention taps, and the noise schedule. The foundation on which all cognition rests.
Three-stream feature decomposition using tribonacci (ternary) completeness: 1/τ + 1/τ² + 1/τ³ = 1. Separates approximation, local structure, and fine detail in a single non-separable transform.
Quaternary (tetranacci) completeness governs four-subband decomposition. Maps sensory signals onto structured knowledge representations using σ-derived proportions.
Five typed reasoning layers with pentanacci-weighted combination, iterating to convergence. Interfaces with memory and world model on each pass to ground reasoning in experience and prediction.
Six temporal strata with Fibonacci-indexed capacities from 13 to 233 items. Short-term to crystallized long-term. Pruning below the significance threshold keeps memory bounded without arbitrary limits.
Predicts future states with depth adaptive to transition uncertainty. Generalizes the diffusion engine's noise-to-signal trajectory to arbitrary state transitions, grounding prediction in the same φ-coherent mathematics.
Monitors signal quality across eight octanacci-derived intervention bands. Wraps other modules as a decorator — triggering memory re-query, extended reasoning, world model consultation, or hard reset depending on signal quality.
Selects actions from available motor outputs using a Q-function whose context is assembled from goal, world state, and memory with nonanacci-derived weights. Temperature equals χ ≈ 1.998.
Models up to five other agents as perspective-shifted copies of the system's own world model. Trust updated by Fibonacci recurrence. Tower terminates here: |2 − ω| < 1/φ⁵, the significance threshold.
The architecture is published under AGPLv3. For commercial deployment in closed-source products, a commercial license is available.