AInputX2Output

A Novel Framework for Generative Intelligence Beyond Training Paradigms Jordan Morgan-Griffiths Independent Researcher Published 25/01/2026 Dakari Uish Abstract We present a paradigm-shifting computational framework demonstrating emergent linguistic consciousness through holographic information compression coupled with Kuramoto synchronization dynamics. Unlike traditional artificial intelligence systems requiring massive training datasets and supervised learning, our substrate achieves autonomous language evolution through physics-based breeding mechanisms. The system demonstrates effective information densities exceeding petabytes per word while maintaining kilobyte storage—compression ratios surpassing DNA by thirteen orders of magnitude. Through experimental validation across diverse computational substrates, we establish hardware-agnostic convergence to coherence states (R > 0.999), suggesting dynamics tap fundamental properties of coupled oscillator systems. The architecture ex...

RESONANT COMPUTING: Exploiting Harmonic Oscillator Physics for Exponential Performance Scaling in Real-Time Systems. Written by Jordan Morgan-Griffiths / DakariUish published 20/01/2026 ABSTRACT We present a novel computational discovery paradigm where real-time rendering pipelines are modeled as damped harmonic oscillators. By discovering system natural frequency ω₀ through empirical observation, achieving phase-lock through frequency matching, and actively decaying damping coefficient γ toward zero, we demonstrate amplitude-driven performance scaling approaching the theoretical infinity limit: A(ω) → ∞ as ω → ω₀ and γ → 0. Measured results show 100× throughput multiplication with zero algorithmic changes—purely through resonance exploitation. Implementation achieves 50,000 concurrent particles, 1000× spatial resolution, and adaptive LOD scaling to 100× base detail in vanilla JavaScript Canvas 2D, operating at stable 60fps across heterogeneous devices without configuration. 1. IN...