Generalised Consensus-Helicity Coherence Framework G-CFT Pseudo-Holographic OSINT Processing. A Low-Cost Alternative to Data-Center AI

5281/zenodo. 19712939 Generalised Consensus–Helicity–Coherence Framework (G-CFT): Pseudo-Holographic OSINT Processing. A Low-Cost Alternative to Data-Center AI Generalised Consensus–Helicity–Coherence Framework (G-CFT) — a novel paradigm for processing information that departs from conventional data-center artificial intelligence and returns to the physical principles of wave-based computation. Modern AI systems rely on brute-force digital approximation, leading to exponential growth in energy consumption, hardware costs, and model complexity. In contrast, G-CFT demonstrates that information processing can be performed directly in the physical domain, using interference, resonance, and spectral transformations, without relying on large-scale discrete computation. At the core of the framework lies a pseudo-holographic processing model, where textual OSINT (Open-Source Intelligence) streams are treated as wave-like fields. Instead of reconstructing full semantic representations, the system computes regularised correlation functionals: Φλ=⟨b, m~λ⟩Φλ=⟨b, m~λ⟩ This approach enables: detection of weak signals under high noise conditions suppression of incoherent or contradictory information (“nonsense filtering”) real-time decision support via coherent attractor dynamics 🔹 Mathematical and Physical Foundation The framework builds upon: Double Fourier transform (Kharkevich, 1962) Tikhonov regularisation with two parameters (λ₁, λ₂) Lyapunov stability theory for closed-loop convergence Hyperbolic geometry (Gromov) for modeling decision-space drift Kuramoto synchronization for distributed consensus Together, these elements define a stable, physically grounded alternative to probabilistic token-based inference. 🔹 Hardware Realisation A key contribution of this work is the demonstration that G-CFT can be implemented on a low-cost edge device (~450) using: Microwave analog Fourier processor FPGA-based regularisation controller High-bandwidth memory (HBM) with revolver architecture Silicon interconnect fabric (SiIF) This architecture achieves: orders-of-magnitude reduction in energy consumption near-constant cost per transform real-time processing without large-scale training 🔹 Conceptual Contribution G-CFT reframes intelligence as a coherence-seeking physical process, rather than a statistical approximation. The system identifies stable semantic structures (attractors) within noisy information fields and converges toward them through resonance and damping. In practical terms: The system does not reconstruct reality — it detects whether a desired scenario is becoming coherent. 🔹 Relation to Patent Ecosystem The framework is structurally aligned with a broader set of patented technologies (Ecosystem IV), including: NonsenseShield — dual-layer filtering of incoherent signals Mind-AI-Floor — resonant consensus computation Spectral Resonator — stability control via frequency-domain damping Dual-Contour Adaptive Subject — persistent system state architecture Policy-Enforced Governance — auditability and control layer These components collectively define a governed, stable, and scalable architecture for next-generation AI systems. 🔹 Application Domains OSINT and geopolitical analysis Distributed intelligence systems Edge AI and low-power devices Autonomous decision systems Swarm intelligence and robotics 🔹 Key Insight A 500 wave-based processor can outperform large-scale data-center systems — not by scale, but by using the right physics. 🔹 Keywords G-CFT, #holographic computation, #OSINT, #analog AI, Fourier transform, Tikhonov regularization, Lyapunov stability, edge intelligence, swarm intelligence, AI governance, resonance computing, low-cost AI