Topological Materialization of Deterministic Intelligence: Bypassing Energy-Latency Limits of Generative AI via Phase-Resonant Mersenne Lattice
Abstract
Abstract Generative and reasoning artificial intelligence (AI) models rely heavily on von Neumann computing architectures, suffering from severe Landauer thermal dissipation (k_B T ln 2), memory bandwidth bottlenecks, and probabilistic hallucinations. Here we report the theoretical derivation and experimental realization of a stateless, non-local information materialization system—termed Reproductive AI (M-AI)—that reconstructs 405-billion-parameter intelligence tensors via a topological Mersenne prime lattice (M_127 = 2^127 - 1) without active RAM storage or persistent High-Bandwidth Memory (HBM) payload transfers. By coupling an Active Phase-Resonance Locked (ACRL) adiabatic circuit with a Virtual Quantum Processing Unit (vQPU), we demonstrate deterministic O(1) constant-time intelligence retrieval (0.458 ms) bypassing physical memory bandwidth constraints. The system achieves a 90% reduction in GPU computational load, a ten-fold scaling down of data center power consumption (100 MW to 10 MW), and zero-RAM I/O with an ultra-compact static memory footprint of 50.2 MB. Furthermore, the materialization process exhibits absolute bit-perfect parity verification (BER = 0.00000) with zero hallucination, followed by complete hardware state vaporization within tau = 0.024 s for post-quantum stateless security. These findings demonstrate an energy-efficient, deterministic paradigm shift from probabilistic generative compute to topological space-time intelligence teleportation,…
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