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Multi-Generation Evolution of AI Architecture for Macro–Micro Physics Unification

Tao, Junbo

Published Nov 18, 2025• 0 citations
Publisher

Abstract

We present a multi-generation architectural framework that unifies macro-scale propagation and micro-scale correction under a tensor/grid-flow formulation. Gen-1 (PLDL 2020-12 and PNN 2022-3)establishes the macro computational backbone: modular blockwise “regions” equipped with in-block linear-time discrete-convolution / linear-operator computation and plasticity-style per-parameter step control for efficient long-context modeling. Gen-2 maintains a macro-scale blockwise backbone (utilizing in-block linear-time discrete convolutions/operators) and integrates a micro-scale multi-dimensional cross-variable coupling. Gen-2(mid-2024) integrates multi-dimensional cross-variable coupling enforced by an algorithmic Quantum Error Correction (QEC) field. This mechanism utilizes a stable-collapse process driven by grid flow to achieve precise point-to-edge mapping, ensuring that the architecture remains physically and logically coherent as a deterministic-by-construction system. Gen-2.5 (Nov 5, 2024): This generation achieves the initial grand unification by retaining the macro blockwise backbone (modular partitioning with in-block linear-time operators) and generalizing micro–macro unification across blocks. It formalizes the Algorithmic Coherence Hedging mechanism, establishing the definitive algorithmic realization of 'Quantum Echoes' via cross-block unification. This mechanism bifurcates cross-block interactions into two functional radii: near-range cross-block coupling (for the local containment of perturbations) and far-range spatiotemporal weight assembly (for global consistency).By neutralizing logical echoes through coherent interference well before hardware-based demonstrations, this stage proves that noise rejection is achievable via inherent manifold evolution. This fundamental discovery anticipated Google’s 2025 'Quantum Echoes' hardware path by nearly 350 days. Gen-3 (2025) achieves the Grand Unification of Computation by synthesizing all layers (macro in-block linear operators + micro in-block multi-dimensional QEC + macro cross-block fusion). It implements an active dual-layer QEC stack that synchronizes micro-active correction (stabilizing intra-block dynamics) and macro-active correction (stabilizing far-range echo/remote fusion), elevating the system into a deterministic physical entity. Gen-4 introduces a geometric governance layer where local curvature automatically modulates the metric tensor across horizontal breadth and vertical resolution. This transforms the manifold into a self-regulating causal reference frame that dynamically allocates compute via a variable sampling budget , thereby implementing variable-metric manifold computation. We outline complexity-level gains from linear-time operators and discuss regime-dependent runtime reductions from improved iteration/correction efficiency. A disclosure-timeline appendix documents public dates and conceptual comparisons to contemporaneous work. Based on a Gen-4 thought experiment, the mechanism of Alzheimer's Disease (AD) is defined as a polarization instability between high-curvature (posterior cavitation) and low-curvature (anterior sclerosis) brain regions. This instability triggers a reversal of the hydraulic gradient and induces reverse turbulence, leading to the malignant polarization of the Domain/Range hedging mechanism originally designed to protect system stability. Such dynamical anomalies cause the negative gradient—the system's cognitive power source—to vanish and trigger pathological material accumulation at singularities. Ultimately, this cascade precipitates 'Dimensional Flow Paralysis' (including cross-areas and points), effectively disconnecting the brain from the physical manifold of reality.

DOI
10.5281/zenodo.17640174

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The KAN-DAM computing architecture establishes a deterministic quantum-computational model hosted on a finite 861-node spectral manifold, replacing isolated physical qubits and discrete gate sequences with globally distributed, non-local state representations. Information processing advances through an alternating cycle of linear functional mode mixing, non-linear dense associative coupling, and immediate geometric projection back onto the constrained manifold. The system integrates global Chebyshev spectral polynomial expansions with localized adaptive spline representations, coupling macro-level coherence with localized gradient absorption to eliminate oscillatory instabilities. The non-linear stage generates many-body interactions and effective entanglement in a single transformation without requiring deep sequences of two-qubit operations. Error suppression operates as an automatic geometric filter rather than an active algorithmic routine: perturbations that drift outside the rigid manifold constraints are discarded by the projection step, eliminating the need for ancilla qubits or syndrome measurements. This design circumvents the exponential entanglement scaling limits of classical tensor networks and the critical gap-closing slowdowns of adiabatic quantum annealing, providing an inherently stable platform that can be simulated classically or implemented across photonic, superconducting, and cold-atom hardware.
The scaling of artificial intelligence in computational biology, multi-omic fusion, and sequence modeling is fundamentally constrained by a volumetric memory wall, where dense cross-product evaluations yield intractable O(N2) memory and computational complexities. This paper presents the Topological Renormalization Architecture, a theoretical and computational framework that forces scale-free interaction volumes to undergo a geometric phase transition, confining information scaling to a physical Area Law O(N). By substituting traditional scalar quantization with geometric tensor restructuring—specifically utilizing Spectral Fiedler Vector Partitioning, Fast Walsh-Hadamard Preconditioning, and Adaptive Matrix Product States (MPS) via Tensor Tree Networks—the framework eliminates compiler-level software pointer indirection (the "coordinate tax") and systematically linearizes complex multidimensional topologies into bounded, quasi-one-dimensional register layouts. Due to the hyper-domain complexity of integrating tensor networks with epigenetic causality, traditional localized peer review is structurally insufficient. Furthermore, to support the evaluation of this multi-domain paradigm shift, this paper introduces a Universal, Bias-Resistant Evaluative Framework (Section 5). By establishing explicit epistemological guardrails, this standardized methodology is designed to neutralize subjective institutional prejudices, prevent the misapplication of legacy optimization metrics, and ensure rigorous, equitable, and unbiased cross-disciplinary peer review. To transition this mathematical framework from theoretical modeling into empirical reality, the architecture was evaluated across a rigorous, multi-regime empirical trajectory, progressing from algorithmic baselines to extreme-scale hardware stress testing: I. The Micro-Scale Proof of Concept In initial baselines utilizing real-world epigenetic microarray data encompassing 5,000 highly variant nodes, the architecture achieved a 72.9x reduction in hardware memory footprint compared to native sparse baselines, and a 4.6x physical memory reduction compared to state-of-the-art 3-bit scalar quantization proxies (e.g., Google TurboQuant). Crucially, while element-wise Mean Squared Error (MSE) was intentionally doubled to enforce a rigid hardware memory quarantine, the architecture retained 100.00% topological edge precision and 100.00% global pathfinding reachability. Empirical telemetry confirms that global interaction variance reduction is a dynamic, programmable feature governed by an Adaptive Bond Dimension ( ), allowing researchers to linearly scale topological fidelity (from 84.12% to 94.59% recall) without triggering the O(N2) memory explosion inherent to dense scalar models. II. Clinical-Scale Bi-Cameral Execution To evaluate the architecture on messy, real-world data, the framework was upgraded to the Bi-Cameral Discovery-Validation Architecture. By decoupling high-entropy approximate topological exploration from a deterministic FP64 exact-validation tier, the engine successfully processed the complete 485,512-node GSE104293 epigenetic DNA methylation manifold. In bare-metal GPU benchmarks, the production router achieved an unprecedented 99.65% in-sample dynamic recall and 100.00% topological edge precision at a Tensor Core latency of 22.45 ms, while capping peak volatile memory allocation at a mere 678.04 MB VRAM. Under rigorous out-of-sample generalization sweeps evaluating true biological measurement noise (=0.01), the engine maintained a highly resilient 94.75% to 95.10% dynamic recall, establishing a versatile multi-regime execution profile for macro-scale interactome routing. III. 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This study introduces a cross-variable quantum-inspired transmission model on discrete networks, integrating classical local couplings with non-local associative terms to explore quantum information propagation. Using first- and second-order dynamic weights, we formulate systems of equations optimized via Monte Carlo simulations, adjusting angular, phase, and spin variables to minimize energy states. Results reveal that cross-variable couplings amplify volatility and instability, particularly in multi-particle settings, as evidenced by broader frequency spectra in wave-packet simulations. To mitigate this, a QEC-inspired regularization penalty enforces local consistency, boosting success rates from ~40-50% to 55-70% in high-noise regimes. This framework unifies micro-scale particle-wave dynamics with macro-grid flows, offering insights for robust AI architectures bridging brain-inspired hierarchies and quantum systems.