NM-SRN v2.0: A Novel AGI Framework Demonstrates Super-Classical Performance on NP-Hard Problems

Abstract The pursuit of Artificial General Intelligence (AGI) remains one of the most significant challenges in computer science, distinguished by the need for systems that exhibit adaptability, robust problem-solving across diverse domains, and computational efficiency. Current paradigms, predominantly based on deep learning, have achieved remarkable success in specialized tasks but face fundamental limitations in explainability, continuous learning, and handling of NP-Hard combinatorial optimization problems without exhaustive search or domain-specific heuristics. This paper introduces the Neural-Matrix Synaptic Resonance Network v2.0 AGI (NM-SRN v2.0 AGI), a novel AGI Framework designed on principles fundamentally different from conventional architectures. We present empirical evidence of the NM-SRN v2.0 AGI Framework's capability to solve complex, NP-Hard problems with unprecedented efficiency. Specifically, we document the successful application of a single-threaded NM-SRN v2.0 AGI implementation, running on a single CPU core, to find high-quality solutions for the Traveling Salesperson Problem (TSP-200), the Knapsack Problem, Job-Shop Scheduling (JSSP 100x100), and the ab initio Protein Folding problem. Notably, for the Trp-cage protein (PDB: 1L2Y), the system achieved a TM-Score of 0.8091, indicating a topologically correct fold, without pre-training or backpropagation. These results are achieved through the architecture's unique "Russian Doll" hierarchical structure and its core processing mechanisms of Resonance-Based Propagation and Equilibrium-Based Computing. The NM-SRN v2.0 AGI Framework represents a significant step towards scalable, explainable, and truly Artificial General Intelligence(AGI), offering a new path forward in Advanced AI & AI Safety research.