QNLLM v3.1: A Formally Verifiable Continual Learning System with 21 Behavioral Invariants

This deposit presents QNLLM v3.1, a quantum-classical hybrid continual learning system that provides mathematical guarantees against catastrophic forgetting. The system implements 21 formal behavioral invariants ensuring predictable, safe lifelong learning. Key features include: ultra-sparse memory with O(log N) scaling (validated up to 1M neurons), deterministic reasoning with bit-identical replay, quantum-inspired computation achieving 47.6x speedup, and non-regression learning with formal proof. The architecture combines 16-qubit circuit simulation with biologically-motivated spiking neural networks (LIF neurons) and includes 105+ pure ML algorithms with zero external framework dependencies. Complete validation across 36 tests demonstrates 100% invariant compliance. The 52-page paper provides comprehensive mathematical foundations, 5 rigorous proofs, experimental results, and full implementation details. Applications include continual learning research, safe autonomous systems, and reproducible ML with audit trails