QNLLM v3.1: A Formally Verifiable Continual Learning System with 21 Behavioral Invariants
Abstract
QNLLM is a specialized continual-learning language model designed around formal neurological learning laws and 21 verified invariants. The system emphasizes deterministic reasoning, adaptive memory, non-regression learning, provenance-tracked outputs, and bounded generation. This release provides full specifications, invariant proofs, and reproducible documentation without requiring pretrained LLM weights or large-scale compute infrastructure.
Fields of Study & Subjects
Quantum Computing Algorithms and Architecture
Domain Adaptation and Few-Shot Learning
Advanced Memory and Neural Computing
Publication details
- Journal / source
- Zenodo (CERN European Organization for Nuclear Research)
- Publication date
- 1 January 2026
- Language
- EN