Completeness-Gated Temporal Knowledge Versioning for Continual LLM Learning

Current Large Language Models are frozen in time, producing erroneous answers on temporally evolving knowledge at rates between 18–23%. This paper presents the Completeness-Gated Temporal Knowledge Management System (CGTKMS), a novel architecture that moves knowledge management outside the LLM into a dedicated Management Neuron. The system organizes knowledge in temporal buckets, tracks coverage completeness per topic, and only archives old knowledge when its replacement is verified as complete. Projected result: temporal error rates reduced from ~20% to under 3%, with zero catastrophic forgetting and full backward compatibility.