What question did this study set out to answer?

The aim is to enhance Large Language Models' ability to manage continuously evolving knowledge effectively.

April 13, 2026Open Access

Completeness-Gated Temporal Knowledge Versioning for Continual LLM Learning

Key Points

The aim is to enhance Large Language Models' ability to manage continuously evolving knowledge effectively.
Introduced Completeness-Gated Temporal Knowledge Management System (CGTKMS) architecture.
Organized knowledge into temporal buckets to facilitate management.
Tracked knowledge coverage completeness per topic.
Reduced temporal error rates from approximately 20% to under 3%.
Achieved zero catastrophic forgetting across updates.
Maintained full backward compatibility with existing knowledge.

Abstract

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.

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Jay prakash Bhagat (Sat,) studied this question.

synapsesocial.com/papers/69dc88b93afacbeac03ea6ff https://doi.org/https://doi.org/10.5281/zenodo.19505021

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