English

Continual Knowledge Updating in LLM Systems: Learning Through Multi-Timescale Memory Dynamics

Machine Learning 2026-05-08 v2 Artificial Intelligence Computation and Language

Abstract

LLMs are trained once, then deployed into a world that never stops changing. External memory compensates for this, but most systems manage it explicitly rather than letting it adapt on its own. Biological memory works differently: coupled multi-timescale dynamics make new associations immediately usable, strengthen what repetition confirms, and let the rest fade. We argue that external memory should follow a similar principle. In Memini, this view takes the form of an associative memory that organizes knowledge as a directed graph. Each edge carries two coupled internal variables, one fast and one slow, following the Benna-Fusi model of synaptic consolidation. From this coupling, episodic sensitivity, gradual consolidation, and selective forgetting emerge as facets of a single mechanism, reframing external memory as a learning substrate that reorganizes through its own dynamics.

Keywords

Cite

@article{arxiv.2605.05097,
  title  = {Continual Knowledge Updating in LLM Systems: Learning Through Multi-Timescale Memory Dynamics},
  author = {Andreas Pattichis and Constantine Dovrolis},
  journal= {arXiv preprint arXiv:2605.05097},
  year   = {2026}
}

Comments

Preprint. 9 pages, 2 figures

R2 v1 2026-07-01T12:53:08.994Z