Continual Knowledge Updating in LLM Systems: Learning Through Multi-Timescale Memory Dynamics
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...
| Authors | Andreas Pattichis, Constantine Dovrolis |
|---|---|
| Primary category | cs.LG |
| Published | 6 May 2026 |
| Last updated at source | 24 June 2026 |
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