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

arXiv:2605.05097v3 Announce Type: replace 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

Source: arXiv cs.LG — read the full report at the original publisher.

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