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
The increasing scale and complexity of LLM deployments highlight the pressing need for more adaptive and efficient knowledge management strategies that move beyond static training cycles.
Improving LLM adaptability to real-time changing information is crucial for maintaining relevance and accuracy, especially in dynamic operational environments where continuous learning is paramount.
Traditional LLM knowledge updating, which is often a manual or episodic retraining process, may evolve towards more autonomous, biological-inspired memory dynamics, enabling continuous adaptation.
- · AI system developers
- · Enterprises deploying LLMs in fast-changing sectors
- · Research institutions focused on cognitive AI
- · Legacy AI update methodologies
- · Static knowledge base providers (if not adaptable)
- · LLM systems lacking continuous learning mechanisms
LLMs can maintain higher accuracy and relevance over longer periods without frequent, expensive retraining cycles.
This could accelerate the adoption of autonomous AI agents by improving their ability to navigate and act in dynamic, real-world contexts.
The development of truly 'living' AI knowledge systems might begin to blur the lines between static algorithms and adaptive, evolving intelligences.
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Read at arXiv cs.LG