DYNA : Dynamic Episodic Memory Networks for Augmenting Large Language Models with Temporal Knowledge Graphs in Continuous Learning
arXiv:2606.15778v1 Announce Type: new Abstract: Large Language Models (LLMs) struggle to incorporate new knowledge without forgetting or costly retraining. We propose DYNA, a lightweight framework that augments a frozen LLM with a temporal knowledge graph where events are nodes and temporal relations are directed, timestamped edges. The graph serves as an external, updatable memory. At query time, DYNA retrieves relevant nodes via random walks and centrality measures, then augments the LLM's response. Evaluated on three temporal recall tasks, DYNA reduces catastrophic forgetting by ~7% compare
The rapid advancement and widespread deployment of LLMs highlight their limitations in integrating new, temporal knowledge efficiently without prohibitive computational costs or performance degradation.
This development addresses a core architectural challenge for LLMs, enabling continuous learning and reducing catastrophic forgetting, crucial for their long-term applicability in dynamic environments.
LLMs can now be augmented with external, updatable memory systems, allowing them to remain current and contextually relevant without constant, expensive retraining.
- · AI developers
- · Enterprises deploying LLMs
- · Knowledge management platforms
- · LLM retraining services
- · Traditional static knowledge bases
LLMs become more agile and adaptable to real-time information, extending their practical lifespan and reducing operational overhead.
This could accelerate the development of more sophisticated AI agents capable of learning and adapting continuously in complex environments.
The ability of LLMs to maintain up-to-date knowledge might lead to new forms of autonomous decision-making systems that are less prone to outdated information.
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Read at arXiv cs.CL