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EM-LLM: Human-Inspired Episodic Memory for Infinite Context LLMs

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In this work, we introduce EM-LLM, an architecture that integrates key aspects of human episodic memory and event cognition into LLMs with no fine-tuning, enabling them to handle practically infinite context lengths while maintaining computational efficiency. EM-LLM organises sequences of tokens into coherent episodic events using a combination of Bayesian surprise and graph-theoretic boundary refinement in an online fashion. Experiments on the LongBench and $\infty$-Bench benchmarks demonstrate EM-LLM's superior performance, consistently outperforming the SOTA retrieval model InfLLM across various baseline LLMs.

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