MemFly: On-the-Fly Memory Optimization via Information Bottleneck

Abstract:Long-term memory enables large language model agents to tackle complex tasks through historical interactions. However, existing frameworks encounter a fundamental dilemma between compressing redundant information efficiently and maintaining precise retrieval for downstream tasks. To bridge this gap, we propose MemFly, a framework grounded in information bottleneck principles that facilitates on-the-fly memory evolution for LLMs. Our approach minimizes compression entropy while maximizing relevance entropy via a gradient-free optimizer, constructing a stratified memory structure for efficient storage. To fully leverage MemFly, we develop a hybrid retrieval mechanism that seamlessly integrates semantic, symbolic, and topological pathways, incorporating iterative refinement to handle complex multi-hop queries. Comprehensive experiments demonstrate that MemFly substantially outperforms state-of-the-art baselines in memory coherence, response fidelity, and accuracy.

Artificial Intelligence (cs.AI); Machine Learning (cs.LG)

arXiv:2602.07885 [cs.AI]

(or arXiv:2602.07885v2 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2602.07885 Focus to learn more arXiv-issued DOI via DataCite

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