MemFly: On-the-Fly Memory Optimization via Information Bottleneck
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.
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Zhenyuan Zhang
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Xianzhang Jia
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Zhiqin Yang
claimed_verified
2026-02-17T15:51:50.536Z
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zhiqin yang
visity
user
visity
698fd54f50fb2c0be47835f8
Zhenbo Song
698fd54f50fb2c0be47835f9
Wei Xue
698fd54f50fb2c0be47835fa
Sirui Han
698fd54f50fb2c0be47835fb
Yike Guo
MemFly: On-the-Fly Memory Optimization via Information Bottleneck
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.
MemFly addresses the challenge of long-term memory in language models by using information bottleneck principles to create an adaptive memory structure with hybrid retrieval mechanisms for improved task performance.
information bottleneck long-term memory language model agents memory coherence response fidelity hybrid retrieval mechanism semantic retrieval symbolic retrieval topological retrieval iterative refinement