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

• URL: http://arxiv.org/abs/2602.07885v1
• Date: Sun, 08 Feb 2026 09:37:25 GMT
• Title: MemFly: On-the-Fly Memory Optimization via Information Bottleneck
• Authors: Zhenyuan Zhang, Xianzhang Jia, Zhiqin Yang, Zhenbo Song, Wei Xue, Sirui Han, Yike Guo,
• Abstract summary: Long-term memory enables large language model agents to tackle complex tasks through historical interactions.<n>Existing frameworks encounter a dilemma between compressing redundant information efficiently and maintaining precise retrieval for downstream tasks.<n>MemFly is a framework grounded in information bottleneck principles that facilitates on-the-fly memory evolution for LLMs.<n>MemFly substantially outperforms state-of-the-art baselines in memory coherence, response fidelity, and accuracy.
• Score: 35.420309099411874
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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