重新思考记忆：持续演变的连接性

摘要

现有基于记忆增强的大语言模型智能体通常将记忆视为静态存储库，采用预定义表示和固定检索流水线，这在动态智能体环境中显得脆弱——因为反馈、任务变化及异构信号会持续重塑应该记住什么以及如何建立记忆间的关联。为此，我们提出FluxMem——一种连接演化的记忆框架，将记忆构建为异构图表征，并通过三个阶段逐步优化其拓扑结构：初始连接形成、反馈驱动优化和长期巩固。在执行过程中，FluxMem修复缺失连接、剪除干扰、对齐抽象粒度，并将重复发生的成功轨迹提炼为可复用的程序化回路，整个过程受记忆泛化性和演化成熟度两项指标引导。在LoCoMo、Mind2Web和GAIA这三个本质差异显著的基准测试中，FluxMem均取得了持续最优性能，展现出在复杂智能体环境中强大的适应与泛化能力。相关代码将开源至https://github.com/zjunlp/LightMem。

English

Existing memory-augmented LLM agents often treat memory as a static repository with pre-defined representations and fixed retrieval pipelines, which is brittle in dynamic agentic environments where feedback, task variation, and heterogeneous signals continuously reshape what should be remembered and how it should be connected. To address this, we propose FluxMem, a connectivity-evolving memory framework that models memory as a heterogeneous graph and progressively refines its topology through three stages: initial connection formation, feedback-driven refinement, and long-term consolidation. During execution, FluxMem repairs missing links, prunes interference, aligns abstraction granularity, and distills recurrent successful trajectories into reusable procedural circuits, guided by one metric for memory generalizability and evolutionary maturity. Across three fundamentally distinct benchmarks including LoCoMo, Mind2Web, and GAIA, FluxMem achieves consistent state-of-the-art performance, demonstrating strong adaptation and generalization in complex agentic environments. The code will be open-sourced in https://github.com/zjunlp/LightMem.