User behavior modeling lies at the heart of personalized applications like recommender systems. With LLM-based agents, user preference representation has evolved from latent embeddings to semantic memory. While existing memory mechanisms show promise in textual dialogues, modeling non-textual behaviors remains challenging, as preferences must be inferred from implicit signals like clicks without ground truth supervision. Current approaches rely on a single unstructured summary, updated through simple overwriting. However, this is suboptimal: users exhibit multi-faceted interests that get conflated, preferences evolve yet naive overwriting causes forgetting, and sparse individual interactions necessitate collaborative signals. We present STEAM (\textit{\textbf{ST}ructured and \textbf{E}volving \textbf{A}gent \textbf{M}emory}), a novel framework that reimagines how agent memory is organized and updated. STEAM decomposes preferences into atomic memory units, each capturing a distinct interest dimension with explicit links to observed behaviors. To exploit collaborative patterns, STEAM organizes similar memories across users into communities and generates prototype memories for signal propagation. The framework further incorporates adaptive evolution mechanisms, including consolidation for refining memories and formation for capturing emerging interests. Experiments on three real-world datasets demonstrate that STEAM substantially outperforms state-of-the-art baselines in recommendation accuracy, simulation fidelity, and diversity.

翻译：用户行为建模是个性化应用（如推荐系统）的核心。随着基于大语言模型的智能体发展，用户偏好表征已从潜在嵌入演变为语义记忆。虽然现有记忆机制在文本对话中展现出潜力，但建模非文本行为仍具挑战性，因为偏好必须从点击等隐式信号中推断，且缺乏真实监督。当前方法依赖单一非结构化摘要，通过简单覆写进行更新。然而，这种方式存在不足：用户的多方面兴趣被混淆；偏好会演进但简单覆写会导致遗忘；稀疏的个体交互需要协同信号。本文提出STEAM（结构化演进智能体记忆），这是一个重新构想智能体记忆组织与更新方式的新框架。STEAM将偏好分解为原子记忆单元，每个单元捕获一个独立的兴趣维度，并与观测到的行为建立显式关联。为利用协同模式，STEAM将跨用户的相似记忆组织为社群，并生成原型记忆以实现信号传播。该框架进一步整合了自适应演进机制，包括用于精炼记忆的巩固机制和用于捕获新兴兴趣的形成机制。在三个真实数据集上的实验表明，STEAM在推荐准确性、模拟保真度和多样性方面显著优于当前最先进的基线方法。