AI 中文总结
研究针对LLM智能体个性化记忆的现有基准不足，提出FinPerMA基准，在276个角色的2994个问题上测试7种LLM，发现其记忆配置远未饱和，简单检索优于专用记忆系统且冲击后差距扩大。
AI 中文摘要
大型语言模型（LLM）智能体越来越多地被用作高风险领域（如金融咨询）的个性化助手，但目前仍不清楚它们能否在长期过程中维护和更新个性化用户模型。现有的个性化记忆基准主要测试事实保留能力，或依赖约束较弱的模型生成轨迹，而事件驱动的偏好适应问题尚未得到充分探索。我们提出FinPerMA，这是一个基于事件的基准，针对冻结的纵向投资者轨迹评估个性化记忆。其生成流程结合了确定性的、基于理论的影响规则、受控的LLM叙事以及自动化质量筛选；冲击后检查点用于隔离智能体是否已将重要事件整合到其持久用户模型中。在来自276个角色的2994个问题上，7种前沿LLM及多达7种记忆配置均远未达到饱和：无全上下文配置的整体准确率超过约0.47，多项选择题准确率超过约39%。归因分析显示，基于摘要的记忆通常保留事实细节，但会丢失个性化所需的偏好信号；因此，简单检索可优于专门构建的记忆系统，且在冲击后差距会进一步扩大。
英文摘要
Large language model (LLM) agents are increasingly used as personalized assistants in high-stakes domains such as financial advising, yet it remains unclear whether they can maintain and update an individualized user model over long horizons. Existing personalized-memory benchmarks primarily test factual retention or rely on weakly constrained model-generated trajectories, leaving event-driven preference adaptation underexplored. We introduce FinPerMA, an event-grounded benchmark that evaluates personalized memory against frozen longitudinal investor trajectories. Its generation pipeline combines deterministic, theory-informed impact rules, controlled LLM narration, and automated quality screening; a Post-Shock checkpoint isolates whether an agent has integrated a material event into its persistent user model. On 2,994 questions from 276 personas, seven frontier LLMs and up to seven memory configurations remain far from saturated: no full-context configuration exceeds approximately 0.47 overall accuracy or approximately 39% on multiple-choice questions. Attribution analysis shows that summary-based memory often preserves factual details while losing the preference signals needed for personalization; simple retrieval can therefore outperform purpose-built memory systems, with the gap widening after shocks.
