AI 中文总结
ProTTT提出档案监督的元学习框架，通过测试时训练构建语义用户记忆，压缩历史以降低推理成本，优于现有基线。
AI 中文摘要
个性化要求语言模型从不断增长的用户历史中捕获用户特定知识。现有的基于上下文的方法随着用户历史的积累会增加推理成本，并依赖于单独的检索或摘要阶段，而基于参数的方法在添加新用户数据时通常需要重建用户表示。我们引入了ProTTT，一个用于学习语义用户记忆的档案监督元学习框架。记忆构建从共享初始化开始，并通过在用户历史上进行测试时训练为每个用户更新，使其能够随着历史的增长而持续演变。然而，由于仅进行测试时训练并不能明确鼓励记忆捕获个性化所需的语义用户知识，我们使用文本用户档案作为监督来学习这个共享初始化，以便在用户历史上进行测试时训练能更有效地捕获语义知识。ProTTT在多种基准测试中始终优于全历史上下文学习（ICL）和所有参数基线，同时通过将用户历史压缩为轻量级参数化记忆大幅降低推理成本。我们的分析还表明，档案监督是学习语义用户知识的可靠目标，由此产生的记忆能够跟踪和保留不断演变的用户偏好，同时在不同历史规模下保持稳健。总体而言，我们证明了测试时训练在个性化中的有效性，并将ProTTT确立为持续演变用户记忆的基线。
英文摘要
Personalization requires language models to capture user-specific knowledge from a growing user history. Existing context-based approaches incur increasing inference costs as user history accumulates and rely on separate retrieval or summarization stages, while parametric-based approaches often require reconstructing user representations when new user data is added. We introduce ProTTT, a profile-supervised meta-learning framework for learning semantic user memory. The memory construction starts from a shared initialization and is updated for each user through test-time training on user history, allowing it to evolve continuously as the history grows. However, since test-time training alone does not explicitly encourage the memory to capture semantic user knowledge necessary for personalization, we learn this shared initialization using textual user profiles as supervision, so that test-time training on user history captures semantic knowledge more effectively. ProTTT consistently outperforms both full history ICL and all parametric baselines across diverse benchmarks, while substantially reducing inference cost by compressing user history into a lightweight parameterized memory. Our analysis also shows that profile supervision is a reliable objective for learning semantic user knowledge and that the resulting memory can track and retain evolving user preferences, while remaining robust across different history sizes. Overall, we demonstrate the effectiveness of test-time training for personalization and establish ProTTT as a baseline for continuously evolving user memory.
