AI 中文总结
该研究针对个性化生成系统中记忆使用的效用问题，提出TRACE-Memory两阶段框架，经多阶段训练后在三类数据集的4500个任务上表现优于多种记忆使用方法，支持选择性个性化。
AI 中文摘要
个性化生成系统会根据请求检索用户历史（即记忆相关性）并将其注入模型上下文。然而，相关历史可能涉及错误的偏好维度、重复的公共信息或提供的支持不足。我们认为，仅当个人记忆能在仅基于公共信息的响应之外增加效用时才应使用。我们提出TRACE-Memory，这是一个用于选择性个性化的两阶段框架。阶段1查询请求和公共上下文缺失的用户特定信息，然后检索面向覆盖范围的候选池；阶段2根据响应级增量效用接纳可溯源至来源的紧凑证据子集，或弃权（不执行）。我们通过结构化SFT初始化、降维分阶段GRPO热身以及嵌套多样本联合GRPO逐步训练查询生成和证据接纳策略。在来自Goodreads、Amazon Reviews和Reddit的4500个受控与自然任务中，TRACE-Memory始终优于随机和词汇记忆使用，优于语义检索，且随着本地生成器容量增加，仍与前沿LLM记忆管道具有竞争力，其将证据接纳建立在公共上下文充足性的基础上，支持选择性而非默认的个性化。
英文摘要
Personalized generation systems retrieve user history by request--memory relevance and inject it into the model context. Yet relevant history may concern the wrong preference aspect, duplicate public information, or provide insufficient support. We argue that personal memory should be used only when it adds utility beyond a public-only response. We propose TRACE-Memory, a two-stage framework for selective personalization. Stage 1 queries for user-specific information missing from the request and public context, then retrieves a coverage-oriented candidate pool. Stage 2 admits a compact subset of source-traceable evidence units, or the empty set, according to response-level incremental utility. We progressively train the query-generation and evidence-admission policies through structured SFT initialization, reduced-space stage-wise GRPO warm-up, and nested multi-sample Joint GRPO. Across 4,500 Controlled and Natural tasks from Goodreads, Amazon Reviews, and Reddit, TRACE-Memory consistently outperforms random and lexical memory use, improves over semantic retrieval, remains competitive with frontier-LLM memory pipelines as local generator capacity increases, and conditions evidence admission on public-context sufficiency, supporting selective rather than default personalization.
Comments9 pages, 4 figures, and 6 tables. Submitted to the 41st AAAI Conference on Artificial Intelligence (AAAI 2027)
