标题： 内心之外：为长期个性化对话系统演变以用户为中心的核心记忆树 显示英文标题

标题： Inside Out: Evolving User-Centric Core Memory Trees for Long-Term Personalized Dialogue Systems

摘要： 现有的长期个性化对话系统难以在无限制的交互流与有限的上下文约束之间取得平衡，常常导致记忆噪声积累、推理退化和个性不一致。 为解决这些挑战，本文提出了Inside Out，一种利用全局维护的PersonaTree作为长期用户画像载体的框架。 通过用初始模式约束主干，并更新分支和叶子，PersonaTree实现了可控的增长，在保持一致性的同时实现记忆压缩。 此外，我们通过基于过程的奖励进行强化学习训练了一个轻量级的MemListener，以生成结构化、可执行和可解释的{添加，更新，删除，无操作}操作，从而支持个性化树的动态演化。 在生成响应时，PersonaTree被直接用于在延迟敏感的场景中增强输出；当用户需要更多细节时，代理模式被触发，在PersonaTree的约束下按需引入细节。 实验表明，PersonaTree在抑制上下文噪声和保持个性一致性方面优于全文拼接和各种个性化记忆系统。 值得注意的是，小型的MemListener模型在记忆操作决策方面达到了与强大推理模型如DeepSeek-R1-0528和Gemini-3-Pro相当甚至更优的性能。 显示英文摘要

摘要： Existing long-term personalized dialogue systems struggle to reconcile unbounded interaction streams with finite context constraints, often succumbing to memory noise accumulation, reasoning degradation, and persona inconsistency. To address these challenges, this paper proposes Inside Out, a framework that utilizes a globally maintained PersonaTree as the carrier of long-term user profiling. By constraining the trunk with an initial schema and updating the branches and leaves, PersonaTree enables controllable growth, achieving memory compression while preserving consistency. Moreover, we train a lightweight MemListener via reinforcement learning with process-based rewards to produce structured, executable, and interpretable {ADD, UPDATE, DELETE, NO_OP} operations, thereby supporting the dynamic evolution of the personalized tree. During response generation, PersonaTree is directly leveraged to enhance outputs in latency-sensitive scenarios; when users require more details, the agentic mode is triggered to introduce details on-demand under the constraints of the PersonaTree. Experiments show that PersonaTree outperforms full-text concatenation and various personalized memory systems in suppressing contextual noise and maintaining persona consistency. Notably, the small MemListener model achieves memory-operation decision performance comparable to, or even surpassing, powerful reasoning models such as DeepSeek-R1-0528 and Gemini-3-Pro.