Current language model systems remain fundamentally stateless across sessions, limiting their ability to personalize interactions over time. While retrieval-augmented generation and fine-tuning improve knowledge access and domain capability, they do not enable persistent understanding of individual users. We propose an emotion-attended stateful memory architecture that dynamically constructs user-specific conversational context using long-term history, emotional signals, and inferred intent at inference time. To evaluate its impact, we conducted a controlled A/B study across thirty non-scripted conversations spanning six emotionally distinct categories using the same underlying language model in both conditions. The memory-enriched condition consistently outperformed the stateless baseline across all evaluated scenarios. The largest gains were observed in memory grounding (95% improvement), plan clarity (57%), and emotional validation (34%). Results remained consistent even in emotionally adversarial conversations involving grief, distress, and uncertainty. These findings suggest that stateful emotional memory may represent a foundational infrastructure layer for hyper-personalized AI systems, though broader validation across larger and more diverse evaluations remains necessary

翻译：当前语言模型系统在会话间本质上仍保持无状态，这限制了其随时间积累个性化交互的能力。尽管检索增强生成与微调技术提升了知识获取与领域能力，但未能实现对个体用户的持续理解。我们提出一种情感关注的状态化记忆架构，能在推理时通过长期历史、情感信号与推断意图动态构建用户专属对话上下文。为评估其影响，我们在同一底层语言模型条件下，围绕六类情感截然不同的对话场景开展了三十场非脚本对话的受控A/B实验。结果表明，具备记忆增强的条件在所有评估场景中均稳定优于无状态基线。最大提升体现在记忆锚定（提升95%）、计划清晰度（提升57%）与情感验证（提升34%）指标上。即便在涉及悲伤、痛苦与不确定性的情感对抗性对话中，结果仍保持一致性。这些发现表明，状态化情感记忆可能构成超个性化AI系统的基础设施层，但仍需在更大规模、更多样化的评估中进一步验证其普适性。