让AI记住用户情绪与历史，实现持续个性化对话
Emotion-Attended Stateful Memory (EASM):The Architecture for Hyper-Personalization at Scale
- 用情绪信号和长期对话历史动态构建用户专属记忆
- 记忆增强后记忆锚定提升95%，计划清晰度提高57%
- 在悲伤、焦虑等复杂情绪场景下仍表现稳定，适合高个性化需求场景
当前语言模型系统在会话间仍为无状态，难以实现长期个性化。尽管检索增强生成和微调能提升知识获取与领域能力，但无法持久理解个体用户。我们提出一种情绪感知的状态化记忆架构（EASM），在推理时结合长期历史、情绪信号和推断意图，动态构建用户专属对话上下文。通过控制A/B测试，在30次非脚本化对话中，涵盖六类情感场景，使用相同基础语言模型对比。加入记忆的条件在所有场景中均显著优于无状态基线，最大提升达记忆锚定95%、计划清晰度57%、情绪验证34%。即使在涉及悲伤、痛苦和不确定性的对抗性情绪对话中，效果依然稳定。结果表明，状态化情绪记忆或可成为超个性化AI系统的基础架构，但需更大规模多样性验证。
原文摘要 · Abstract (English)
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
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