Emotion-Attended Stateful Memory (EASM):The Architecture for Hyper-Personalization at Scale
For developers of conversational AI systems, this work addresses the limitation of stateless models in achieving hyper-personalization, but the results are based on a small-scale study (30 conversations) and require broader validation.
The paper proposes an emotion-attended stateful memory architecture for language models that enables persistent user-specific context across sessions. In a controlled A/B study, the memory-enriched condition outperformed the stateless baseline, with gains of 95% in memory grounding, 57% in plan clarity, and 34% in emotional validation.
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