Emotion-Attended Stateful Memory (EASM):The Architecture for Hyper-Personalization at Scale
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 m...
| Authors | Vineet Kotecha, Vansh Gupta |
|---|---|
| Primary category | cs.AI |
| Published | 14 May 2026 |
| Last updated at source | 14 May 2026 |
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