Emotion-Attended Stateful Memory (EASM):The Architecture for Hyper-Personalization at Scale

ArXi:2605.14833v1 Announce Type: new 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.