Emotion-Attended Stateful Memory (EASM):The Architecture for Hyper-Personalization at Scale
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866914567431389184 |
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| author | Kotecha, Vineet Gupta, Vansh |
| author_facet | Kotecha, Vineet Gupta, Vansh |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_14833 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Emotion-Attended Stateful Memory (EASM):The Architecture for Hyper-Personalization at Scale Kotecha, Vineet Gupta, Vansh Artificial Intelligence Human-Computer Interaction I.2.7; H.5.2; I.2.6 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 |
| title | Emotion-Attended Stateful Memory (EASM):The Architecture for Hyper-Personalization at Scale |
| topic | Artificial Intelligence Human-Computer Interaction I.2.7; H.5.2; I.2.6 |
| url | https://arxiv.org/abs/2605.14833 |