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arXiv · 2609.12322

Affective Agent: On-Device Personalized Intervention Reasoning for Wearable Systems

Reina Mun·Zishen Wan·Vijay Janapa Reddi
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Abstract

Affective computing has advanced wearable state inference, but on-device reasoning about whether, when, and how to intervene remains challenging. We present Affective Agent, a three-layer reference architecture for personalized intervention reasoning under uncertainty on wearable-class hardware. It combines a compact sub-billion-parameter language model with physiological evidence, context, and user history to decide whether, when, and how to intervene, without cloud dependency or per-user retraining. The architecture is organized into three interacting layers (perception, personalization, and reasoning), adapting to individual users through host-managed structured memory evolution rather than per-user weight updates. We instantiate Affective Agent in indoor environmental quality control and evaluate it on held-out, simulator-generated longitudinal scenarios spanning physiological variation, context, signal quality, and intervention history. Results show that memory-driven personalization and two-pass structured reasoning improve intervention decisions within this synthetic evaluation. By moving the decision layer on-device, this work demonstrates a path from wearable state inference toward closed-loop, personalized intervention on wearable-class hardware.

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Reina Mun, Zishen Wan, Vijay Janapa Reddi. 2026-09-11. Affective Agent: On-Device Personalized Intervention Reasoning for Wearable Systems. https://doi.org/10.1109/mic.2026.3732091

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