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

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.

Accepted for publication in IEEE Internet Computing, Special Issue on Wearable Computing (Sep/Oct 2026). 4 figures, 2 tables

Artificial Intelligence (cs.AI); Machine Learning (cs.LG)

arXiv:2609.12322 [cs.AI]

(or arXiv:2609.12322v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2609.12322 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

https://doi.org/10.1109/MIC.2026.3732091 Focus to learn more DOI(s) linking to related resources

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