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

• URL: http://arxiv.org/abs/2609.12322v1
• Date: Fri, 11 Sep 2026 01:01:44 GMT
• Title: Affective Agent: On-Device Personalized Intervention Reasoning for Wearable Systems
• Abstract summary: We present Affective Agent, a three-layer reference architecture for personalized intervention reasoning under uncertainty on wearable-class hardware.<n>It combines a compact sub-billion- parameter language model with physiological evidence, context, and user history to decide whether, when, and how to intervene.<n>Results show that memory-driven personalization and two-pass structured reasoning improve intervention decisions within this synthetic evaluation.
• Score: 6.912208161207008
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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