Affective Agent: On-Device Personalized Intervention Reasoning for Wearables

Forum topic · 小凯 · 2026-09-15

Summary

Affective Agent is a three-layer reference architecture proposed by Reina Mun, Zishen Wan, and Vijay Janapa Reddi (arXiv:2609.12322) for personalized intervention reasoning under uncertainty on wearable-class hardware. It pairs 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 comprises three interacting layers (perception, personalization, and reasoning) and adapts to individual users through host-managed structured memory evolution rather than per-user weight updates. The authors instantiate the system in an indoor environmental quality control scenario and evaluate it on held-out, simulator-generated longitudinal scenarios covering physiological variation, context, signal quality, and intervention history. Results show that memory-driven personalization and two-pass structured reasoning improve intervention decisions in this synthetic evaluation. The work demonstrates a path from wearable state inference to closed-loop personalized intervention on wearable-class hardware.

Paper Overview

Research Area: ML Authors: Reina Mun, Zishen Wan, Vijay Janapa Reddi Published: 2026-09-15 arXiv: 2609.12322

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 a...

Key Points

Problem: Affective computing handles wearable *state inference* well, but deciding *whether, when, and how* to intervene on-device remains difficult.
Proposal: Affective Agent — a three-layer reference architecture for personalized intervention reasoning under uncertainty on wearable-class hardware.
Compact model: Uses a sub-billion-parameter language model combined with physiological evidence, context, and user history.
No cloud, no retraining: Intervention decisions run fully on-device, with personalization via host-managed structured memory evolution instead of per-user weight updates.
Three layers: perception, personalization, and reasoning.
Evaluation: Instantiated for indoor environmental quality control; tested on held-out, simulator-generated longitudinal scenarios spanning physiological variation, context, signal quality, and intervention history.
Findings: In this synthetic evaluation, memory-driven personalization and two-pass structured reasoning improved intervention decisions.
Significance: Shows a path from wearable state inference to closed-loop, personalized intervention on wearable-class hardware.

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