AICYHCJul 2

Copewell: A Multi-Agent Swarm Architecture for Equitable Mental Wellness Support

arXiv:2607.022450.9
Predicted impact top 100% in AI · last 90 daysOriginality Synthesis-oriented
AI Analysis

This work aims to expand equitable access to mental health support for underserved populations, but the contribution is incremental as it combines existing techniques without empirical validation.

Copewell introduces a multi-agent swarm system for mental wellness support, addressing the treatment gap in low- and middle-income countries. The architecture integrates multi-source assessment, emotion mapping, and dual-mode interventions, but no concrete performance numbers are provided.

Mental health disorders affect nearly one billion people globally, yet 75% of individuals in low- and middle-income countries receive no treatment due to workforce shortages, cost barriers, and stigma. Current AI-powered wellness solutions predominantly rely on single-mode conversational interfaces that suffer high abandonment rates and fail to provide measurable, immediate relief calibrated to users' dynamic emotional states. This paper presents Copewell, a novel multi-agent swarm system designed to expand access to mental wellness support through human-centered AI principles. Our architecture introduces three technical innovations: (1) a multi-source assessment framework integrating self-reported, physiological, and contextual data to mitigate algorithmic bias; (2) valence-arousal emotion mapping using Russell's Circumplex Model of Affect to route users to specialized AI agents; and (3) dual-mode intervention delivery combining conversational support with evidence-based sensory wellness protocols. We examine the sociotechnical design considerations underlying Copewell's development, including a privacy-first architecture, embedded ethical oversight through a dedicated Ethics Supervisor agent, and participatory design informed by mental health practitioners. Early practitioner engagement and beta deployment inform design decisions and identify directions for future empirical evaluation. This work contributes to responsible AI discourse by demonstrating how technical architecture can operationalize equity and safety principles from inception.

Foundations

The foundational work for this paper's niche, ranked by how specifically the neighbourhood builds on it — not by global fame.

Your Notes