CogniPair: From LLM Chatbots to Conscious AI Agents -- GNWT-Based Multi-Agent Digital Twins for Social Pairing -- Dating & Hiring Applications
Journal:
arXiv
Published Date:
Jun 4, 2025
Abstract
Current large language model (LLM) agents lack authentic human psychological
processes necessary for genuine digital twins and social AI applications. To
address this limitation, we present a computational implementation of Global
Workspace Theory (GNWT) that integrates human cognitive architecture principles
into LLM agents, creating specialized sub-agents for emotion, memory, social
norms, planning, and goal-tracking coordinated through a global workspace
mechanism. However, authentic digital twins require accurate personality
initialization. We therefore develop a novel adventure-based personality test
that evaluates true personality through behavioral choices within interactive
scenarios, bypassing self-presentation bias found in traditional assessments.
Building on these innovations, our CogniPair platform enables digital twins to
engage in realistic simulated dating interactions and job interviews before
real encounters, providing bidirectional cultural fit assessment for both
romantic compatibility and workplace matching. Validation using 551 GNWT-Agents
and Columbia University Speed Dating dataset demonstrates 72% correlation with
human attraction patterns, 77.8% match prediction accuracy, and 74% agreement
in human validation studies. This work advances psychological authenticity in
LLM agents and establishes a foundation for intelligent dating platforms and HR
technology solutions.
