Cognibit: From Digital Exhaustion to Real-World Connection Through Gamified Territory Control and LLM-Powered Twin Networking
This work addresses social connection for users by integrating simulation and gamification, though it appears incremental as it builds on prior simulation-only matching methods.
The paper tackles the problem of social discovery by developing an LLM-powered platform that uses digital twins to simulate conversations for compatibility estimation and gamified territory control to encourage real-world interactions, achieving validation on a dataset of 551 participants and identifying scaling bottlenecks in deployment.
We present an LLM-powered social discovery platform that uses digital twins to autonomously evaluate interpersonal compatibility through behavioral simulation. The platform unifies three key pillars: (1) digital twins that engage in autonomous multi-turn conversations on behalf of users to estimate compatibility, (2) gamified territory conquest mechanics that incentivize real-world exploration and create organic settings for in-person encounters, and (3) AI companions that preserve persistent shared memory across devices. Built upon CogniPair's cognitive architecture (Ye et al., 2026), validated on the Columbia Speed Dating dataset (551 participants), our system extends prior simulation-only matching into a fully deployed social discovery environment. Through deployment, we derive empirical cost-quality baselines and identify fundamental scaling bottlenecks that remain hidden in component-level testing alone.