Cognibit: From Digital Exhaustion to Real-World Connection Through Gamified Territory Control and LLM-Powered Twin Networking

TL;DR

Cognibit is a social discovery platform that combines digital twins, gamification, and LLMs to evaluate compatibility and promote real-world interactions, extending simulation into a deployed environment.

Contribution

It introduces a fully deployed social discovery system using digital twins and gamification, validated on real data, revealing practical scalability challenges.

Findings

Validated on Columbia Speed Dating dataset with 551 participants.

Derived empirical cost-quality baselines for the system.

Identified fundamental scaling bottlenecks in deployment.

Abstract

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.