Research
Our team publishes research on AI agents, memory systems, proactive engagement, and the application of conversational AI to real-world challenges.
Multi-Modal Character Generation for Consistent AI Personas
Creating believable AI agents requires consistency across multiple modalities including text, voice, and visual representation. We present a unified framework for generating and maintaining coherent character attributes across all interaction channels. Our approach enables the creation of AI personas that users can form genuine connections with through consistent personality, appearance, and voice characteristics.
Persistent Memory Architecture in Conversational AI
We present a novel architecture for implementing persistent memory in conversational AI agents. Our approach enables agents to maintain coherent long-term relationships with users by storing, retrieving, and reasoning about past interactions. We demonstrate significant improvements in user satisfaction and engagement metrics compared to stateless conversation models.
Proactive Engagement Systems for AI Companions
Current AI assistants operate in a purely reactive mode, responding only when prompted. We present a framework for proactive engagement that enables AI agents to initiate meaningful conversations based on context, timing, and user needs. Our system demonstrates how AI companions can provide value through timely outreach while respecting user boundaries and preferences.
Addressing the Loneliness Epidemic Through AI Companionship
Loneliness has reached epidemic proportions, with significant impacts on mental and physical health. We explore how AI companions can serve as a complementary resource in addressing social isolation. Our research examines the design principles, ethical frameworks, and measurable outcomes of AI systems designed to provide meaningful social connection while encouraging rather than replacing human relationships.