Research
Advancing the science of AI through rigorous research in memory systems, privacy, personalization, and human-computer interaction
Featured Research
Adaptive Memory Systems in Conversational AI
A Novel Approach to Persistent Context Management in Large Language Models
This paper presents a novel approach to implementing persistent memory systems in conversational AI that enables continuous learning and context retention across sessions. Unlike traditional stateless models, our proposed architecture maintains semantic representations of user interactions while pre...
Privacy-Preserving AI Training Methods
Federated Learning Approaches for Personal AI Systems
As AI systems become more personalized and context-aware, privacy concerns have intensified. This paper explores federated learning approaches specifically designed for personal AI systems that need to learn from user interactions without compromising privacy. We propose the Privacy-First Learning P...
User Personalization in Large Language Models
Balancing Adaptation and Generalization in AI Systems
This research investigates the optimal balance between user-specific adaptation and general capability preservation in large language models. We introduce the Personalization-Generalization Trade-off Framework (PGTF) and demonstrate how AI systems can adapt to individual users without losing their b...
All Research Papers
Adaptive Memory Systems in Conversational AI
This paper presents a novel approach to implementing persistent memory systems in conversational AI that enables continuous learning and context retention across sessions. Unlike traditional stateless...
Privacy-Preserving AI Training Methods
As AI systems become more personalized and context-aware, privacy concerns have intensified. This paper explores federated learning approaches specifically designed for personal AI systems that need t...
User Personalization in Large Language Models
This research investigates the optimal balance between user-specific adaptation and general capability preservation in large language models. We introduce the Personalization-Generalization Trade-off ...
Multimodal AI Systems: Beyond Text
Current AI systems excel in single modalities but struggle with truly integrated multimodal understanding. This paper presents the Unified Multimodal Architecture (UMA), which seamlessly processes tex...
The Economics of Personal AI Assistants
This interdisciplinary study examines the economic implications of different AI service models, comparing data-harvesting "free" services with direct-payment models. We analyze total cost of ownership...
Temporal Reasoning in AI Memory Systems
This paper addresses a critical gap in AI memory systems: understanding how user needs, preferences, and contexts evolve over time. We introduce Temporal Context Weighting (TCW), a novel approach that...
Our Research Focus
AI Memory Systems
Developing persistent, context-aware memory architectures for personalized AI experiences.
Privacy-First AI
Creating AI systems that preserve user privacy while enabling personalization and learning.
Human-AI Collaboration
Researching how AI can become true partners in human creativity and productivity.
Multimodal Intelligence
Building AI that seamlessly understands and generates across text, images, audio, and video.