Generative AI Research
Advancing the frontier of language models and intelligent systems
Our Generative AI research focuses on pushing the boundaries of what's possible with large language models, from enhancing their memory capabilities to building sophisticated agent systems that can autonomously solve complex problems.
Active Research Areas
Memory Engineering
Developing advanced memory architectures for LLMs to maintain context over extended interactions
- •Long-term memory systems
- •Context compression techniques
- •Semantic memory indexing
- •Memory retrieval optimization
RAG Systems
Building robust Retrieval-Augmented Generation pipelines for enhanced knowledge integration
- •Vector database optimization
- •Hybrid search strategies
- •Document chunking algorithms
- •Real-time knowledge updates
LLM Optimization
Fine-tuning and optimizing large language models for specialized domains
- •Parameter-efficient fine-tuning
- •Model quantization techniques
- •Inference acceleration
- •Multi-modal integration
Agent Systems
Creating autonomous AI agents capable of complex reasoning and tool use
- •Tool-use frameworks
- •Multi-agent coordination
- •Goal-oriented planning
- •Self-improvement mechanisms
Current Projects
TigerStyle Coding Framework
An innovative approach to code generation that combines advanced prompting techniques with memory-augmented models to produce more reliable and maintainable code.
Recent Publications
Memory-Augmented RAG: A Novel Approach to Knowledge Integration
Forthcoming, 2025
Efficient Fine-tuning Strategies for Domain-Specific LLMs
In Review