Research Index

Applying large language model memory architectures to enhance power grid management and renewable energy integration. The approach combines HippoRAG's associative memory techniques with non-linear control systems to create an adaptive, predictive grid management system that can handle complex multi-energy scenarios while optimizing for efficiency and stability.

A novel framework combining metacognitive assessment capabilities with retrieval-augmented generation (RAG) for creating more reliable healthcare AI assistants. The system continuously evaluates its own knowledge limitations and uncertainty while building an evolving knowledge graph of medical information, enabling more trustworthy clinical decision support.

A novel framework combining reinforcement learning with knowledge graphs to train LLMs in healthcare to develop accurate metacognitive abilities. The system learns to calibrate confidence levels, recognize knowledge boundaries, and identify situations requiring human intervention through iterative self-evaluation and external validation.

A novel defense mechanism against data poisoning in medical LLMs using iterative diffusion models to detect and filter malicious training data. The approach combines knowledge graph validation with discrete diffusion modeling to create a robust verification layer that can identify and neutralize poisoned data before model training.

A novel framework combining ThinkPrune's reasoning optimization with HealthBench's medical evaluation metrics to create resource-efficient medical LLMs for clinical deployment. The system dynamically adjusts reasoning depth based on task complexity and available computational resources while maintaining safety through biomedical knowledge graph verification.