Research Index

A novel framework that combines Test-Time Reinforcement Learning with multi-agent systems to develop adaptive reward functions for energy management in smart grids. The system learns to optimize both agent coordination and energy efficiency through self-evolution of reward mechanisms, addressing both the limitations of current multi-agent LLM systems and energy management challenges.

A novel approach combining bio-inspired multi-agent systems with optical wireless power transfer for dynamic EV fleet management. The system uses adaptive LED arrays and reinforcement learning to optimize both power distribution and charging schedules, while incorporating real-time fleet behavior patterns.

A novel framework combining LLM-guided reinforcement learning with dynamic wireless power transfer systems for optimizing vehicle-to-grid (V2G) energy distribution. The system uses natural language interfaces to coordinate between human operators, electric vehicles, and power grid infrastructure while incorporating real-time power transfer optimization.

A novel framework combining wireless power transfer technology with multi-agent LLM systems to create self-healing, adaptive energy distribution networks. The system uses AI to optimize power transfer paths, predict equipment failures, and automatically reroute energy flow while maintaining system stability under various attack scenarios.