Research Index

Develop an intelligent digital twin system that combines safety-critical control theory with real-time hydrogen storage monitoring using adaptive learning algorithms. The system would integrate multiple data streams to predict and prevent hazardous conditions while optimizing storage efficiency through reinforcement learning approaches.

Develop an intelligent control system that combines machine learning, human expertise, and real-time safety monitoring for hydrogen storage facilities. The system would adapt its control parameters based on both sensor data and human operator feedback, while maintaining strict safety constraints through barrier functions.

Applying LLM metacognition principles to improve power grid stability through intelligent Dynamic Voltage Restorer (DVR) control. The system combines uncertainty-aware decision making from medical LLMs with fast terminal sliding mode control for robust, self-aware power quality management.

A novel framework integrating large language models with microgrid control systems for intelligent predictive maintenance and resource optimization. The system combines natural language processing of maintenance logs, sensor data analysis, and reinforcement learning to create self-healing power networks that can anticipate failures and automatically adjust resource allocation.

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 framework combining LLM-based reasoning with reinforcement learning to optimize microgrid energy management while adapting to real-world uncertainties. The system uses natural language processing to interpret complex grid conditions and environmental factors, then employs multi-agent RL to coordinate distributed energy resources and storage systems.

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 framework that applies token entropy patterns from LLM reasoning to guide real-time decision-making in robotic control systems. By identifying high-entropy decision points and using reinforcement learning with verifiable rewards, the system can develop efficient, explainable reasoning patterns for complex robotic tasks while maintaining real-time performance constraints.

A novel framework that dynamically allocates computational resources during LLM reasoning based on task complexity and required accuracy. By combining insights from token entropy patterns and length-controlled reasoning, the system adaptively switches between short and long-form reasoning to optimize performance while minimizing computational costs.

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 large language models and formation control for surgical robot swarms, enabling real-time adaptation to dynamic surgical environments. The system uses LLM reasoning capabilities to interpret surgical context and guide precise multi-robot coordination while maintaining safety constraints.

A novel approach combining diffusion LLMs with reinforcement learning to optimize electric vehicle charging and microgrid energy management. The system uses parallel generation capabilities of diffusion models to simulate multiple energy scenarios simultaneously while adapting to real-time grid conditions through RL-based optimization.

A novel approach combining digital twin technology with bio-inspired reinforcement learning to create self-healing protection systems for microgrids. The system learns from both simulated and real-world scenarios to adaptively optimize protection settings while maintaining grid stability, drawing inspiration from biological immune systems' ability to learn and adapt to new threats.

A novel framework combining large language model agents with distributed control systems for autonomous microgrid management. The system uses LLM-powered agents to handle both high-level planning and low-level control decisions, while leveraging hybrid-triggered communication for efficient coordination across the network.

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.

A novel framework combining length-controlled reasoning with multi-agent collaboration for efficient problem-solving. The system dynamically adjusts reasoning depth and agent interaction based on task complexity, using reinforcement learning to optimize both computational efficiency and solution accuracy.

A novel framework combining adaptive length control and multi-agent collaboration for efficient LLM reasoning. The system dynamically adjusts reasoning length and complexity based on task difficulty while leveraging specialized agent roles to optimize computation and accuracy trade-offs.