Research Index

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.