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.

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.