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 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.