Research Index

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 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.