A research paper proposes an Entropy-Based Adaptive Guidance Framework to improve collaboration in heterogeneous multi-agent systems with different-capability LLMs. The framework uses entropy metrics to assess agent understanding and dynamically adjusts guidance intensity, combined with RAG mechanisms for experience retention. Experiments on GSM8K, MBPP, and CVRP benchmarks demonstrate improved effectiveness and stability in heterogeneous agent cooperation.