With recent breakthroughs in large language models (LLMs) for reasoning, planning, and complex task generation, artificial intelligence systems are transitioning from isolated single-agent architectures to multi-agent systems with collaborative intelligence. However, in heterogeneous multi-agent systems (HMAS), capability differences among agents give rise to consistent cognitive problems, where strong and weak models fail to contribute effectively. We define the collaboration as a strong-weak system. Through comprehensive experiments, we disclose a counterintuitive phenomenon in the strong-weak system: a strong-weak collaboration may under-perform weak-weak combinations, revealing that cognitive mismatching are key bottlenecks limiting heterogeneous cooperation. To overcome these challenges, we propose an Entropy-Based Adaptive Guidance Framework that dynamically aligns the guidance with the cognitive state of each agent. The framework quantifies the understanding of weak agents through multi-dimensional entropy metrics - covering expression, uncertainty, structure, coherence, and relevance - and adaptively adjusts the intensity of the guidance at light, moderate and intensive levels. Furthermore, a Retrieval-Augmented Generation (RAG) mechanism is incorporated to retain successful collaboration experiences, enabling both immediate adaptation and long-term learning. Extensive experiments on three benchmark datasets, GSM8K, MBPP, and CVRP demonstrate that our approach consistently enhances the effectiveness and stability of heterogeneous collaboration. The results highlight that adaptive guidance not only mitigates cognitive imbalance but also establishes a scalable pathway toward more robust, cooperative multi-agent intelligence.

翻译：随着大语言模型在推理、规划及复杂任务生成方面取得突破性进展，人工智能系统正从孤立的单智能体架构向具备协作智能的多智能体系统演进。然而，在异构多智能体系统中，智能体间的能力差异引发了一致的认知问题，导致强模型与弱模型均无法有效贡献。我们将此类协作定义为强弱系统。通过大量实验，我们揭示了强弱系统中一个反直觉的现象：强弱协作的表现可能弱于弱弱组合，这表明认知错配是限制异构协作的关键瓶颈。为应对这些挑战，我们提出了一种基于熵的自适应引导框架，该框架能动态调整引导策略以匹配各智能体的认知状态。该框架通过多维度熵指标——涵盖表达熵、不确定熵、结构熵、连贯熵与相关熵——量化弱智能体的理解程度，并以轻度、中度与强度三个层级自适应调节引导强度。此外，我们引入检索增强生成机制来保留成功的协作经验，实现即时适应与长期学习。在GSM8K、MBPP和CVRP三个基准数据集上的大量实验表明，我们的方法持续提升了异构协作的效能与稳定性。结果突出表明，自适应引导不仅能缓解认知失衡，还为构建更鲁棒、更协同的多智能体智能开辟了可扩展的路径。