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[2602.13639] Guided Collaboration in Heterogeneous LLM-Based Multi-Agent Systems via Entropy-Based Understanding Assessment and Experience Retrieval
Summary
The paper discusses a novel Entropy-Based Adaptive Guidance Framework for enhancing collaboration in heterogeneous multi-agent systems using large language models (LLMs), addressing cognitive mismatches among agents.
Why It Matters
As AI systems evolve towards multi-agent architectures, understanding how to effectively manage collaboration between agents with varying capabilities is crucial. This research provides insights into overcoming cognitive barriers, thereby improving the efficiency and robustness of AI collaborations, which has implications for various applications in AI and robotics.
Key Takeaways
- Heterogeneous multi-agent systems face cognitive mismatches that hinder collaboration.
- The proposed framework adapts guidance based on the cognitive state of agents, enhancing collaboration effectiveness.
- Experiments demonstrate that adaptive guidance can outperform traditional collaboration methods.
- Entropy metrics are utilized to assess and adjust the guidance levels for agents.
- The approach establishes a scalable pathway for robust multi-agent intelligence.
Computer Science > Artificial Intelligence arXiv:2602.13639 (cs) [Submitted on 14 Feb 2026] Title:Guided Collaboration in Heterogeneous LLM-Based Multi-Agent Systems via Entropy-Based Understanding Assessment and Experience Retrieval Authors:Linlin Wang, Tianqing Zhu, Laiqiao Qin, Longxiang Gao, Wanlei Zhou View a PDF of the paper titled Guided Collaboration in Heterogeneous LLM-Based Multi-Agent Systems via Entropy-Based Understanding Assessment and Experience Retrieval, by Linlin Wang and 4 other authors View PDF HTML (experimental) Abstract: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 framew...
