Entropy-Based Guided Collaboration in Heterogeneous LLM Multi-Agent Systems
Researchers have identified a counterintuitive phenomenon where strong-weak collaborations in heterogeneous LLM multi-agent systems often underperform compared to weak-weak combinations due to cognitive mismatches. To address this, they propose an Entropy-Based Adaptive Guidance Framework that uses multi-dimensional entropy metrics to assess agent understanding and dynamically adjust guidance intensity. Experiments on benchmark datasets demonstrate that this approach, combined with a Retrieval-Augmented Generation mechanism, significantly enhances the effectiveness and stability of heterogeneous collaboration.
- ▪The study reveals that strong-weak collaborations in heterogeneous multi-agent systems can underperform weak-weak combinations due to cognitive mismatching.
- ▪The proposed framework quantifies agent understanding using multi-dimensional entropy metrics covering expression, uncertainty, structure, coherence, and relevance.
- ▪A Retrieval-Augmented Generation mechanism is incorporated to retain successful collaboration experiences for both immediate adaptation and long-term learning.
- ▪Extensive experiments on the GSM8K, MBPP, and CVRP benchmarks show that the adaptive guidance approach consistently improves collaboration effectiveness and stability.
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| Original publisher | arXiv.org |
| Canonical URL | https://arxiv.org/abs/2602.13639 |
| Publication time | Fri, 25 Sep 2026 14:12:31 +0000 |
| Retrieval time | 2026-09-25T14:15:32.354Z |
| Last seen | 2026-09-25T14:15:32.354Z |
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| Commercial reuse | May the content be reused commercially? | Not permitted |
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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…
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