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MASPOB: Bandit-Based Prompt Optimization for Multi-Agent Systems with Graph Neural Networks

arXiv – CS AI|Zhi Hong, Qian Zhang, Jiahang Sun, Zhiwei Shang, Mingze Kong, Xiangyi Wang, Yao Shu, Zhongxiang Dai|March 4, 2026 at 05:00 AM|1 views

🤖AI Summary

Researchers introduce MASPOB, a bandit-based framework that optimizes prompts for Multi-Agent Systems using Graph Neural Networks to handle topology-induced coupling. The system reduces search complexity from exponential to linear while achieving state-of-the-art performance across benchmarks.

Key Takeaways

→MASPOB uses Upper Confidence Bound bandits to balance exploration and exploitation for sample-efficient prompt optimization.
→Graph Neural Networks are integrated to capture structural priors and learn topology-aware representations of prompt semantics.
→The framework employs coordinate ascent to reduce search complexity from exponential to linear scale.
→Multi-Agent Systems performance is highly sensitive to input prompts, making optimization crucial for deployment.
→Extensive experiments show MASPOB consistently outperforms existing baselines across diverse benchmarks.

#multi-agent-systems #prompt-optimization #graph-neural-networks #bandits #llm #machine-learning #research #optimization #ai-systems

Read Original →via arXiv – CS AI

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