Core Insights - The article discusses the introduction of the "Agentic Supernet" concept, which dynamically customizes agent teams based on task difficulty, outperforming existing methods by up to 11.82% while reducing inference costs to 45% of those methods [4][38]. Group 1: Challenges in Multi-Agent Systems - Current multi-agent systems often rely on cumbersome manual configurations and prompt engineering, leading to inefficiencies [6]. - Automated optimization methods tend to create overly complex systems that waste resources on simple tasks [7]. - There is a lack of a universal solution that excels across all tasks, resulting in task conflicts [7]. Group 2: Paradigm Shift - The paper proposes a paradigm shift from seeking a single optimal agent architecture to optimizing a probabilistic distribution of potential agent architectures [10]. - The "Agentic Supernet" serves as a vast repository of various foundational capabilities, allowing for dynamic selection and combination based on task requirements [12][20]. Group 3: MaAS Framework - The MaAS framework consists of three main strategies: defining a universal blueprint, intelligent scheduling, and self-evolution [15]. - The first step involves creating a comprehensive "Agentic Supernet" that includes all possible agent capabilities [16]. - The intelligent scheduler, or controller, dynamically selects the most suitable skill modules for each task, ensuring efficient resource allocation [21][25]. Group 4: Performance and Cost Efficiency - MaAS demonstrates superior performance across multiple benchmarks, achieving an average score of 83.59% and outperforming 14 baseline methods [32]. - The inference cost of MaAS is significantly lower, averaging only 45% of that of existing systems, with training costs also being substantially reduced [33][34]. - The framework exhibits strong generalization capabilities, effectively adapting to various tasks and domains [38].