Recent advancements in Large Language Models (LLMs) have greatly extended the capabilities of Multi-Agent Systems (MAS), demonstrating significant effectiveness across a wide range of complex and open-ended domains. However, despite this rapid progress, the field still relies heavily on empirical trial-and-error. It lacks a unified and principled scientific framework necessary for systematic optimization and improvement. This bottleneck stems from the ambiguity of attribution: first, the absence of a structured taxonomy of factors leaves researchers restricted to unguided adjustments; second, the lack of a unified metric fails to distinguish genuine collaboration gain from mere resource accumulation. In this paper, we advocate for a transition to design science through an integrated framework. We advocate to establish the collaboration gain metric ($Γ$) as the scientific standard to isolate intrinsic gains from increased budgets. Leveraging $Γ$, we propose a factor attribution paradigm to systematically identify collaboration-driving factors. To support this, we construct a systematic MAS factor library, structuring the design space into control-level presets and information-level dynamics. Ultimately, this framework facilitates the transition from blind experimentation to rigorous science, paving the way towards a true science of Collective AI.

翻译：近年来，大型语言模型（LLMs）的进展极大地拓展了多智能体系统（MAS）的能力，在广泛复杂且开放式的领域中展现出显著成效。然而，尽管发展迅速，该领域仍严重依赖经验性试错，缺乏系统优化与改进所必需的统一且原则性的科学框架。这一瓶颈源于归因的模糊性：首先，缺乏结构化的因素分类体系使得研究者局限于无导向的调整；其次，缺乏统一度量标准，难以区分真正的协作增益与单纯的资源累积。本文主张通过集成框架向设计科学转型：我们倡议确立协作增益度量（$Γ$）作为科学标准，以从增加的资源预算中分离出内在增益。借助$Γ$，我们提出一种因素归因范式，以系统识别驱动协作的关键因素。为此，我们构建了一个系统化的MAS因素库，将设计空间结构化分为控制级预设与信息级动态。最终，该框架推动从盲目实验向严谨科学的转变，为迈向真正的集体人工智能科学铺平道路。