Towards a Science of Collective AI: LLM-based Multi-Agent Systems Need a Transition from Blind Trial-and-Error to Rigorous Science

Jingru Fan; Dewen Liu; Yufan Dang; Huatao Li; Yuheng Wang; Wei Liu; Feiyu Duan; Xuanwen Ding; Shu Yao; Lin Wu; Ruijie Shi; Wai-Shing Leung; Yuan Cheng; Zhongyu Wei; Cheng Yang; Chen Qian; Zhiyuan Liu; Maosong Sun

arXiv · 2602.05289

Towards a Science of Collective AI: LLM-based Multi-Agent Systems Need a Transition from Blind Trial-and-Error to Rigorous Science

Jingru Fan·Dewen Liu·Yufan Dang·Huatao Li·Yuheng Wang·Wei Liu·Feiyu Duan·Xuanwen Ding·Shu Yao·Lin Wu·Ruijie Shi·Wai-Shing Leung·Yuan Cheng·Zhongyu Wei·Cheng Yang·Chen Qian·Zhiyuan Liu·Maosong Sun

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Abstract

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.

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Jingru Fan, Dewen Liu, Yufan Dang, Huatao Li, Yuheng Wang, Wei Liu, Feiyu Duan, Xuanwen Ding, Shu Yao, Lin Wu, Ruijie Shi, Wai-Shing Leung, Yuan Cheng, Zhongyu Wei, Cheng Yang, Chen Qian, Zhiyuan Liu, Maosong Sun. 2026-02-05. Towards a Science of Collective AI: LLM-based Multi-Agent Systems Need a Transition from Blind Trial-and-Error to Rigorous Science. https://arxiv.org/abs/2602.05289

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