揭示多智能体协作失败的根源：任务结构限制成功率
How Task Structure Limits Multi-Agent Success: An Information-Theoretic Analysis
- 用信息论分析任务约束图分割导致的信息瓶颈
- 成功概率随最小割代价指数下降，实验证实该规律
- 高最小割代价时应重构任务，而非盲目增加智能体
多智能体系统（MAS）本应通过协作突破单智能体系统的局限。然而，在任务约束图典型性假设和有限通信条件下，我们证明了MAS的成功概率紧密依赖于任务约束的连通性，每个智能体的信息处理能力有限。具体而言，成功概率随任务约束图在智能体间分割产生的信息瓶颈呈指数衰减。我们定义这一量为任务潜在约束图的最小割代价 $C_{\min}$。该信息理论界限适用于具有外部反馈的开放系统与无反馈的封闭系统。我们在合成实验和真实世界数据（SWE-bench 提交记录）上验证了该理论。由此框架可知，有效的多智能体设计需结合任务内在约束与工程优化；当 $C_{\min}$ 较高时，应重构任务，而非简单扩大智能体或通信规模。
原文摘要 · Abstract (English)
Multi-agent systems (MAS) were expected to overcome the limitation of single-agent systems (SAS) through collaboration. However, under typicality conditions on the task's constraint graph and bounded inter-agent communication, we prove that the success probability of a MAS is closely tied to the connectivity of task constraints, where each agent has limited information-processing capacity. Specifically, the success probability decays exponentially with an information bottleneck that emerges from partitioning the task's constraint graph among agents. We define this quantity as the \emph{minimum cut cost} $C_{\min}$ of the potential constraint graph of each task. This information-theoretic bound applies to both open systems with external feedback and closed systems without. We validate our theory on both synthetic experiments and real-world empirical data from SWE-bench submissions. From our framework, effective MAS design should incorporate task-inherent constraints alongside engineering optimization, and when $\Cmin$ is high, practitioners should restructure tasks rather than simply scaling agents or communication.
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