通过因果推理发现基于大语言模型的多智能体系统的高效且可解释的通信拓扑

中文摘要 AI 辅助

基于大语言模型（LLM）的多智能体系统（MAS）的性能在很大程度上取决于有效的通信拓扑。然而，现有的拓扑生成方法通常仅通过任务级奖励驱动的黑盒优化来学习通信拓扑。尽管这种优化方法有效，但它几乎无法解释为何会选择特定的通信边，因此难以识别促成成功协作的关键通信子图。为解决这一局限，我们提出了E2-Explainer，这是一种模型无关框架，用于对任意拓扑生成器生成的通信拓扑提供可解释的解释。具体而言，我们将拓扑解释表述为因果归因问题，该问题旨在识别由边缘级任务保留证据支持的紧凑通信子图。我们采用格兰杰式目标来获取此类证据，该目标用于衡量屏蔽每个通信通道如何改变任务结果以及最终响应的稳定性。随后，将得到的受预算约束的子图提炼为摊销解释器，从而能够在部署时无需重复进行边缘级评估即可实现高效的事后解释。在多个推理和编码基准上进行的大量实验表明，E2-Explainer能够识别出可保留成功协作的关键通信子图，这些子图也可直接执行以修剪冗余通信边，在保持具有竞争力的任务性能的同时大幅降低通信成本。

英文摘要

The performance of large language model (LLM)-based multi-agent systems (MAS) largely depends on effective communication topologies. Existing topology generation methods, however, typically learn communication topologies through black-box optimization driven solely by task-level rewards. While effective, such optimization provides little insight into why particular communication edges are selected, making it difficult to identify the critical communication subgraphs responsible for successful collaboration. To address this limitation, we propose E2-Explainer, a model-agnostic framework for providing interpretable explanations of communication topologies produced by arbitrary topology generators. Specifically, we formulate topology explanation as a causal attribution problem that identifies compact communication subgraphs supported by edge-level evidence of task preservation. We obtain this evidence with a Granger-style objective that measures how masking each communication channel changes the task outcome and the stability of the final response. The resulting budgeted subgraphs are then distilled into an amortized explainer, enabling efficient post-hoc explanation without repeated edge-level evaluations at deployment. Extensive experiments on multiple reasoning and coding benchmarks demonstrate that E2-Explainer identifies critical communication subgraphs that preserve successful collaboration. These subgraphs can also be executed directly to prune redundant communication edges, substantially reducing communication costs while maintaining competitive task performance.

发表机构

• University of Chinese Academy of Sciences（中国科学院大学）
• Institute of Software, Chinese Academy of Sciences（中国科学院软件研究所）
• Beijing University of Posts and Telecommunications（北京邮电大学）

机构由 AI 辅助整理，请以论文原文为准。