多智能体协作常因沟通失误失败，该研究提出轻量级纠错模块提升准确率。
AgentAsk: Multi-Agent Systems Need to Ask
- 基于四类通信错误设计轻量纠错模块，干预关键对话节点。
- 在五个基准上最高提升4.69%准确率，延迟和开销均低于10%。
- 无需修改架构，可无缝集成到现有系统，适合工程落地。
基于大语言模型的多智能体系统（MAS）通过协作有望提升问题解决能力，但常因智能体间信息传递中的错误传播而无法稳定超越单智能体基线。本文对这类失败进行系统性实证分析，提出一种边级错误分类法，识别出四大主导错误类型：数据缺口、信号污染、指代漂移和能力缺口，是多智能体交互失败的主要根源。基于此分类，本文提出AgentAsk，一种轻量级澄清模块，在多智能体系统中边缘层面主动介入，通过在关键点施加最小化澄清，防止错误连锁扩散。该模块训练时权衡澄清成本、延迟与准确率，且架构无关，易于集成。在五个基准上评估显示，AgentAsk将准确率最高提升4.69%，同时保持延迟和额外开销低于10%，展现出高效率与极低开销。
原文摘要 · Abstract (English)
Multi-agent systems (MAS) built on large language models promise improved problem-solving through collaboration, yet they often fail to consistently outperform strong single-agent baselines due to error propagation at inter-agent message handoffs.In this work, we conduct a systematic empirical analysis of such failures and introduce an edge-level error taxonomy that identifies four dominant error types: Data Gap, Signal Corruption, Referential Drift, and Capability Gap, as primary sources of failure in multi-agent interactions. Building on this taxonomy, we propose AgentAsk, a lightweight clarification module designed to intervene at the edge level in MAS to prevent cascading errors. The module operates by strategically applying minimal clarifications at critical points within the system, improving the accuracy and efficiency of the overall task. AgentAsk is trained to balance the trade-offs between clarification cost, latency, and accuracy, while it is also architecture-agnostic and can be easily integrated into existing systems. Evaluated across five benchmarks, AgentAsk consistently improves accuracy by up to 4.69%, while keeping latency and extra costs below 10% compared to baseline MAS, showcasing its high efficiency and minimal overhead.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。