大语言模型多智能体网络中的可靠性传播可行性
Reliability-Contagion Feasibility in LLM Multi-Agent Networks
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中文总结 AI 辅助
研究大语言模型多智能体网络中错误主张传播问题，制定校正感知网络模型并结合多数投票基准，推导早期入侵条件等，通过模拟和实验得出相关结果，为在可靠性和传播约束下选择连通性提供依据。
中文摘要 AI 辅助
通信使大语言模型智能体能够汇集证据，但也为错误主张的传播创造了路径。我们制定了一个校正感知网络模型，跟踪易感、暴露、感染和校正的智能体，并推导出其在异构通信网络中的早期入侵条件。然后将此传播模型与一个分析性多数投票基准相结合，其中一个清洁任务可靠性目标施加了最低连通性要求。在每条通信边的固定暴露下，可靠性和错误控制施加了相反的图约束。我们刻画了它们的交集何时为空以及何时包含中间连通性范围，并确定了在可靠图类中达到最小入侵因子的正则图（如果存在）。在固定发送方预算下，均匀一阶阈值与网络密度无关，表明通信预算惯例决定了添加的边是否会增加早期传播风险。对21000条轨迹的有限网络模拟说明了这些方向性预测。一个受控的grok - 4.3实验然后在36个新的封闭世界任务上评估了三种六节点拓扑结构，其中一个平衡的12任务子集持续到完全级联。随着度从2增加到4和5，平均第一代后代从0.667增加到1.333和1.667，而暴露邻居中的采用率保持在0.333。在完全级联子集中的平均非种子错误采用率为0.200、0.333和0.333。这些结果共同为在明确的可靠性和传播约束下选择连通性提供了一个易于处理的基础。
英文摘要
Communication allows large language model agents to pool evidence, but it also creates paths along which an erroneous claim can spread. We formulate a correction-aware network model that tracks susceptible, exposed, infectious, and corrected agents and derive its early-invasion condition for heterogeneous communication networks. We then couple this propagation model to an analytic majority-vote benchmark in which a clean-task reliability target imposes a minimum connectivity requirement. Under fixed exposure per communication edge, reliability and error control impose opposing graph constraints. We characterize when their intersection is empty and when it contains an intermediate connectivity range, and identify regular graphs that attain the smallest invasion factor within the reliable graph class when such graphs exist. Under a fixed sender budget, the homogeneous first-order threshold is independent of network density, showing that the communication-budget convention determines whether added edges increase early propagation risk. Finite-network simulations on 21,000 trajectories illustrate these directional predictions. A controlled grok-4.3 experiment then evaluates three six-node topologies on 36 new closed-world tasks, with a balanced 12-task subset continued to full cascades. Mean first-generation offspring increased from 0.667 to 1.333 and 1.667 as degree increased from 2 to 4 and 5, while the adoption fraction among exposed neighbours remained 0.333. Mean non-seed erroneous adoption in the full-cascade subset was 0.200, 0.333, and 0.333. Together, these results provide a tractable basis for selecting connectivity under explicit reliability and propagation constraints.
