When a multi-agent system produces an incorrect or harmful answer, who is accountable if execution logs and agent identifiers are unavailable? Multi-agent language systems increasingly rely on structured interactions such as delegation and iterative refinement, yet the final output often obscures the underlying interaction topology and agent contributions. We introduce IET (Implicit Execution Tracing), a metadata-independent framework that enables token-level attribution directly from generated text and a simple mechanism for interaction topology reconstruction. During generation, agent-specific keyed signals are embedded into the token distribution, transforming the text into a self-describing execution trace detectable only with a secret key. At detection time, a transition-aware scoring method identifies agent handover points and reconstructs the interaction graph. Experiments show that IET recovers agent segments and coordination structure with high accuracy while preserving generation quality, enabling privacy-preserving auditing for multi-agent language systems.

翻译：当多智能体系统产生错误或有害答案时，若执行日志与智能体标识符均不可用，责任应如何追溯？多智能体语言系统日益依赖委托、迭代优化等结构化交互，但最终输出往往掩盖了底层的交互拓扑与智能体贡献。本文提出IET（隐式执行追踪）框架——一种不依赖元数据的解决方案，能够直接从生成文本实现词元级归因，并提供简单的交互拓扑重建机制。在生成过程中，通过将特定于智能体的密钥信号嵌入词元分布，文本被转化为仅能通过密钥解析的自描述执行轨迹。在检测阶段，基于状态转移感知的评分方法可识别智能体交接点并重建交互图。实验表明，IET在保持生成质量的同时，能以高精度还原智能体分段与协作结构，为多智能体语言系统实现隐私保护的审计功能。