DUOTRACE让LLM智能体故障定位更准，先检测异常再归因，准确率提升近10%。
研究人员提出DUOTRACE，一种基于LLM的故障归因检测过滤器。该方法采用先检测后归因的范式，通过双视图语义-结构节点表示、Tree-LSTM轨迹编码器和数据增强技术，在6个基准测试中分别提升8.7%和7.0%的智能体级和步骤级归因准确率。
Detect Before You Attribute: Cascade Failure Attribution for Multi-Agent Systems
Large language model (LLM)-based agents have shown strong potential in solving complex tasks through multi-step reasoning, yet they remain vulnerable to execution failures. Accurate failure attribution is therefore critical for improving agent reliability. Existing topology- and spectrum-based methods exploit trajectory structures but often overlook fine-grained semantics, while LLM-based attribution methods capture semantic cues but suffer from long-context degradation over lengthy trajectories. To address these challenges, we propose DUOTRACE, a plug-and-play detection filter for LLM-based failure attribution. DUOTRACE follows a detect-before-attribute paradigm: it first detects anomalous executions and then supplies focused trajectory evidence to downstream LLM-based attribution methods. For effective VAE-based anomaly detection on agent trajectories, DUOTRACE integrates dual-view semantic-structural node representations, a Tree-LSTM-based trajectory encoder, and prefix-chain- and LLM-based data augmentation to handle heterogeneous nodes, hierarchical execution structures, and limited failure data. Experiments with six LLM-based attribution baselines show that DUOTRACE improves agent-level and step-level attribution accuracy by 8.7% and 7.0%, respectively.