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 anomalo...
| Authors | Jiayi Zhang, Zexin Wang, Degang Sun et al. |
|---|---|
| Primary category | cs.AI |
| Published | 30 August 2026 |
| Last updated at source | 30 August 2026 |
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