EDGE: Error Dependency Graph-Guided Multi-Error Attribution in Multi-Agent LLM Systems

Abstract:Large language model (LLM) agent failures often contain multiple related errors rather than a single mistake. Existing attribution methods usually identify a responsible agent, step, or root cause, but do not explicitly model dependency between errors. We introduce EDGE, an Error Dependency Graph-guided multi-Error attribution framework. EDGE constructs an error dependency graph from observed error events and validates a reliable causal subset through counterfactual rollout. The inference graph guides a two-stage LLM-as-judge detector for error attribution, and the intervention-validated subgraph provides a more reliable basis for explanation and repair analysis. Experiments on TRAIL and MAST show that EDGE improves category-level multi-error attribution across most evaluated models and settings. Experiments with adapted Who&When-style prompts show that the graph helps across prompting strategies. These results suggest that dependency structure is a useful diagnostic prior for agent failures beyond isolated root-cause prediction.

Artificial Intelligence (cs.AI)

arXiv:2609.01360 [cs.AI]

(or arXiv:2609.01360v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2609.01360 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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