SOURCE-LINKED INTELLIGENCE
EDGE: Error Dependency Graph-Guided Multi-Error Attribution in Multi-Agent LLM Systems
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 subgr
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-01T15:00:54.000Z
First collected: 2026-09-21T06:01:56.170Z. This is not the publication date.