From Flat Logs to Causal Graphs: Hierarchical Failure Attribution for LLM-based Multi-Agent Systems

Researchers introduce CHIEF, a new framework that improves failure analysis in LLM-powered multi-agent systems by transforming execution logs into hierarchical causal graphs. The system uses oracle-guided backtracking and counterfactual attribution to better identify root causes of failures, outperforming existing methods on benchmark tests.