# Error-Propagation Modeling

The work proposes EMFA, which reconstructs failed multi-agent trajectories into structured dependency representations, explicitly models cascading error propagation and persistent interaction loops, and combines dual-branch candidate screening with counterfactual verification to identify the decisive error—the agent–step pair whose correction would recover the failed execution; on the Who&When benchmark it achieves state-of-the-art step-level attribution accuracy, improving the previous best by 3.45 and 4.40 percentage points on the Hand-Crafted and Algorithm-Generated subsets, with the best agent-level accuracy on Hand-Crafted and competitive results on Algorithm-Generated.

The work proposes EMFA, which reconstructs failed multi-agent trajectories into structured dependency representations, explicitly models cascading error propagation and persistent interaction loops, and combines dual-branch candidate screening with counterfactual verification to identify the decisive error—the agent–step pair whose correction would recover the failed execution; on the Who&When benchmark it achieves state-of-the-art step-level attribution accuracy, improving the previous best by 3.45 and 4.40 percentage points on the Hand-Crafted and Algorithm-Generated subsets, with the best agent-level accuracy on Hand-Crafted and competitive results on Algorithm-Generated.