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This work proposes Root-Cause Trajectory Attribution (RCTA), a training-free method that organizes original candidate records and explicit handoff instructions for attribution that reaches 24.1% exact root-step accuracy and 51.1% responsible-role accuracy.
DUOTRACE follows a detect-before-attribute paradigm: it first detects anomalous executions and then supplies focused trajectory evidence to downstream LLM-based attribution methods, which improves agent-level and step-level attribution accuracy.
This work introduces LongRCA Bench, comprising 1,140 failed trajectories across five domains without injected errors, and presents Root-Cause Trajectory Attribution (RCTA), a training-free method that retrieves candidate error steps from segment summaries and traces them to available earlier handoff instructions.
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