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Paper Frames Root-Cause Attribution as Search for Long-Horizon Agent Failures
A new arXiv preprint argues that identifying why long-horizon AI agents fail is best treated as a search problem over large execution logs. The approach aims to turn outcome-level failure signals into targeted fixes by locating the specific steps that caused a breakdown. The work targets reliability engineering for agents deployed on extended, multi-step tasks.