AI RESEARCH
A Framework for Assessing AI Agent Decisions and Outcomes in AutoML Pipelines
arXiv CS.AI
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ArXi:2602.22442v2 Announce Type: replace Agent-based AutoML systems rely on large language models to make complex, multi-stage decisions across data processing, model selection, and evaluation. However, existing evaluation practices remain outcome-centric, focusing primarily on final task performance. Through a review of prior work, we find that none of the surveyed agentic AutoML systems report structured, decision-level evaluation metrics intended for post-hoc assessment of intermediate decision quality.