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Agentic ML Exploration (A-MLE), an autonomous LLM-agent system that systematically explores ML techniques across a portfolio of ads ranking models, is presented and suggested that agentic exploration is a practical force multiplier for ML engineers in industrial recommenders.
The results show that some search procedures can occasionally find large gains but still choose brittle updates, so the reliability of the chosen harness should be reported alongside average held-out lift, so the reliability of the chosen harness should be reported alongside average held-out lift.
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