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arXiv Machine Learning By Ryotaro Shimizu, Sai HtaungKham, Mo Fan

Preference Is Not Permission: Personalizing the Auto-Purchase Line in Agentic Commerce

Read the original on arXiv Machine Learning →

The paper introduces a new benchmark for personalized shopping that focuses on determining how far an autonomous agent should commit to purchasing an item. It defines a three-valued, evidence‑conditioned boundary—‘don’t buy’, ‘confirm first’, or ‘buy automatically’—based on product information sufficiency, reversibility, price norm, and urgency. Experiments with instruction‑tuned models show that these agents often overreach, committing even when fit is low or evidence is missing, and that a shallow learner trained on a person’s own labels can predict their auto‑purchase line more accurately than any prompt‑model combination.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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