PACEShop: Evaluating Personalized, Actionable, Compositional, and Evidence-grounded Shopping Assistants

Weimin Lyu; Chen Luo; Guangrui Li; Yaochen Xie; Dhineshkumar Ramasubbu; Arief Koesdwiady; Wanqiu Long; Hansu Gu; Yutong Chen; Zheshen Wang; Dakuo Wang; Yi Liu

arXiv · 2608.26180

PACEShop: Evaluating Personalized, Actionable, Compositional, and Evidence-grounded Shopping Assistants

Weimin Lyu·Chen Luo·Guangrui Li·Yaochen Xie·Dhineshkumar Ramasubbu·Arief Koesdwiady·Wanqiu Long·Hansu Gu·Yutong Chen·Zheshen Wang·Dakuo Wang·Yi Liu

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Abstract

Shopping assistants are shifting from ranked product lists toward structured decision support, where systems must synthesize shopper context, product evidence, and next-step guidance into a coherent recommendation experience. This changes the unit of evaluation: a fluent response can still fail by ignoring shopper context, contradicting itself across components, or leaving defects too vague to localize. Existing personalization, grounding, and LLM-as-a-judge benchmarks cover pieces of this problem, but they do not define a joint evaluation target for structured shopping-assistant responses. We formulate this missing evaluation target as PACE: Personalized, Actionable, Compositional, and Evidence-grounded evaluation. We instantiate PACE with two artifacts: PACEShop, a benchmark dataset that makes the target measurable through 22,625 controlled records with structured personas, auditable evidence pools, GOOD/BAD labels, and gold defect family and location annotations; and PACEJudge, a training-free judging protocol that makes the target reportable through a structured output contract. Our experiments show that generic judges can recognize broad quality but fail to recover the diagnostic fields required for PACE; PACEShop makes these failures verifiable, and PACEJudge improves persona-source, cross-component, grounding, and family/location closure without retraining, showing that realistic shopping-assistant evaluation requires a task-matched output contract rather than only a stronger backbone or scalar prompt.

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Weimin Lyu, Chen Luo, Guangrui Li, Yaochen Xie, Dhineshkumar Ramasubbu, Arief Koesdwiady, Wanqiu Long, Hansu Gu, Yutong Chen, Zheshen Wang, Dakuo Wang, Yi Liu. 2026-08-20. PACEShop: Evaluating Personalized, Actionable, Compositional, and Evidence-grounded Shopping Assistants. https://arxiv.org/abs/2608.26180

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