PhoneValue-Bench: A Multimodal Benchmark and Datasets for Consumer Smartphone Recommerce Valuation
Zihan Deng, Jun Gao
Abstract
As consumer smartphone recommerce scales, buyers must quote binding repurchase prices from seller-uploaded multi-view photos and diagnostic screenshots before devices reach the warehouse. Accurate remote valuation requires fusing noisy cosmetic evidence with identity, battery, lock, and channel risk from screenshots and staff forms, since an error in any modality changes both grade and payout. However, existing multimodal resources mainly target general VLM competence, screen reading, or factory defect detection, and largely overlook staff-aligned trade-in fields, dual appearance/staff ground truth, and executable valuation scorecards. This gap stems from two challenges: (1) the lack of large-scale real trade-in data with audited dual annotations and a compact ranking benchmark kept separate from training labels, and (2) the need to jointly reason over unordered multi-view cosmetics and diagnostic screens under operational deduction rules. To address these challenges, we propose PhoneValue-Bench, a dual-GT evaluation suite with tier-gated scorecards, together with PhoneValue-Corpus, a matched weakly labeled training set of over twenty thousand orders and hundreds of thousands of captioned images. We further present ValueAgent, a staged perception-fusion-valuation reference pipeline aligned with production inspection, plus an executable public scorer and submission schema. Experiments show that ValueAgent reaches a measured 84.1/100 on Bench-Full, with 97.0 appearance fill and 54.1 staff-field fill, establishing a practical dual-GT benchmark for multimodal recommerce valuation.