[2604.00513] MOON3.0: Reasoning-aware Multimodal Representation Learning for E-commerce Product Understanding

Computer Science > Machine Learning arXiv:2604.00513 (cs) [Submitted on 1 Apr 2026] Title:MOON3.0: Reasoning-aware Multimodal Representation Learning for E-commerce Product Understanding Authors:Junxian Wu, Chenghan Fu, Zhanheng Nie, Daoze Zhang, Bowen Wan, Wanxian Guan, Chuan Yu, Jian Xu, Bo Zheng View a PDF of the paper titled MOON3.0: Reasoning-aware Multimodal Representation Learning for E-commerce Product Understanding, by Junxian Wu and 8 other authors View PDF HTML (experimental) Abstract:With the rapid growth of e-commerce, exploring general representations rather than task-specific ones has attracted increasing attention. Although recent multimodal large language models (MLLMs) have driven significant progress in product understanding, they are typically employed as feature extractors that implicitly encode product information into global embeddings, thereby limiting their ability to capture fine-grained attributes. Therefore, we argue that leveraging the reasoning capabilities of MLLMs to explicitly model fine-grained product attributes holds significant potential. Nevertheless, achieving this goal remains non-trivial due to several key challenges: (i) long-context reasoning tends to dilute the model's attention to salient information in the raw input; (ii) supervised fine-tuning (SFT) primarily encourages rigid imitation, limiting the exploration of effective reasoning strategies; and (iii) fine-grained details are progressively attenuated during forward propaga...