MOON3.0: Reasoning-aware Multimodal Representation Learning for E-commerce Product Understanding
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 reas...
| Authors | Junxian Wu, Chenghan Fu, Zhanheng Nie et al. |
|---|---|
| Primary category | cs.LG |
| Published | 1 April 2026 |
| Last updated at source | 5 August 2026 |
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