Researchers introduce Valley3, a multimodal large language model designed for e-commerce applications across different markets. The model handles text, images, video, and audio within a single framework, aiming to combine understanding and reasoning abilities across those modalities. The paper is a revised arXiv preprint, with an updated version posted to the cs.AI category.
Researchers propose RA-CoA, a training-free method for fashion image captioning that combines retrieval with a chain-of-attributes reasoning approach. The work targets e-commerce use cases, where captioning demands fine-grained visual analysis and correct domain-specific fashion terminology rather than generic scene description. It is published as an arXiv preprint in the cross-listed machine learning category.
A new arXiv paper surveys self-supervised approaches to modeling event stream data, the timestamped sequences generated by digital activity in areas such as healthcare, e-commerce, gaming, and finance. The authors argue for unified methods across these domains and outline remaining challenges and future directions. The work is positioned as a progress-and-prospects review rather than a new model or benchmark.
Researchers present a multi-agent vision-language system that helps generate written product reviews grounded in user-uploaded images and videos from e-commerce platforms. The approach is intended to make use of visual feedback showing item quality, defects, packaging, and real-world use. The work is published as an arXiv preprint.