arXiv:2605.28733v1 Announce Type: new Abstract: Product images strongly influence consumer decision-making in online marketplaces. Empowered by multimodal contrastive learning, generative AI can output images that closely align with text prompts. Yet existing generative AI models do not directly optimize marketplace performance. This is a critical gap, since semantic alignment alone does not guarantee that an image will sell. To address this limitation, we propose a \textit{utility-aware multimodal contrastive learning} framework that incorporates consumer demand into a novel Utility-Aware Inf
The proliferation of generative AI for content creation necessitates moving beyond mere semantic alignment to directly optimize for business outcomes, particularly in e-commerce, as models mature.
A strategic reader should care because this represents a tangible step towards AI models directly impacting economic performance metrics, shifting focus from technical capabilities to commercial utility.
Generative AI is shifting from purely aesthetic or semantically similar outputs to explicitly incorporating marketplace performance data, making its creations directly responsive to economic utility functions.
- · E-commerce platforms
- · Generative AI developers
- · Online retailers
- · AI-powered advertising
- · Traditional product photography
- · Generic generative AI models
- · Ad agencies relying on subjective design
Increased sales conversion rates for online products using utility-aware generated images.
Accelerated adoption of AI tools by businesses prioritizing measurable ROI from creative assets.
The development of 'economic AI agents' that autonomously generate and optimize content for maximum profit across various digital channels.
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Read at arXiv cs.AI