arXiv:2603.18528v2 Announce Type: replace Abstract: Text-to-image models produce images that align well with natural language prompts, but compositional generation has long been a central challenge. Models often struggle to satisfy multiple concepts within a single prompt, frequently omitting some concepts and resulting in partial success. Such failures highlight the difficulty of jointly optimizing multiple concepts during reward optimization, where competing concepts can interfere with one another. To address this limitation, we propose Correlation-Weighted Multi-Reward Optimization (CMO), a
The paper addresses a core limitation of current text-to-image models (compositional generation) at a time when multimodal AI capabilities are rapidly advancing.
Improving compositional generation makes AI models more capable of fulfilling complex, multi-concept directives, expanding their utility across creative, design, and potentially agentic applications.
This advancement could lead to more reliable and controllable AI-generated content, reducing the need for extensive post-generation editing or multiple regeneration attempts.
- · AI developers
- · Creative industries
- · Gaming
- · E-commerce
- · Manual content creation workflows (for specific tasks)
AI models will generate more coherent and accurate images/content from complex prompts.
This capability will enable AI to handle more sophisticated creative and design tasks autonomously.
Improved compositional ability could accelerate the development of more general and less hallucinating AI agents capable of complex physical world interactions.
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Read at arXiv cs.AI