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Research questionHow can image generation and editing agents reliably verify and integrate retrieved multimodal world knowledge?Parametric knowledge may be incomplete for the facts and visual appearances required by a prompt, while retrieved text and images can be difficult to check and combine during generation or editing. As a result, relevant evidence may still fail to prevent factual or visual errors.
AI
AI Agents
Computer Vision
Evaluation & Benchmarks
Image Generation
Multimodal Models
Reinforcement Learning
Retrieval-Augmented Generation
Latest papersRecent research connected to this question, newest first.WeAgent-MMGenEdit: A Full-Stack Recipe for Multimodal Agentic Image Generation and EditingThe source addresses agentic multimodal image generation and editing, supported by a multimodal runtime, supervised and reinforcement-learning trajectories, post-training for the agent policy and image backend, and a bilingual benchmark. Its evidence covers knowledge-intensive generation and multi-image editing, with 23K supervised trajectories and 14.7K reinforcement-learning tasks.research paper · Sep 4, 2026
Related questions
How can open image generators follow fine-grained editing instructions without sacrificing image quality or inference speed?How can multimodal models integrate evidence across deeply interleaved text and images?How can multimodal models rely on images or audio rather than language shortcuts?How can image editors infer edit regions and preserve non-target content without introducing artifacts?