Do Text Edits Generalize to Visual Generation? Benchmarking Cross-Modal Knowledge Editing in UMMs
arXiv:2606.00477v1 Announce Type: new Abstract: Unified multimodal models (UMMs) have emerged as a promising paradigm for general-purpose multimodal intelligence. As they are deployed in real-world applications, effectively updating internal knowledge becomes critical. While knowledge editing has matured for text-only models, it remains unclear whether edits that successfully modify textual outputs also transfer to image generation in UMMs. To study this question, we introduce UniKE, the first benchmark for cross-modality knowledge editing in UMMs, comprising 2,971 edit subjects spanning attri
As unified multimodal models (UMMs) become more prevalent, the ability to precisely control and update their knowledge across different modalities is a critical and immediate research challenge.
The development of benchmarks for cross-modal knowledge editing directly impacts the reliability, safety, and adaptability of increasingly complex AI systems deployed in real-world applications.
The explicit focus on cross-modal knowledge editing introduces a new dimension to how AI models are improved and maintained, moving beyond text-only updates to comprehensive multimodal coherence.
- · AI researchers
- · Multimodal AI developers
- · Companies deploying UMMs
- · AI models with brittle knowledge bases
- · Current knowledge editing methods limited to single modalities
Improved methods for updating and refining internal knowledge in unified multimodal AI models.
More reliable and adaptable AI systems that can integrate new information across text and visual domains without unintended side effects.
Accelerated development of general-purpose multimodal intelligence, leading to AI agents with more coherent and robust understanding of the world.
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