COHERENCE: Benchmarking Fine-Grained Image-Text Alignment in Interleaved Multimodal Contexts
In recent years, Multimodal Large Language Models (MLLMs) have achieved remarkable progress on a wide range of multimodal benchmarks. Despite these advances, most existing benchmarks mainly focus on single-image or multi-image comprehension. In real-world scenarios such as document reading, information is often presented as interleaved multimodel contexts. This requires MLLMs not only to recognize the content of individual images, but also to identify relevant textual and visual evidence, establish fine-grained alignments between them, and reason over these aligned signals in interleaved contexts based on contextual evidence. However, there is still a lack of systematic benchmarks for qua...
| Authors | Bingli Wang, Huanze Tang, Haijun Lv et al. |
|---|---|
| Primary category | cs.CV |
| Published | 30 April 2026 |
| Last updated at source | 13 May 2026 |
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