Paper Detail
Zihao Wang, Xi Xiang, Yuwen Sun, Yingyu Li, Yabo Zhang, Yihan Zeng, Fan Li, Wangmeng Zuo
Current evaluations and training of multimodal models predominantly focus on multi-image tasks, largely overlooking interleaved text-image scenarios. In such multi-image tasks, text typically serves merely as task instructions, lacking deep semantic interaction with the visual content. In contrast, realworld applications like text-image co-creation, character tracking, and spatial reconstruction require constant interaction between text and images. Consequently, models must possess a deep understanding of these interleaved contexts. To bridge this gap, we introduce a novel benchmark, TIC-Bench (deeply interleaved Text-Image Contexts), designed to evaluate the capability of models to integrate text-image clues and recover the ground truth facts within deeply interleaved contexts. This benchmark encompasses three core domains: Logical, Temporal, and Spatial Association, which are further categorized into eight specific types, comprising a total of 2,280 questions. We evaluated 10 state-of-the-art MLLMs and observed a substantial performance gap compared to human experts, together with persistent difficulties in integrating evidence distributed across interleaved visual and textual inputs. Ultimately, this benchmark provides a valuable analytical tool for assessing and advancing the ability of multimodal models to effectively integrate text and image information in deeply interleaved contexts. TIC-Bench is publicly available at https://huggingface.co/datasets/pino10010/TIC-Bench
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@article{wang2026deeply,
title = {Deeply Interleaved Text-Image Contexts for Multimodal LLMs Assessment},
author = {Zihao Wang and Xi Xiang and Yuwen Sun and Yingyu Li and Yabo Zhang and Yihan Zeng and Fan Li and Wangmeng Zuo},
year = {2026},
abstract = {Current evaluations and training of multimodal models predominantly focus on multi-image tasks, largely overlooking interleaved text-image scenarios. In such multi-image tasks, text typically serves merely as task instructions, lacking deep semantic interaction with the visual content. In contrast, realworld applications like text-image co-creation, character tracking, and spatial reconstruction require constant interaction between text and images. Consequently, models must possess a deep unders},
url = {https://arxiv.org/abs/2609.02573},
keywords = {cs.CV},
eprint = {2609.02573},
archiveprefix = {arXiv},
}
{}