Author

Rong-Hao Xian

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Preprint Sep 2026

Pay More Attention To Text In High-Resolution MLLMs

Failures of high-resolution MLLMs are commonly attributed to a visual problem, motivating zooming, cropping, and related visual interventions to recover fine-grained evidence or suppress interference. Yet recent studies suggest that relevant visual evidence is already encoded in intermediate representations, indicating...

Z. Mao, Wen-Zhuo Zhao, Xian-Jie Liu et al. · 0 citations

Preprint Jul 2026

Is It Time for the Renaissance of Salient Object Detection in the Era of MLLMs?

The zero-shot capabilities of multimodal large language models (MLLMs) are pushing salient object detection (SOD) beyond task-specific supervision. To disentangle MLLMs beyond conventional mask-based evaluation, we decompose SOD into localization and segmentation, and re-engineer datasets with phrases, boxes, and attri...

Wenzhuo Zhao, Xiuzhi Li, Z. Mao et al. · 0 citations

Jul 2026

Thinking Once Is Enough: Intermediate-Layer Evidence Routing for High-Resolution VQA

Thinking-Once is proposed, a training-free, single-visual-pass evidence-routing method that reconstructs question-conditioned attention at this window, preserves core entity tokens and compact background context, and routes this evidence to later layers without extra visual encoding.

Z. Mao, Xian-Jie Liu, Tian-Yu Meng et al. · arXiv.org · 0 citations