Author

Yi Zhang

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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

#artificial intelligence Preprint Sep 2026

Learning to Retrieve Missing Evidence for Long-Term Memory QA

This work introduces MERA (Missing-Evidence Retrieval Augmentation), which separates globally searchable memory from a question-specific evidence state, and trains a lightweight planner through reinforcement learning, rewarding queries that recover previously missing evidence.

Yi-Xuan Deng, Yi Zhang, Wei Liu et al. · 0 citations

Preprint Sep 2026

Transferring the Intelligence of VLMs to Robotic Control

RoboDawn is investigated, a human-intuitive interface that exposes robotic control to an agentic VLM through a compact set of discrete translation, rotation, and gripper commands, and an in-context learning (ICL) scheme is introduced that ground the VLM in both interface usage and task-solving strategies.

Meng-Hao Guo, Zhe-Han Mo, Jia-Jun Wang et al. · 2 citations · ⚡2

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

Preprint Aug 2026

SEER: Long-Context Reasoning via Selective Visual-Text Compression

SEER is presented, a framework that learns to select query-relevant images through visual scanning and retrieve textual content only where needed, combining the efficiency of visual compression with the precision of text-based reasoning.

Jiawei Xu, Zhilin Zhai, Jinrui Fang et al. · 3 citations