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cs.CV2026

EvoGUI: An Evolution-Aware Benchmark for GUI State-Transition Understanding

Yaohan Yang, Minglei Shi, Borui Zhang +2

GUI agents must reason about how actions transform interface states, but end-to-end success rates entangle this ability with perception, grounding, planning, and recovery. We intro…

cs.CV2026

Attention at Rest Stays at Rest: Breaking Visual Inertia for Cognitive Hallucination Mitigation

Boyang Gong, Yu Zheng, Fanye Kong +2

Like a body at rest that stays at rest, we find that visual attention in multimodal large language models (MLLMs) exhibits pronounced inertia, remaining largely static once settled…

cs.CV2026

AdaZoom-GUI: Adaptive Zoom-based GUI Grounding with Instruction Refinement

Siqi Pei, Liang Tang, Tiaonan Duan +9

GUI grounding is a critical capability for vision-language models (VLMs) that enables automated interaction with graphical user interfaces by locating target elements from natural…

cs.CV2025

SparseMM: Head Sparsity Emerges from Visual Concept Responses in MLLMs

Jiahui Wang, Zuyan Liu, Yongming Rao +1

Multimodal Large Language Models (MLLMs) are commonly derived by extending pre-trained Large Language Models (LLMs) with visual capabilities. In this work, we investigate how MLLMs…

cs.CV2025

Ola: Pushing the Frontiers of Omni-Modal Language Model

Zuyan Liu, Yuhao Dong, Jiahui Wang +4

Recent advances in large language models, particularly following GPT-4o, have sparked increasing interest in developing omni-modal models capable of understanding more modalities.…

cs.CV20241 cited

Oryx MLLM: On-Demand Spatial-Temporal Understanding at Arbitrary Resolution

Zuyan Liu, Yuhao Dong, Ziwei Liu +3

Visual data comes in various forms, ranging from small icons of just a few pixels to long videos spanning hours. Existing multi-modal LLMs usually standardize these diverse visual…