most citedHuman-Aligned Bench: Fine-Grained Assessment of Reasoning Ability in MLLMs vs. Humans

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cs.CV2026

SCOPE-Router: Cost-Aware Open-Set VLM Routing for Execution-Oriented Tasks

Tao Yu, Yifei Qu, Zhiqing Cui +14

Model routing aims to select the most suitable model from a candidate pool for each query, balancing quality and cost. Existing VLM routing research is limited to traditional VQA e…

cs.CV2026

GroupToM-Bench: Benchmarking Group Theory of Mind and Nonlinear Social Emergence in MLLMs

Weidong Tang, Jierui Li, Yueling Hou +7

True general intelligence requires not only a model of the physical world but also a social world model: the capacity to infer how individual mental states interact and crystallize…

cs.CV2025

RAPID^3: Tri-Level Reinforced Acceleration Policies for Diffusion Transformer

Wangbo Zhao, Yizeng Han, Zhiwei Tang +7

Diffusion Transformers (DiTs) excel at visual generation yet remain hampered by slow sampling. Existing training-free accelerators - step reduction, feature caching, and sparse att…

cs.CV2025

Neural-Driven Image Editing

Pengfei Zhou, Jie Xia, Xiaopeng Peng +15

Traditional image editing typically relies on manual prompting, making it labor-intensive and inaccessible to individuals with limited motor control or language abilities. Leveragi…

cs.CV20251 cited

Human-Aligned Bench: Fine-Grained Assessment of Reasoning Ability in MLLMs vs. Humans

Yansheng Qiu, Li Xiao, Zhaopan Xu +3

The goal of achieving Artificial General Intelligence (AGI) is to imitate humans and surpass them. Models such as OpenAI's o1, o3, and DeepSeek's R1 have demonstrated that large la…

cs.CV2025

REPA Works Until It Doesn't: Early-Stopped, Holistic Alignment Supercharges Diffusion Training

Ziqiao Wang, Wangbo Zhao, Yuhao Zhou +9

Diffusion Transformers (DiTs) deliver state-of-the-art image quality, yet their training remains notoriously slow. A recent remedy -- representation alignment (REPA) that matches D…