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20222026
most citedGraph Neural Network with Curriculum Learning for Imbalanced Node Classification

6 citations · 9 across the 11 of their papers we have counts for

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6 papers · 1 filter

math.ST2026

Synergistic Learning in Domain Adaptation with a Known Link: A Minimax Understanding

Pengcheng Xiang, Ling Zhou, Yuhong Yang

When can combining source and target data yield much better performance than using either alone? From a minimax viewpoint, writing for the minimax risk from source a…

eess.SY2026

Edge-Native Embodied Intelligence for Action-Aware Wireless Edge Networks

Yiru Wang, Chuanao Jiang, Jiahui Cui +4

Embodied intelligence is shifting artificial intelligence from passive digital perception toward active physical interaction. However, foundation-model-enabled embodied agents face…

cs.RO2026

PhysX-CoT: Structured Physical Reasoning from a Single Image to Simulation-Ready 3D Assets

Jie Huang, Xiaohe Li, Jiahao Li +6

Simulation-ready 3D assets are central to robotics and embodied AI. Generating them from a single image is usually framed as a vision-language model that emits a serialized asset f…

cs.AI2026

Lingjing: A Simulation Testbed for Multi-Agent Embodied Tasks in Open-Ended Cities

Xiaohe Li, Yiru Wang, Junhao Fan +6

Urban embodied intelligence requires coordination among heterogeneous agents (e.g., UAVs, ground robots, and autonomous vehicles) in dynamic cities. Simulators therefore provide a…

cs.CV2026

Decoding the Delta: Unifying Remote Sensing Change Detection and Understanding with Multimodal Large Language Models

Xiaohe Li, Jiahao Li, Kaixin Zhang +5

While Multimodal Large Language Models (MLLMs) excel in general vision-language tasks, their application to remote sensing change understanding is hindered by a fundamental "tempor…

cs.CV2026

Clue Matters: Leveraging Latent Visual Clues to Empower Video Reasoning

Kaixin zhang, Xiaohe Li, Jiahao Li +4

Multi-modal Large Language Models (MLLMs) have significantly advanced video reasoning, yet Video Question Answering (VideoQA) remains challenging due to its demand for temporal cau…