Xiangheng Kong — accepted AI-conference papers

2026

MIRAGE: Towards AI-Generated Image Detection in the Wild

OuCheng Huang, Manxi Lin, Jiexiang Tan, Xiaoxiong Du, Yang Qiu, Junjun Zheng +3

AAAI 2026technical

The spreading of AI-generated images (AIGI), driven by advances in generative AI, poses a significant threat to in- formation security and public trust. Existing AIGI detectors, while effective against images in clean laboratory settings, fail to generalize to in-the-wild scenarios. These real-world

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2026

Rosetta Stone For Unified MLLMs: A Unified Tokenizer to Decipher Understanding and Generation

Wenyu Sun, Hufei Li, Ruijin Jin, Xiangheng Kong, Yuning Jiang

CVPR 2026

Major state-of-the-art unified tokenizers predominantly adopt pixel reconstruction and feature alignment as pretext tasks, they leave key domains largely unexplored such as architecture, supervised objectives and tasks interaction, potentially resulting in limited performance. We systematically inve

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