Author

Tianjie Ju

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#artificial intelligence May 2026

Causal Probing for Internal Visual Representations in Multimodal Large Language Models

This work proposes a causal framework based on activation steering to actively probe and manipulate internal visual representations, revealing a divergence in concept encoding: entity knowledge is distinctively localized, whereas abstract concepts are globally distributed across the network.

Zehao Deng, Tianjie Ju, Zheng Wu et al. · arXiv.org · 0 citations

Preprint Aug 2026

OmniScientist: An Omni-Modal Omni-Discipline AI Scientist

OmniScientist is introduced, an end-to-end, omni-modal AI scientist that conducts multidisciplinary research directly from heterogeneous raw evidence and demonstrates that lifecycle-wide perception is essential for evidence-grounded scientific discovery and provides a practical path toward broadly capable AI scientists...

Bobo Li, Hao Fei, Tianjie Ju et al. · 1 citation

Preprint Aug 2026

UrbanGround: From Local Perception to Spatial Agency in a Real-Scale City

This paper proposes UrbanGround, the first sandbox to make this question testable in a physically constrained replica of Hong Kong built from territory-wide 3D geospatial data, and hopes it will support broader study of how far current MLLM agents can explore reliably in complex, open-ended urban environments.

Tianjie Ju, Zheng Wu, Yue-Qing Sun et al. · 2 citations