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Ming-Hao Han

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#artificial intelligence Preprint Sep 2026

OmniReasoning: Pushing the Limits of Audio-Visual Joint Reasoning

Recent advances have enabled unified omni-modal models in understanding audio, vision, and language. However, existing benchmarks, training data, and learning methods largely treat the modalities independently, leaving the capability of audio-visual joint reasoning poorly evaluated and insufficiently elicited. We addre...

Junming Lin, Yuxuan Wang, Zhen-Xin Lei et al. · 0 citations
#artificial intelligence Preprint Sep 2026

OmniFysics-Nano-V2 Technical Report: Understanding the Physical World Across Modalities

Omni-modal models have expanded multimodal interaction across vision, audio, speech, and language. However, their training is predominantly organized around semantic descriptions and general-purpose objectives, leaving physical attributes, interaction states, and causal mechanisms only partially specified. This gap is...

Yi-Zhou Liu, Jing-Hang Han, Kai Qiu et al. · 0 citations

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