Predicting how a patient's vital signs respond to an intervention is a central question in intensive care. World models can do so by learning dynamics as a function of prior actions. However, such models tend to be brittle outside of training data. Clinicians choose drug dosages based on the patient's state, so the ass...
Zheng Xu, Nicholas Konz, Zhen Tan et al.· 0 citations
Missing modality remains a longstanding challenge in multimodal learning. Existing methods typically address this issue through modality recovery or adaptive strategies. However, they overlook models'internal cross-modal dependencies formed during multimodal training, which later impair robustness. We systematically ch...
Song-Yuan Sui, Zhen Tan, Mohan Zhang et al.· 0 citations
A time series world model (TSWM) predicts a controlled system's state from its observed history and planned actions and exogenous inputs. Current approaches build forecasters with actions as covariates, trained and evaluated on prediction error under the executed plan. Yet world models compare unexecuted plans, but the...
H. Zhang, Jia-Heng Guo, Zheng Xu et al.· 0 citations
OPD-Aha is introduced, which reconstructs the distillation target directly from this isolated visual preference rather than relying on the fragile teacher-student discrepancy, yielding broad and consistent improvements across diverse fine-grained perception and complex multimodal reasoning benchmarks.
Chen-Hao Qiu, Da-Wei Li, Ye-Chao Zhang et al.· 0 citations
Examination of the impact of agent diversity, agent open-mindedness, and human–AI collaboration (HAIC) in a multi-LLM-agent system for automated content analysis demonstrates reliable and accurate measurement of four communication variables across three datasets, with improved performance following agent discussion.
Xinyan Zhao, Chengshuai Zhao, Mordecai Mengesteab et al.· Social science computer revi...· 0 citations
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