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DynG-Diff proposes a state-aware dynamic guidance diffusion framework for probabilistic multivariate time series forecasting, improving robustness by adaptively handling variable heterogeneity.
PathGuide reformulates classifier-free guidance selection as an on-policy transport problem in flow-based generative models, using the weak form of the continuity equation to dynamically optimize guidance scales for improved sample fidelity.