Researchers have developed TracingFlow, a new framework for inferring the trajectory of complex systems from sparse data. Unlike existing methods that assume first-order dynamics, TracingFlow accounts for regulatory momentum and time-delayed responses by using neural networks to regress the acceleration field. This allows it to capture high-curvature transitions and nonlinear evolutions in processes like cell differentiation. The framework was evaluated on synthetic and real-world datasets and shown to achieve superior accuracy in distributional reconstruction and trajectory faithfulness. --- Why it matters: This matters because existing methods for inferring system trajectories can be limited by their assumption of first-order dynamics, which may not accurately capture the underlying forces at play. TracingFlow's ability to model second-order dynamics could have significant implications for fields like single-cell omics and generative modeling. Source: https://arxiv.org/abs/2608.21070