arXiv AI
TracingFlow: A Simulation-Free Trajectory Inference Framework Based on Second-Order Dynamics
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arXiv Machine Learning
Oct 21
PACE: Geometry-Aware Bridge Transport for Single-Cell Trajectory Inference
PACE is a trajectory inference framework that reconstructs continuous transport dynamics from destructive single‑cell time‑course snapshots. It uses a state‑ and time‑dependent anisotropic Riemannian metric to guide transport along tangent directions, alternates between refining cross‑time couplings and fitting neural bridges, and distills the learned dynamics into a global velocity field. Across multiple datasets, PACE outperforms existing methods, reducing key distance metrics by an average of 23.7% and improving RNA‑velocity alignment by 15.4%.
arXiv AI
Oct 9
Finsler Flow Matching: Dynamics-Aware Geodesic Interpolation for Single-Snapshot Trajectory Inference
The paper introduces Finsler Flow Matching (FFM), a method for learning continuous stochastic dynamics from discrete Markov transition graphs in single‑cell snapshot data. FFM constructs a Finsler geometry using first and second local moments, learns directed geodesics via neural networks, and employs these to build geometry‑aware stochastic paths that can be simulated without explicit score or flow matching. Benchmark tests show that FFM better recovers intermediate cellular states, especially when dynamics are directional or anisotropic.
arXiv Machine Learning
Oct 19
Learning smooth dynamics via Kinematic Interpolants
The paper introduces Kinematic Interpolants (KI), a new framework that extends stochastic interpolants to infer nonlinear dynamics from multiple snapshots in phase space, incorporating both position and velocity measurements. KI addresses the ambiguity in dynamics inference from real-world data and is scalable without simulation or unnecessary approximations. Experiments on various scientific datasets show that KI reconstructs underlying dynamics more accurately than existing baselines while requiring significantly less training time.
arXiv Machine Learning
Oct 15
Neural Wasserstein Splines: Representation-Invariant Second-Order Priors for Trajectory Inference
arXiv:2610.19878v1 Announce Type: new Abstract: Population dynamics are often observed only as unpaired snapshots, which leave the evolution between measurements underdetermined. Second-order regular...
arXiv Machine Learning
Oct 5
Generalizable single-cell perturbation response prediction using energy-guided flow matching
The paper introduces scEGFlow, an energy‑guided flow matching framework that predicts how single cells respond to perturbations by modeling continuous transitions from control to perturbed states. It uses conditional flow matching and condition‑specific energy gradients to dynamically adjust predictions without retraining, achieving superior performance on imaging and transcriptomic benchmarks, even for unseen perturbations or limited data. The method preserves cellular manifold geometry, heterogeneity, and accurately recapitulates gene‑expression regulatory patterns.
arXiv Machine Learning
Oct 15
Is Flow Matching Just Trajectory Replay for Sequential Data?
arXiv:2602.08318v3 Announce Type: replace-cross Abstract: Flow matching (FM) is increasingly used in scientific domains for time series generation and forecasting, where data often arise from underly...