Modern nuclear and particle physics detectors such as the sPHENIX TPC produce increasingly large, sparse data volumes that strain conventional reconstruction. This work evaluates FM4NPP, a ~200M-parameter Mamba-based foundation model pretrained on TPC spacepoint sequences via self-supervised $k$-Next-Nearest-Neighbor prediction, on downstream track finding and particle identification. The central claim is that a single frozen backbone with lightweight adapters can outperform a traditional Cellular-Automaton seeder at low momentum and match or exceed a Gaussian-Process-Regression $dE/dx$ baseline for PID, potentially extending physics reach while simplifying analysis chains.

Vector Diffusion Maps (VDM) capture pairwise connection relationships in complex datasets via the Graph Connection Laplacian, but eigenvalue decomposition costs $O(n^{2.81})$, prohibiting large-scale applications. This paper proposes LA-VDM (Landmark Accelerated VDM), which constrains diffusion through landmark points and introduces a novel two-stage normalization scheme with parameters $\alpha$ and $\beta$ to handle non-uniform sampling densities in both data and landmarks. Under a manifold model with the frame bundle structure, the authors prove that LA-VDM asymptotically converges to the connection Laplacian while reducing complexity to $O(nm^2)$, enabling applications to datasets with millions of points.