A preprint posted to arXiv in May 2026 outlines a data-driven framework that extracts effective transport corrections for floating Sargassum from limited Lagrangian drifter trajectories. Using physically motivated ocean-atmosphere diagnostics and finite-memory representations, the authors compare multilayer perceptron ensembles against Sparse Identification of Nonlinear Dynamics (SINDy) under leave-one-trajectory-out validation. In the Puerto Rico region, delayed symbolic corrections yielded modest but consistent trajectory improvements; in the Gulf Stream, corrections remained largely instantaneous despite persistent predictive value from delayed information. The work underscores that coarse circulation products miss sub-grid processes critical to seaweed beaching, yet the study relies on a small set of trajectories whose exact count is not detailed, limiting statistical power. Prior peer-reviewed work, such as the 2019 Science paper identifying the Great Atlantic Sargassum Belt, documented basin-scale bloom expansion tied to nutrient and circulation shifts, while a 2023 NOAA technical report quantified annual Caribbean cleanup costs exceeding $100 million. The arXiv study does not connect these economic stakes to forecast skill, nor does it test generalization beyond the two sampled regimes. This omission matters because Sargassum arrival windows directly shape tourism revenue and beach access; models that perform unevenly across flow regimes risk leaving managers under-prepared in data-poor areas such as the Lesser Antilles. Finite-memory effects highlighted here align with inertial-particle theory but also expose the persistent difficulty of extracting stable delayed closures from sparse observations, suggesting hybrid physics-ML approaches will require expanded drifter arrays before operational deployment.