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[2602.20463] A Long-Short Flow-Map Perspective for Drifting Models
Summary
This paper presents a novel perspective on drifting models through a long-short flow-map factorization, proposing a new likelihood learning framework validated by theoretical analysis and empirical evaluations.
Why It Matters
Understanding drifting models is crucial in machine learning, particularly for applications in dynamic environments. This paper offers a fresh approach that could enhance model accuracy and robustness, paving the way for future research and practical applications in various fields.
Key Takeaways
- Introduces a long-short flow-map factorization for drifting models.
- Proposes a new likelihood learning formulation aligned with density evolution.
- Validates the framework through theoretical analysis and empirical tests.
- Highlights open problems for future research in the field.
- Provides a closed-form optimal velocity representation for transport processes.
Computer Science > Machine Learning arXiv:2602.20463 (cs) [Submitted on 24 Feb 2026] Title:A Long-Short Flow-Map Perspective for Drifting Models Authors:Zhiqi Li, Bo Zhu View a PDF of the paper titled A Long-Short Flow-Map Perspective for Drifting Models, by Zhiqi Li and 1 other authors View PDF HTML (experimental) Abstract:This paper provides a reinterpretation of the Drifting Model~\cite{deng2026generative} through a semigroup-consistent long-short flow-map factorization. We show that a global transport process can be decomposed into a long-horizon flow map followed by a short-time terminal flow map admitting a closed-form optimal velocity representation, and that taking the terminal interval length to zero recovers exactly the drifting field together with a conservative impulse term required for flow-map consistency. Based on this perspective, we propose a new likelihood learning formulation that aligns the long-short flow-map decomposition with density evolution under transport. We validate the framework through both theoretical analysis and empirical evaluations on benchmark tests, and further provide a theoretical interpretation of the feature-space optimization while highlighting several open problems for future study. Comments: Subjects: Machine Learning (cs.LG) Cite as: arXiv:2602.20463 [cs.LG] (or arXiv:2602.20463v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2602.20463 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Subm...
