aiqbench analysis
REFINE introduces a closed-loop learning framework for trajectory data that integrates feedback control theory with generative reconstruction to overcome the limitations of static, open-loop augmentation methods. By establishing convergence guarantees through control-theoretic analysis, this approach enables models to capture fine-grained movement semantics and global spatio-temporal dependencies more effectively than current state-of-the-art techniques. The method demonstrates significant performance gains across multiple downstream tasks while maintaining computational efficiency on real-world datasets.
REFINE: Trajectory Representation Learning via Closed-Loop Transcription -- Extended Version
arXiv:2609.07206v1 Announce Type: new Abstract: Trajectory representation learning underpins a wide range of trajectory analytics tasks; however, most existing self-supervised approaches, whether discriminative or generative, adopt an open-loop paradigm, relying on fixed data augmentations or random masking without feedback, which limits their ability to generalize and scale. We propose REFINE, a simple yet effective Representation lEarning Framework vIa closed-loop traNscription rEfinement for trajectory data. Drawing upon feedback control theory, REFINE tightly couples road-network-aware generative reconstruction with feedback-driven contrastive learning, enabling the model to capture fine-grained local movement semantics and global spatio-temporal dependencies without manually designed augmentation views. We further provide a control-theoretic analysis that establishes convergence guarantees for the proposed closed-loop optimization. Extensive experiments on four real-world datasets demonstrate that REFINE consistently outperforms state-of-the-art methods across multiple downstream tasks while remaining computationally efficient and scalable. This paper is an extended version of REFINE: Trajectory Representation Learning via Closed-Loop Transcription, to appear in KDD 2026.
Abstract:Trajectory representation learning underpins a wide range of trajectory analytics tasks; however, most existing self-supervised approaches, whether discriminative or generative, adopt an open-loop paradigm, relying on fixed data augmentations or random masking without feedback, which limits their ability to generalize and scale. We propose REFINE, a simple yet effective Representation lEarning Framework vIa closed-loop traNscription rEfinement for trajectory data. Drawing upon feedback control theory, REFINE tightly couples road-network-aware generative reconstruction with feedback-driven contrastive learning, enabling the model to capture fine-grained local movement semantics and global spatio-temporal dependencies without manually designed augmentation views. We further provide a control-theoretic analysis that establishes convergence guarantees for the proposed closed-loop optimization. Extensive experiments on four real-world datasets demonstrate that REFINE consistently outperforms state-of-the-art methods across multiple downstream tasks while remaining computationally efficient and scalable. This paper is an extended version of REFINE: Trajectory Representation Learning via Closed-Loop Transcription, to appear in KDD 2026.
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
arXiv:2609.07206 [cs.LG]
(or arXiv:2609.07206v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2609.07206 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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