Published in Preprint, 2026
We propose PEARL, a label-efficient method that softly aligns embeddings toward class prototypes to improve local neighborhood structure without changing dimensionality or requiring heavy retraining.
Citation as: Ruiyu ZHANG, Lin Nie, Wai-Fung Lam, Qihao Wang, and Xin Zhao. (2026). "PEARL: Prototype-Enhanced Alignment for Label-Efficient Representation Learning with Deployment-Driven Insights from Digital Governance Communication Systems." Preprint. arXiv:2601.17495. https://arxiv.org/abs/2601.17495
