《Galaxy的分词器指南：科学基础模型的一个基准》
The Galaxy's Guide to the Tokenizer: A Benchmark for Scientific Foundation Models
摘要
令牌化是将科学数据适配到基于Transformer的基础模型中的关键步骤，但其对学习到的表示方式的影响却仍不明确。我们在统一的Transformer框架下比较了四种令牌化方法：Affine、AIM、JetFormer和VQ-VAE。使用来自DESI Legacy Survey的640,000张星系图像以及共享的AstroPT模型结构，我们从重建精度和物理属性的预测能力方面评估了每种方法的性能。研究结果表明，不同方法之间存在权衡关系：基于流结构的JetFormer具有更高的重建质量，而VQ-VAE则能很好地预测星系的物理属性。Affine和AIM则能更好地保留局部形态信息。我们发现，重建质量和表示质量是相互独立的，没有一种方法能在所有任务中始终表现最佳。通过以独立测量的物理量作为评估依据，我们希望这项研究能够凸显科学数据作为构建可解释基础模型基准数据的潜力。
English Abstract
Tokenization is central to adapting scientific data for transformer-based foundation models, yet its impact on learned representations remains poorly understood. We compare four tokenization strategies, Affine, AIM, JetFormer, and VQ-VAE, within a unified transformer framework for astronomical imaging. Using 640,000 galaxy images from the DESI Legacy Survey and a shared AstroPT backbone, we evaluate each method on reconstruction fidelity and prediction of physical properties. Our results reveal trade-offs across approaches. The flow-based JetFormer achieves higher reconstruction quality, while VQ-VAE yields strong probe performance for galaxy physical properties. Affine and AIM better preserve localized morphological information. We find that reconstruction and representation quality are decoupled, and no single method consistently performs best across the tasks considered here. By grounding our evaluation in independently measured physical quantities, we hope this study serves to highlight the potential of scientific data as a basis for constructing interpretable benchmarks for foundation models.