做表征对齐、多模态学习或可解释性研究的团队，这篇论文把概念对齐的混乱局面理清了——CoSAE 用极少量配对数据就能实现强对齐，值得直接复现试试。
本文提出一个统一框架，将概念对齐分解为“对齐什么”（表征 vs 概念）和“对齐层级”（实例级 vs 分布级）两个维度，从而定义四种属性。作者发现现有方法常混淆这些属性，优化一个目标并不能可靠恢复其他目标。他们引入 InterVenchA 基准来独立测量提取质量、翻译质量和概念一致性。最后提出 CoSAE（耦合稀疏自编码器），联合强制执行互补的对齐目标，仅需 0.1% 配对数据即可在分布目标锚定下恢复实例级对齐。这项工作表明概念对齐本质上是多目标优化问题，需要明确定义、测量和优化。
A Unifying Framework for Concept-Based Representational Similarity
Learned representations across models and modalities often exhibit striking structural similarities, suggesting shared underlying concept decompositions. However, concept alignment remains poorly defined: existing approaches optimize different objectives under the same terminology, obscuring what is actually aligned. We propose a unifying framework that decomposes alignment along two axes: what is aligned (representations vs. concepts) and at what level (instance-wise vs. distributional). This induces four corresponding properties -- instance-wise and distributional variants of translation and concept consistency -- and reveals precisely which of these guarantees existing methods provide. We further introduce \InterVenchA, an intervention-based benchmark that separately measures extraction quality, translation quality, and concept consistency. Through theory and experiments, we show that commonly assumed equivalences between alignment objectives fail in practice: optimizing one property does not reliably recover the others, and purely unsupervised objectives fail to recover meaningful instance-level alignment. We then propose the Coupled Sparse Autoencoder (CoSAE), which jointly enforces complementary alignment objectives. Strong alignment emerges only in this regime. Surprisingly, as little as 0.1\% paired data is sufficient to recover instance-level alignment when anchoring distributional objectives. Overall, our results show that concept alignment is fundamentally multi-objective: it must be defined, measured, and optimized as such.