AI RESEARCH
SEMASIA: A Large-Scale Dataset of Semantically Structured Latent Representations
arXiv CS.LG
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ArXi:2605.09485v1 Announce Type: new Latent representations learned by neural networks often exhibit semantic structure, where concept similarity is reflected by geometric proximity in embedding space. However, comparing such spaces across models remains difficult: changes in architecture, pre