The internal representations of large language models (LLMs) remain largely opaque, hindering interpretability, alignment, and cross-lingual transfer. A core obstacle is polysemanticity—the phenomenon whereby individual neurons activate for semantically unrelated concepts—which arises from the superposition of more features than there are physical dimensions. Existing approaches, such as Sparse Autoencoders (SAEs), address this by decomposing embeddings into millions of monosemantic features, yet they recover no macroscopic coordinate system that organizes these features. In this work, we introduce the Atlas Autoencoder (Atlas AE), a topology-preserving autoencoder trained on approximately 100,000 English dictionary definitions, which compresses 768-dimensional transformer embeddings onto a 128-dimensional latent manifold. Linear probing within this latent space recovers eleven interpretable cognitive axes—Concreteness, Agency, Sentiment, Intentionality, Sociality, Power, Temporality, Dynamicity, Negation, Quantity, and Perceptuality—each grounded in established psycholinguistic norms (AUROC 0.75–0.96; all permutation ). Residual subspace analysis confirms that the semantic space saturates at exactly eleven stable dimensions. To assess universality, we project embeddings from five independent transformer models spanning five typologically distinct language families—Indo-European (English, French), Uralic (Finnish), Altaic (Turkish), and Japonic (Japanese)—through the same frozen, English-trained manifold. The resulting coordinate systems exhibit striking geometric isomorphism: core structural axes such as Power and Agency show cross-model variance, and all five languages agree on directional polarity for nine of eleven axes. Inter-axis correlation analysis reveals that the recovered basis is structurally oblique, with stable covariances (e.g., Power–Sociality ) that challenge the widespread orthogonality assumption in representation learning. These findings establish the Universal Semantic Manifold as a compact, interpretable coordinate system that bridges connectionist representations and symbolic cognition, offering a rigorous geometric framework for cross-lingual alignment, controlled semantic steering, and the quantitative study of conceptual structure.