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Research PaperResearchia:202607.30074

Sky sphere representation in language models

Aleksandr Berdnikov

Abstract

We analyze whether language models of size ~100B have a representation of the night sky map that is decodable from their residual stream. We find that most of the considered open-source models do have such a representation, and it often even surfaces to the top principal components on prompts that ask questions like what is close to this object in the night sky''. In all but one model this representation showed significant scores in LOO testing, containing up to 65-85% of variance ($R^2$-score) ...

Submitted: July 30, 2026Subjects: Machine Learning; Data Science

Description / Details

We analyze whether language models of size ~100B have a representation of the night sky map that is decodable from their residual stream. We find that most of the considered open-source models do have such a representation, and it often even surfaces to the top principal components on prompts that ask questions like ``what is close to this object in the night sky''. In all but one model this representation showed significant scores in LOO testing, containing up to 65-85% of variance (R2R^2-score) and having median angular error down to 12βˆ˜βˆ’21∘12^\circ-21^\circ. We verify that our representation is not a simple leak from a correlated flat representation. To our knowledge, this representation is the first example of a curved high-dimensional irreducible feature manifold. Codes used in the paper are published at https://github.com/l3erdnik/Decodable-sky


Source: arXiv:2607.27092v1 - http://arxiv.org/abs/2607.27092v1 PDF: https://arxiv.org/pdf/2607.27092v1 Original Link: http://arxiv.org/abs/2607.27092v1

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Date:
Jul 30, 2026
Topic:
Data Science
Area:
Machine Learning
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