# Shared Geometry Is Not Shared Physics: A Layerwise Test of the Platonic Representation Hypothesis in Astronomy

arXiv: https://arxiv.org/abs/2610.04130 · HTML: https://mlsanity.com/paper/2610.04130/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 23 of its 41 references that are in the map.
- First posted on arXiv 2 Oct 2026.

## Builds on (most cited first, 23 of 23)

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- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
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- [A ConvNet for the 2020s](https://mlsanity.com/paper/2201.03545/) (2022)
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- [On the Spectral Bias of Neural Networks](https://mlsanity.com/paper/1806.08734/) (2018)
- [Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture](https://mlsanity.com/paper/2301.08243/) (2023)
- [A Closer Look at Memorization in Deep Networks](https://mlsanity.com/paper/1706.05394/) (2017)
- [Revisiting Feature Prediction for Learning Visual Representations from Video](https://mlsanity.com/paper/2404.08471/) (2024)
- [The Platonic Representation Hypothesis](https://mlsanity.com/paper/2405.07987/) (2024)
- [PaliGemma 2: A Family of Versatile VLMs for Transfer](https://mlsanity.com/paper/2412.03555/) (2024)
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- [Understanding the Emergence of Multimodal Representation Alignment](https://mlsanity.com/paper/2502.16282/) (2025)
- [Back into Plato's Cave: Examining Cross-modal Representational Convergence at Scale](https://mlsanity.com/paper/2604.18572/) (2026)
- [Practical Galaxy Morphology Tools from Deep Supervised Representation Learning](https://mlsanity.com/paper/2110.12735/) (2021)
- [DeepMerge II: Building Robust Deep Learning Algorithms for Merging Galaxy Identification Across Domains](https://mlsanity.com/paper/2103.01373/) (2021)
- [The Platonic Universe: Do Foundation Models See the Same Sky?](https://mlsanity.com/paper/2509.19453/) (2025)
- [Toward a Theory of Generalizability in LLM Mechanistic Interpretability Research](https://mlsanity.com/paper/2509.22831/) (2025)
- [What Does a Chemical Language Model Know About Molecules?](https://mlsanity.com/paper/2606.23443/) (2026)
- [Not all solutions are created equal: An analytical dissociation of functional and representational similarity in deep linear neural networks](https://mlsanity.com/paper/2609.38998/) (2026)

## Abstract (arXiv)

In this paper we investigate whether geometrically aligned astronomical representations are also scientifically interchangeable. We analyze every layer's representation from 36 pretrained models using cross-matched optical images, infrared images, and spectra. All 139 analyzed model-survey comparisons show significant local-neighborhood alignment somewhere in the network after accounting for the search over depth. However, alignment does not generally increase with network depth and can be stronger for the changes between consecutive computational blocks than for the block outputs themselves. We find that geometry-selected stages under-perform label-selected stages in every image-transfer comparison; moreover, every final HSC-COSMOS-Web model pair is aligned while every redshift transfer has negative R^2. Models can therefore recover a similar cross-survey geometry among astronomical objects without recovering a survey-invariant linear encoding of the physical properties tested here.