2026 workshop program
The Platonic Universe: Do Foundation Models See the Same Sky?
The work
Abstract
I will present our test of the Platonic Representation Hypothesis (PRH) and its Aristotelian refinement (ARH) by using diverse astronomical data to measure representational convergence across foundation models. We propose that astronomy is a natural testbed for this: the historical success of astrophysics is itself evidence that a compact, modality-invariant description of galaxy observables exists, and so representation convergence toward reality should be measurable against the physical parameters astronomers already use. Given this framework, we evaluate eleven foundation model families (spanning supervised classification, self-distillation, joint-embedding prediction, masked autoencoding, vision-language pre-training, and astronomy-specific architectures from O(10M) to O(10B) parameters) on crossmatched JWST, HSC, and Legacy Survey imagery, and DESI spectroscopy. All models are evaluated frozen, with no astronomy-specific fine-tuning. We probe redshift, stellar mass, and specific star formation rate via linear probes, and local (MKNN) and global (CKA) embedding geometry within families, between modalities, and across architectures. We find that physics performance scales predictably with capacity; probe directions align consistently with expected astrophysical correlations and selection effects; and local (but not global) embedding alignment tracks physics performance, including between DESI spectra and HSC imagery—modalities that share essentially no low-level statistics. Our results support the ARH over the strict PRH, and suggest that astro-foundation models can build on general-purpose pre-trained architectures, capitalizing on the broader open machine learning community’s already-spent computational investment.
The speaker
Biography
Mike is an AstroAI fellow at the Harvard-Smithsonian CfA and a recovering startup founder, having previously cofounded Aspia Space where he built AI for satellite imagery. He likes building open foundation models and pointing them at things – things like galaxies, stars, and the Earth – with a soft spot for self-supervised and generative methods applied to problems where deep learning supposedly “shouldn’t work”. He also helps run open research communities (like UniverseTBD, and the Multimodal Universe) trying to make foundation models in astronomy and science open and accessible to everyone.