What AstroPT knows about galaxies, and what that can teach us about LLMs
Interpretability research increasingly asks when concepts emerge during training and whether linear probes recover real structure, but in language models these claims are hard to validate because language offers little ground-truth ordering of concepts or relationships among them. We propose the use of astronomical ground truth through AstroPT, a transformer trained on millions of galaxy images, as a calibration testbed. AstroPT is an LLM-like model trained within a domain where the difficulty ordering of concepts and the relations among them are known in advance. Probing frozen representations across checkpoints, layers, model sizes, and objective choices, we find that galaxy properties ...
| Authors | UniverseTBD, :, Kshitij Duraphe et al. |
|---|---|
| Primary category | cs.LG |
| Published | 23 August 2026 |
| Last updated at source | 23 August 2026 |
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