Astronomical foundation models are increasingly used as reusable representations across surveys, but it is unclear when the same object remains consistently represented across instruments or whether a model can warn us before transfer fails. We present a conformal framework for auditing cross-survey representations usi...
Vaidehi Bulusu, Mehul Goyal, Md Rabius Sany Apu et al.· 0 citations
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 gro...
UniverseTBD Kshitij Duraphe, A. Kumar, Michael J. Smith et al.· 0 citations
The Platonic Representation Hypothesis predicts that sufficiently scaled foundation models converge on a shared representation of the world. As each non-converged model gives a noisy view of a common structure when passed the same input, we ask whether we can combine models into a representation that outperforms its in...
The results support the ARH over the strict PRH, demonstrate astronomy's value as an experimental framework for neural representation learning, 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 com...
UniverseTBD Trinidad Borrell, S. Dillmann, Kshitij Duraphe et al.· arXiv.org· 6 citations
Results show that capability failures can manifest as distributed, task-dependent changes in the structure of visible reasoning, and that CoT dynamics agnostic to whether the verbalized trace reflects the model's internal computations can help diagnose and correct failures.
Shashwat Sourav, Aishwarya H. Balwani· 1 citation
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