Author

Shashwat Sourav

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Preprint Aug 2026

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 gro...

UniverseTBD Kshitij Duraphe, Aman Kumar, Michael J. Smith et al. · 0 citations

#machine learning Sep 2025

The Platonic Universe: Do Foundation Models See the Same Sky?

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

Preprint Aug 2026

The Tell-Tale Trace: Detecting Reasoning Failures in LLMs Using Chain-of-Thought Dynamics

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