Author

Philipp Scholl

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#artificial intelligence Nov 2025

When is a System Discoverable from Data? Discovery Requires Chaos

The deep learning revolution has enabled major AI advances in physical sciences, particularly in discovering dynamical systems from observational data. Yet the reliability of learned surrogates and symbolically discovered models is often undermined by non-uniqueness: models may fit observed trajectories perfectly, but...

Zakhar Shumaylov, P. Zaika, Philipp Scholl et al. · arXiv.org · 11 citations · ⚡1

#artificial intelligence Preprint Sep 2026

Good Pretraining, Bad SFT: Checkpoint Quality Across the Training Stack

It is shown that this assumption can fail in a full 30B mixture-of-experts training pipeline, and the checkpoints that perform better after the full downstream training stack also have higher solution density, i.e., retain downstream performance under local weight perturbations.

Sohir Maskey, Philipp Scholl, Jonas Knupp et al. · 0 citations