The 'Chain of Symbolic Regression' (CoSR) framework shifts automated scientific discovery from 'one-step' end-to-end modeling to a progressive, hierarchical chain that mimics human scientific advancement.
March 17, 2026
Original Paper
Data-driven Progressive Discovery of Physical Laws
arXiv · 2603.13727
The Takeaway
Standard symbolic regression often produces uninterpretable 'overfitted' equations for complex physical systems. By progressively combining knowledge units, this method successfully recapitulated the path from Kepler's laws to universal gravitation, providing a more robust path for discovering new physical laws from experimental data.
From the abstract
Symbolic regression is a powerful tool for knowledge discovery, enabling the extraction of interpretable mathematical expressions directly from data. However, conventional symbolic discovery typically follows an end-to-end, "one-step" process, which often generates lengthy and physically meaningless expressions when dealing with real physical systems, leading to poor model generalization. This limitation fundamentally stems from its deviation from the basic path of scientific discovery: physical