newton-kepler

Code for a research paper studying what physics or algorithms transformers learn internally when trained to predict planetary motion. Used to explore how learned world models relate to Kepler and Newton style laws.

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Overview

This repository holds the code for a research paper titled From Kepler to Newton: Inductive Biases Guide Learned World Models in Transformers. It studies what a transformer learns internally when trained to predict planetary motion, and whether it behaves like a Keplerian or a Newtonian world model. The experiments use 1D sine waves and 2D Kepler orbits, with training scripts and analysis notebooks that reproduce the paper figures.

Key features

  • Training scripts for classification and regression transformers
  • Experiments on 1D sine waves and 2D Kepler orbits
  • Notebooks that reproduce figures and analyze training dynamics
  • Compares full-context attention with local attention

Best for

Researchers studying learned world models or interpretability who want to reproduce and extend the paper's experiments.

Upstream
KindXiaoming/newton-kepler
Fork on GitHub
Guo-astro/newton-kepler
Upstream stars
50
Category
Finance, data and research
License
No license declaredWithout a license, the author keeps all rights. Ask the upstream owner before reusing the code.
Forked
2026-02-09
Sync status
In syncLast synced 2026-10-19

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