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 2026-09-29
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