LLMICL

January 23, 2026 · View on GitHub

noisy_logistic_snapshots

Overview

This repository contains official implementation of the paper:

LLMs learn governing principles of dynamical systems, revealing an in-context neural scaling law

It contains code and data necessary to reproduce the experiments and generate the figures presented in the paper. Some of our data processing routine is adapted from llmtime.

Directory structure

An overview of the repository's structure and contents:

  • /data: Contains functions for converting time series data into string formats suitable for processing, alongside series_generator.ipynb, a Jupyter notebook for generating the dynamical system series explored in the paper.
  • /generated_series: This directory holds all the dynamical time series data generated by the series_generator.ipynb.
  • /models: Includes loaders for both the LLaMA and GPT models. It features ICL.py, which outlines essential packages like Hierarchy-PDF and its auxiliary functions. Additionally, generate_predictions.py is provided for applying the selected Large Language Model (LLM) to input time series and saving the outcomes using Hierarchy-PDF.
  • /processed_series: A storage point for the predictions made by the LLM on each time series.
  • /figure_analysis: Comprises routines for evaluating the LLM predictions found in the /processed_series directory, including the computation of metrics such as loss curves. It enables the reproduction of all figures presented in the paper.
  • /figures: A repository for all figures generated through the analysis processes.

Usage guidelines

Follow these steps to utilize the repository for generating dynamical system series, processing them with LLMs, and analyzing the results:

  1. Series Generation:

    • Navigate to the /data directory and open series_generator.ipynb.
    • Define prediction tasks within this notebook, specifying one trajectory to predict per task, along with configurations for the chosen LLM and Hierarchy-PDF.
    • Execute the notebook to generate task files, and save these in the /generated_series directory as pickle files.

  2. Prediction Processing:

    • Use generate_predictions.py found in the /models directory to process the generated series through the LLM, applying Hierarchy-PDF for prediction.
    • Save the processed task files, now containing LLM predictions, in the /processed_series directory as pickle files.

  3. Analysis and Figure Generation:

    • With predictions stored in /processed_series, employ the routines within /figure_analysis to examine the transition rules extracted by the LLM.
    • Generate and review plots to visualize the analysis, replicating the figures from the paper.

Authors