Predictive Coding Objectives for Operator Discovery

Abstract

The compositional latent dynamics (CLD) framework relies on a discrete library of learned operators; how those operators are discovered has been a sometimes-arbitrary part of the pipeline. We propose a predictive-coding objective that discovers operator boundaries directly from sequential observation streams, and show that operator libraries discovered under the objective produce dynamics models that are uniformly more sample-efficient than libraries discovered by clustering on temporal-difference signals. The discovered operators are interpretable in the same sense as the hand-curated ones from CLD.

Index metadata

Cell

voronoi-19

Compute

61 H100-days

Status

Open release

Code

github.com/alphabell-labs/ab-operators

DOI

10.48550/arXiv.2504.04019

arXiv

arXiv:2504.04019

What this paper is part of

This index entry is part of the World models research axis. The producing cell — voronoi-19 — collaborates with adjacent cells listed in the cell directory. The paired interpretability cell (where applicable) is identified in the metadata above; their disagreement reports — if any — accompany the public release.

How to read this

If you want to use the result: the code (where available) is at https://github.com/alphabell-labs/ab-operators; the dataset is at TBD when one is released. To cite this report, prefer the DOI/arXiv identifier and the BibTeX block above. To discuss this with the producing cell, contact the lab with the index entry slug predictive-coding-operator-discovery.

Limitations

Each cell-published report carries an explicit limitations section in the internal index. We do not paraphrase it here. Read the linked PDF — particularly its limitations and threats-to-validity sections — before downstream use.