On Logical Extrapolation for Mazes with Recurrent and Implicit Networks
Brandon Knutson, Amandin Chyba Rabeendran, Michael Ivanitskiy, Jordan Pettyjohn, Cecilia Diniz Behn, Samy Wu Fung, Daniel McKenzie
Abstract
Recent work suggests that certain neural network architectures — particularly recurrent neural networks (RNNs) and implicit neural networks (INNs) — are capable of logical extrapolation. When trained on easy instances of a task, these networks (henceforth: logical extrapolators) can generalize to more difficult instances. Previous research has hypothesized that logical extrapolators do so by learning a scalable, iterative algorithm for the given task which converges to the solution. We examine this idea more closely in the context of a single task: maze solving. By varying test data along multiple axes — not just maze size — we show that models introduced in prior work fail in a variety of ways, some expected and others less so. It remains uncertain whether any of these models has truly learned an algorithm. However, we provide evidence that a certain RNN has approximately learned a form of `deadend-filling
BibTeX
@inproceedings{aaai2026_onlogicalextrapo,
title = {On Logical Extrapolation for Mazes with Recurrent and Implicit Networks},
author = {Brandon Knutson and Amandin Chyba Rabeendran and Michael Ivanitskiy and Jordan Pettyjohn and Cecilia Diniz Behn and Samy Wu Fung and Daniel McKenzie},
booktitle = {AAAI 2026},
year = {2026}
}