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1. Verfasser: Curry, Paul
Format: Preprint
Veröffentlicht: 2025
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Online-Zugang:https://arxiv.org/abs/2509.08180
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author Curry, Paul
author_facet Curry, Paul
contents The Domain Mixed Unit (DMU) is a new neural arithmetic unit that learns a single parameter gate that mixes between log-space and linear-space representations while performing either addition (DMU add) or subtraction (DMU sub). Two initializations are proposed for the DMU: one covering addition and multiplication, and another covering subtraction and division. The DMU achieves state-of-the-art performance on the NALM Benchmark, a dataset designed to test the ability of neural arithmetic units to generalize arithmetic operations, specifically performing with the highest percentage solved over all seeds on multiplication and division. The DMU will be submitted as a pull request to the open-source NALM benchmark, and its code is available on GitHub at https://github.com/marict/nalm-benchmark
format Preprint
id arxiv_https___arxiv_org_abs_2509_08180
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle The Domain Mixed Unit: A New Neural Arithmetic Layer
Curry, Paul
Machine Learning
68T07
I.2.6
The Domain Mixed Unit (DMU) is a new neural arithmetic unit that learns a single parameter gate that mixes between log-space and linear-space representations while performing either addition (DMU add) or subtraction (DMU sub). Two initializations are proposed for the DMU: one covering addition and multiplication, and another covering subtraction and division. The DMU achieves state-of-the-art performance on the NALM Benchmark, a dataset designed to test the ability of neural arithmetic units to generalize arithmetic operations, specifically performing with the highest percentage solved over all seeds on multiplication and division. The DMU will be submitted as a pull request to the open-source NALM benchmark, and its code is available on GitHub at https://github.com/marict/nalm-benchmark
title The Domain Mixed Unit: A New Neural Arithmetic Layer
topic Machine Learning
68T07
I.2.6
url https://arxiv.org/abs/2509.08180