Title: F6-Net: Algorithmic Reasoning through Gated Pathways and Min-Reduction
Primary area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
Keywords: Neural Algorithmic Reasoning; Graph Neural Networks; Graph Reasoning; Algorithms.

Abstract: Neural Algorithmic Reasoning (NAR) is the research area that aims to build artificial neural networks that can mimic (classical) algorithms, reproducing intermediary steps from their execution traces.
NAR expects to enhance neural network generalization and help to develop more efficient, adaptable, and faster algorithms. 
This capability makes it a highly promising approach for dynamic systems in unpredictable, real-world environments.
Among the existing methods for algorithm reproduction, the Message Passing Neural Network (MPNN) architecture and its variations, such as Triplet-GMPNN, stand out.
This paper proposes a novel variant of Triplet-GMPNN, characterized by three key modifications: a streamlined message-passing process, a new gating-type activation mechanism, and the use of a minimum-type function for embedding reduction.
To ascertain the individual contribution of each component, a comprehensive ablative analysis was conducted. This study evaluates each architectural modification through the lens of algorithmic alignment.
This work advances the understanding of these systems and opens up new design possibilities for future Neural Algorithmic Reasoning architectures.

TL;DR: This paper introduces a novel Neural Algorithmic Reasoning model featuring a streamlined message-passing process, an improved gating mechanism, and uses a minimum-type reduction function for embedding summarization.