Fundamental algorithms such as sorting or hashing are used trillions of times on any given
day. As demand for computation grows, it has become critical for these algorithms to be as …

Combinatorial optimization is a well-established area in operations research and computer
science. Until recently, its methods have focused on solving problem instances in isolation …

Given the recent impressive accomplishments of language models (LMs) for code
generation, we explore the use of LMs as general adaptive mutation and crossover …

Transformers have impressive generalization capabilities on tasks with a fixed context
length. However, they fail to generalize to sequences of arbitrary length, even for seemingly …

Despite recent success in large language model (LLM) reasoning, LLMs struggle with
hierarchical multi-step reasoning tasks like generating complex programs. For these tasks …

Recent work on neural algorithmic reasoning has investigated the reasoning capabilities of
neural networks, effectively demonstrating they can learn to execute classical algorithms on …

Logical reasoning, ie, deductively inferring the truth value of a conclusion from a set of
premises, is an important task for artificial intelligence with wide potential impacts on …

Vehicle routing problems (VRP) are very important in many real-world applications and has
been studied for several decades. Recently, neural combinatorial optimization (NCO) has …

In this paper, we study the OOD generalization of neural algorithmic reasoning tasks, where
the goal is to learn an algorithm (eg, sorting, breadth-first search, and depth-first search) …

State-of-the-art neural algorithmic reasoners make use of message passing in graph neural
networks (GNNs). But typical GNNs blur the distinction between the definition and invocation …