This comprehensive study evaluates the performance of OpenAI's o1-preview large
language model across a diverse array of complex reasoning tasks, spanning multiple …

While vertical thinking relies on logical and commonsense reasoning, lateral thinking
requires systems to defy commonsense associations and overwrite them through …

As a core cognitive skill that enables the transferability of information across domains,
analogical reasoning has been extensively studied for both humans and computational …

Large Language Models (LLMs) are often described as being instances of foundation
models-that is, models that transfer strongly across various tasks and conditions in few-show …

Evaluating the cognitive capacities of large language models (LLMs) requires overcoming
not only anthropomorphic but also anthropocentric biases. This article identifies two types of …

Abstract Large Language Models (LLMs) have taken the field of AI by storm, but their
adoption in the field of Artificial Life (ALife) has been, so far, relatively reserved. In this work …

We recently reported evidence that large language models are capable of solving a wide
range of text-based analogy problems in a zero-shot manner, indicating the presence of an …

Effective meetings are effortful, but traditional videoconferencing systems offer little support
for reducing this effort across the meeting lifecycle. Generative AI (GenAI) has the potential …

We study the depth of grade-school math (GSM) problem-solving capabilities of LLMs. To
this end, we evaluate their performance on pairs of existing math word problems together so …

Achieving visual reasoning is a long-term goal of artificial intelligence. In the last decade,
several studies have applied deep neural networks (DNNs) to the task of learning visual …