Artificial Intelligence (AI) has been rapidly advancing and has demonstrated
its ability to perform a wide range of cognitive tasks, including language
processing, visual recognition, and decision-making. Part of this progress is
due to LLMs (Large Language Models) like those of the GPT (Generative
Pre-Trained Transformers) family. These models are capable of exhibiting
behavior that can be perceived as intelligent. Most authors in Neuropsychology
consider intelligent behavior to depend on a number of overarching skills, or
Executive Functions (EFs), which rely on the correct functioning of neural
networks in the frontal lobes, and have developed a series of tests to evaluate
them. In this work, we raise the question of whether LLMs are developing
executive functions similar to those of humans as part of their learning, and
we evaluate the planning function and working memory of GPT using the popular
Towers of Hanoi method. Additionally, we introduce a new variant of the
classical method in order to avoid that the solutions are found in the LLM
training data (dataleakeage). Preliminary results show that LLMs generates
near-optimal solutions in Towers of Hanoi related tasks, adheres to task
constraints, and exhibits rapid planning capabilities and efficient working
memory usage, indicating a potential development of executive functions.
However, these abilities are quite limited and worse than well-trained humans
when the tasks are not known and are not part of the training data.

研究表明 LLMs（大型语言模型）可以在 Towers of Hanoi 相关任务中生成近乎最优解，体现出计划能力和高效工作记忆利用率，这暗示了一种潜在的执行功能发展，但这些能力相对有限，而非经过充分训练的人类表现更好

人工神经心理学：大型语言模型是否能够发展执行功能？

Artificial Neuropsychology: Are Large Language Models Developing  Executive Functions?

We present a novel, general, optimally fast, incremental way of searching for
a universal algorithm that solves each task in a sequence of tasks. The Optimal
Ordered Problem Solver (OOPS) continually organizes and exploits previously
found solutions to earlier tasks, efficiently searching not only the space of
domain-specific algorithms, but also the space of search algorithms.
Essentially we extend the principles of optimal nonincremental universal search
to build an incremental universal learner that is able to improve itself
through experience. In illustrative experiments, our self-improver becomes the
first general system that learns to solve all n disk Towers of Hanoi tasks
(solution size 2^n-1) for n up to 30, profiting from previously solved, simpler
tasks involving samples of a simple context free language.

本文提出了一种新颖、通用、最优快速、增量式搜索通用算法的方法 ——Optimal Ordered Problem Solver（简称 OOPS），它能够通过经验自我提高，扩展了最优非增量式通用搜索的原理，成为第一个可以学习解决所有汉诺塔问题的通用系统，得益于先前解决的简单任务样例。