Strong Artificial Intelligence (Strong AI) or Artificial General Intelligence
(AGI) with abstract reasoning ability is the goal of next-generation AI. Recent
advancements in Large Language Models (LLMs), along with the emerging field of
Multimodal Large Language Models (MLLMs), have demonstrated impressive
capabilities across a wide range of multimodal tasks and applications.
Particularly, various MLLMs, each with distinct model architectures, training
data, and training stages, have been evaluated across a broad range of MLLM
benchmarks. These studies have, to varying degrees, revealed different aspects
of the current capabilities of MLLMs. However, the reasoning abilities of MLLMs
have not been systematically investigated. In this survey, we comprehensively
review the existing evaluation protocols of multimodal reasoning, categorize
and illustrate the frontiers of MLLMs, introduce recent trends in applications
of MLLMs on reasoning-intensive tasks, and finally discuss current practices
and future directions. We believe our survey establishes a solid base and sheds
light on this important topic, multimodal reasoning.

综合评估了多模态大语言模型的现有评估协议，分类和说明了多模态大语言模型的前沿，介绍了多模态大语言模型在推理密集型任务上的最新趋势，并讨论了当前的实践和未来的发展方向。

多模态大型语言模型（MLLMs）的推理能力探索：多模态推理中新兴趋势的综述

Exploring the Reasoning Abilities of Multimodal Large Language Models  (MLLMs): A Comprehensive Survey on Emerging Trends in Multimodal Reasoning

In the monograph "Strong artificial intelligence. On the Approaches to
Superintelligence" contains an overview of general artificial intelligence
(AGI). As an anthropomorphic research area, it includes Brain Principles
Programming (BPP) -- the formalization of universal mechanisms (principles) of
the brain work with information, which are implemented at all levels of the
organization of nervous tissue. This monograph contains a formalization of
these principles in terms of category theory. However, this formalization is
not enough to develop algorithms for working with information. In this paper,
for the description and modeling of BPP, it is proposed to apply mathematical
models and algorithms developed earlier, which modeling cognitive functions and
base on well-known physiological, psychological and other natural science
theories. The paper uses mathematical models and algorithms of the following
theories: P.K.Anokhin Theory of Functional Brain Systems, Eleanor Rosch
prototypical categorization theory, Bob Rehder theory of causal models and
"natural" classification. As a result, a formalization of BPP is obtained and
computer experiments demonstrating the operation of algorithms are presented.

该文是一篇关于强人工智能的论文，提出了基于类别论的 Brain Principles Programming（BPP）的形式化方法，并应用数学模型和算法对其进行描述和建模，最终得到了计算机实验演示算法操作的结果。

大脑编程原理的规范化

Formalization of the principles of brain Programming (Brain Principles Programming)

In the monograph, STRONG ARTIFICIAL INTELLIGENCE. On the Approaches to
Superintelligence, published by Sberbank, provides a cross-disciplinary review
of general artificial intelligence. As an anthropomorphic direction of
research, it considers Brain Principles Programming, BPP) the formalization of
universal mechanisms (principles) of the brain's work with information, which
are implemented at all levels of the organization of nervous tissue. This
monograph provides a formalization of these principles in terms of the category
theory. However, this formalization is not enough to develop algorithms for
working with information. In this paper, for the description and modeling of
Brain Principles Programming, it is proposed to apply mathematical models and
algorithms developed by us earlier that model cognitive functions, which are
based on well-known physiological, psychological and other natural science
theories. The paper uses mathematical models and algorithms of the following
theories: P.K.Anokhin's Theory of Functional Brain Systems, Eleonor Rosh's
prototypical categorization theory, Bob Rehter's theory of causal models and
natural classification. As a result, the formalization of the BPP is obtained
and computer examples are given that demonstrate the algorithm's operation.

该论文介绍了强人工智能领域中一个交叉学科的研究，提出了 “脑部原理编程” 的概念， 运用数学模型和算法为 BPP 进行了建模， 并提供了相关的计算机示例。