This paper introduces the novel task of multimodal puzzle solving, framed
within the context of visual question-answering. We present a new dataset,
AlgoPuzzleVQA designed to challenge and evaluate the capabilities of multimodal
language models in solving algorithmic puzzles that necessitate both visual
understanding, language understanding, and complex algorithmic reasoning. We
create the puzzles to encompass a diverse array of mathematical and algorithmic
topics such as boolean logic, combinatorics, graph theory, optimization,
search, etc., aiming to evaluate the gap between visual data interpretation and
algorithmic problem-solving skills. The dataset is generated automatically from
code authored by humans. All our puzzles have exact solutions that can be found
from the algorithm without tedious human calculations. It ensures that our
dataset can be scaled up arbitrarily in terms of reasoning complexity and
dataset size. Our investigation reveals that large language models (LLMs) such
as GPT4V and Gemini exhibit limited performance in puzzle-solving tasks. We
find that their performance is near random in a multi-choice question-answering
setup for a significant number of puzzles. The findings emphasize the
challenges of integrating visual, language, and algorithmic knowledge for
solving complex reasoning problems.

这篇论文介绍了一项新颖的任务，即多模式拼图解决，其框架是视觉问题回答。我们提出了一个名为 AlgoPuzzleVQA 的新数据集，旨在挑战和评估多模态语言模型在解决需要视觉理解、语言理解和复杂算法推理的算法拼图中的能力。我们的研究表明，GPT4V 和 Gemini 等大型语言模型在拼图解决任务中表现有限，对于大量拼图的多选题回答设置，它们的性能接近随机。这些发现突出了整合视觉、语言和算法知识以解决复杂推理问题的挑战。