  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.
