# Solving the Clustering Reasoning Problems by Modeling a Deep-Learning-Based Probabilistic Model

arXiv: https://arxiv.org/abs/2403.03173 · HTML: https://mlsanity.com/paper/2403.03173/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Mar 2024.

## Built on by (most cited first, 3 of 3)

- [Funny-Valen-Tine: Planning Solution Distribution Enhances Machine Abstract Reasoning Ability](https://mlsanity.com/paper/2407.02688/) (2024)
- [Symbolic Grounding Reveals Representational Bottlenecks in Abstract Visual Reasoning](https://mlsanity.com/paper/2604.21346/) (2026)
- [Johnny: Structuring Representation Space to Enhance Machine Abstract Reasoning Ability](https://mlsanity.com/paper/2506.01970/) (2025)

## Abstract (arXiv)

Visual abstract reasoning problems pose significant challenges to the perception and cognition abilities of artificial intelligence algorithms, demanding deeper pattern recognition and inductive reasoning beyond mere identification of explicit image features. Research advancements in this field often provide insights and technical support for other similar domains. In this study, we introduce PMoC, a deep-learning-based probabilistic model, achieving high reasoning accuracy in the Bongard-Logo, which stands as one of the most challenging clustering reasoning tasks. PMoC is a novel approach for constructing probabilistic models based on deep learning, which is distinctly different from previous techniques. PMoC revitalizes the probabilistic approach, which has been relatively weak in visual abstract reasoning.