Gestalt Reasoning Machines: Structured Perception for Neuro-Symbolic Inference

Jingyuan Sha, Hikaru Shindo, Kristian Kersting, Devendra Singh Dhami

Keywords: Gestalt Vision, Logic, Neuro-Symbolic AI

Abstract:

This paper introduces Gestalt Reasoning Machines (GRMs), a novel neuro-symbolic framework that integrates Gestalt principles to enhance reasoning models with perception capabilities similar to human cognition.
Traditional models, which rely on large datasets and complex computations, often overlook the crucial human cognitive function of grouping, resulting in inefficiencies when dealing with abstract concepts. GRMs address this challenge by incorporating a grouping mechanism grounded in Gestalt principles, enabling the system to recognize and reason over complex visual patterns that are otherwise difficult to capture through object-level features alone.
This grouping capability allows GRMs to identify higher-order structures and relational configurations that are essential for human-like reasoning. We demonstrate that GRMs outperform purely neural baselines by leveraging logic-based reasoning infused with perceptual grouping cues, offering a more interpretable and cognitively aligned approach.
Our contributions include the design of GRMs and the empirical validation of their effectiveness in visual reasoning tasks that demand structured perception.

Primary Area: neurosymbolic & hybrid AI systems (physics-informed, logic & formal reasoning, etc.)

Submission Number: 7270