Compositional Neuro-Symbolic Reasoning

• URL: http://arxiv.org/abs/2604.02434v1
• Date: Thu, 02 Apr 2026 18:04:34 GMT
• Title: Compositional Neuro-Symbolic Reasoning
• Abstract summary: We study structured abstraction-based reasoning for the Abstraction and Reasoning Corpus (ARC)<n>We propose a neuro-symbolic architecture that extracts object-level structure from grids.<n>We open-source the ARC-AGI-2 Reasoner code.
• Score: 0.7894152109655374
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We study structured abstraction-based reasoning for the Abstraction and Reasoning Corpus (ARC) and compare its generalization to test-time approaches. Purely neural architectures lack reliable combinatorial generalization, while strictly symbolic systems struggle with perceptual grounding. We therefore propose a neuro-symbolic architecture that extracts object-level structure from grids, uses neural priors to propose candidate transformations from a fixed domain-specific language (DSL) of atomic patterns, and filters hypotheses using cross-example consistency. Instantiated as a compositional reasoning framework based on unit patterns inspired by human visual abstraction, the system augments large language models (LLMs) with object representations and transformation proposals. On ARC-AGI-2, it improves base LLM performance from 16% to 24.4% on the public evaluation set, and to 30.8% when combined with ARC Lang Solver via a meta-classifier. These results demonstrate that separating perception, neural-guided transformation proposal, and symbolic consistency filtering improves generalization without task-specific finetuning or reinforcement learning, while reducing reliance on brute-force search and sampling-based test-time scaling. We open-source the ARC-AGI-2 Reasoner code (https://github.com/CoreThink-AI/arc-agi-2-reasoner).

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