GraphARC: A Comprehensive Benchmark for Graph-Based Abstract Reasoning

• URL: http://arxiv.org/abs/2605.31031v1
• Date: Fri, 29 May 2026 09:03:30 GMT
• Title: GraphARC: A Comprehensive Benchmark for Graph-Based Abstract Reasoning
• Abstract summary: We introduce GraphARC, a benchmark for abstract reasoning on graph-structured data.<n>Each task requires inferring a transformation rule from few input-output pairs applying it to a new test graph.<n>We evaluate state-of-the-art language models on GraphARC and observe clear limitations.
• Score: 28.844098517315228
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Relational reasoning lies at the heart of intelligence, but existing benchmarks are typically confined to formats such as grids or text. We introduce GraphARC, a benchmark for abstract reasoning on graph-structured data. GraphARC generalizes the few-shot transformation learning paradigm of the Abstraction and Reasoning Corpus (ARC). Each task requires inferring a transformation rule from a few input-output pairs and applying it to a new test graph, covering local, global, and hierarchical graph transformations. Unlike grid-based ARC, GraphARC instances can be generated at scale across diverse graph families and sizes, enabling systematic evaluation of generalization abilities. We evaluate state-of-the-art language models on GraphARC and observe clear limitations. Models can answer questions about graph properties but often fail to solve the full graph transformation task, revealing a comprehension-execution gap. Performance further degrades on larger instances, exposing scaling barriers. More broadly, by combining aspects of node classification, link prediction, and graph generation within a single framework, GraphARC provides a promising testbed for future graph foundation models.

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