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ARXIV:2605.31031 · GRAPH REASONING · SUBMITTED 01 JUN · 20:23 UTC · FRESHNESS STALE
ARXIV:2605.31031GRAPH REASONINGSUBMITTED 01 JUN · 20:23 UTCFRESHNESS STALESaku Peltonen · August Bøgh Rønberg · Andreas Plesner · Roger Wattenhofer · arXiv
GraphARC is a new benchmark for abstract reasoning on graph data, revealing limitations in current language models and paving the way for graph foundation models.
Opportunity summary
Pain GraphARC is a new benchmark for abstract reasoning on graph data, revealing limitations in current language models and paving the way for graph foundation models.
Evidence 0 refs | 3 sources | 50% coverage
Blocker Evidence unverified
GraphARC is a new benchmark for abstract reasoning on graph data, revealing limitations in current language models and paving the way for graph foundation models. We introduce GraphARC, a benchmark for abstract reasoning on…
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.
ScienceToStartup currently rates this 7.0/10 on the public viability pass. 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 Reasoning moved forward this cycle; last verified June 2026. Public score 7.0/10. Production flags indicate code availability.
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GraphARC is a new benchmark for abstract reasoning on graph data, revealing limitations in current language models and paving the way for graph foundation models.
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10.48550/arXiv.2605.31031GraphARC is a new benchmark for abstract reasoning on graph data, revealing limitations in current language models and paving the way for graph foundation models.
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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PROBLEM
GraphARC is a new benchmark for abstract reasoning on graph data, revealing limitations in current language models and paving the way for graph foundation models. We introduce GraphARC, a benchmark for abstract reasoning on graph-structured data.
METHOD
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.
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. 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. Code a...
WHY NOW
Graph Reasoning moved forward this cycle; last verified June 2026. Public score 7.0/10. Production flags indicate code availability.
{"file name": "input.pdf", "number of pages": 12, "author": "Saku Peltonen; August B\u00f8gh R\u00f8nberg; Andreas Plesner; Roger Wattenhofer"
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GraphARC is a new benchmark for abstract reasoning on graph data, revealing limitations in current language models and paving the way for graph foundation models.
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