arXiv:2605.31031v1 Announce Type: new 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 ins
The continuous drive to advance AI capabilities necessitates more robust and generalizable benchmarks beyond traditional formats like grids or text.
A benchmark for abstract reasoning on graph-structured data represents a significant step towards more sophisticated and human-like AI reasoning, crucial for complex problem-solving.
AI research now has a specialized framework, GraphARC, for evaluating abstract reasoning on graph data, distinguishing it from prior grid-based or text-based assessments.
- · AI research labs
- · Graph AI startups
- · Generative AI developers
- · Robotics
- · Companies relying on simplistic AI benchmarks
- · Legacy AI companies
Improved abstract reasoning capabilities in AI models trained and evaluated on GraphARC.
AI systems demonstrating enhanced ability to generalize and solve novel problems in complex, relational domains.
Acceleration in the development of more autonomous and adaptive AI agents for real-world applications.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.AI