arXiv:2603.24621cs.AI
ARC-AGI-3: A New Challenge for Frontier Agentic Intelligence
Abstract
We introduce ARC-AGI-3, an interactive benchmark for studying agentic intelligence through novel, abstract, turn-based environments in which agents must explore, infer goals, build internal models of environment dynamics, and plan effective action sequences without explicit instructions. Like its predecessors ARC-AGI-1 and 2, ARC-AGI-3 focuses entirely on evaluating fluid adaptive efficiency on novel tasks, while avoiding language and external knowledge. ARC-AGI-3 environments only leverage Core Knowledge priors and are difficulty-calibrated via extensive testing with human test-takers. Our testing shows humans can solve 100% of the environments, in contrast to frontier AI systems which, as of March 2026, score below 1%. In this paper, we present the benchmark design, its efficiency-based scoring framework grounded in human action baselines, and the methodology used to construct, validate, and calibrate the environments.
>> To me, this is more or less what I would think "Artificial General Intelligence" is So then you need to explain ARC-AGI-3: https://arxiv.org/abs/2603.24621 "Our testing shows humans can solve 100% of the environments, in contrast to frontier AI systems which, as of March…
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> So then you need to explain ARC-AGI-3: https://arxiv.org/abs/2603.24621 I don't, we originally had the turing test which was designed to determine human intelligence by its ability to imitate us with natural dialogue, but we've since defeated that. I stated "to me" because…
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