[2603.24621] ARC-AGI-3: A New Challenge for Frontier Agentic Intelligence
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arXiv:2603.24621 (cs)
[Submitted on 24 Mar 2026 (v1), last revised 17 Apr 2026 (this version, v2)]
Title:ARC-AGI-3: A New Challenge for Frontier Agentic Intelligence
Authors:ARC Prize Foundation<br>View a PDF of the paper titled ARC-AGI-3: A New Challenge for Frontier Agentic Intelligence, by ARC Prize Foundation
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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.
Subjects:
Artificial Intelligence (cs.AI)
Cite as:<br>arXiv:2603.24621 [cs.AI]
(or<br>arXiv:2603.24621v2 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2603.24621
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arXiv-issued DOI via DataCite
Submission history<br>From: Francois Chollet [view email]<br>[v1]<br>Tue, 24 Mar 2026 21:58:16 UTC (791 KB)
[v2]<br>Fri, 17 Apr 2026 09:35:41 UTC (795 KB)
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