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.

Figures (6)

• Figure 1: Frontier AI performance on ARC-AGI since introduction in 2019.
• Figure 2: Screenshot of ARC-AGI-3 environment ls20.
• Figure 3: First level of ls20 in graph form. Notice the three repeating states -- an artifact of the three-life mechanic of the level. $P_{\text{win}}$ for this level is exactly 1 in 355.
• Figure 4: Participant demographics.
• Figure 5: Time spent on environments by outcome, split between successful runs ("correct") and unsucessful runs ("correct").