ARC-AGI measures how well a system can infer a rule from a few examples and apply it to a new problem. Instead of asking only what an AI has memorized, it presents small visual puzzles and checks whether the system can produce the exact output grid for an unfamiliar input. The benchmark is designed to probe fluid intelligence and efficient skill acquisition, not general knowledge recall alone.
What ARC-AGI is designed to measure
ARC-AGI stands for Abstraction and Reasoning Corpus for Artificial General Intelligence. François Chollet introduced it in 2019 with his paper On the Measure of Intelligence. Its premise is that intelligence should be assessed by how efficiently a system acquires skills and generalizes them to unfamiliar tasks, rather than by performance on capabilities that can be built through extensive training or memorized knowledge. The ARC Prize Foundation describes intelligence as “a measure of its skill-acquisition efficiency over a scope of tasks, with respect to priors, experience, and generalization difficulty.” ARC Prize Foundation.
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In practical terms, a solver sees a few examples of grids before and after an unstated transformation. It must infer the underlying rule and use it to transform a new grid. The grids use discrete symbols shown as colors; the point is not simply to recognize a color or repeat a visible pattern, but to discover the relation that explains the examples.
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The official task format uses JSON with a train collection of example input/output pairs and a test collection of new inputs. The solver must construct the output for the test input. For ARC-AGI-2, tasks typically have three training pairs, although the guide allows two to ten; test collections typically contain one case, with one to three possible. ARC-AGI guide.
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- Inspect the examples. Compare each input grid with its corresponding output and look for a transformation that accounts for the changes.
- Infer the rule. Identify what is changing and what stays consistent. Some tasks may require interpreting symbols, combining rules, or using context to decide which rule applies.
- Apply the rule to the test input. Produce the output grid rather than selecting a label or explaining the answer in words.
- Match the answer exactly. A task counts as solved only if the output is correct in its dimensions, colors, and positions. In ARC-AGI-1, the repository likewise defines success by a correct test output grid, including its dimensions. ARC-AGI-1 repository.
What ARC-AGI-2 changed
ARC-AGI-2 preserves the original input-output grid format but uses a newly curated, expanded task set intended to measure more detailed performance at higher cognitive complexity. Its stated design goals include making brute-force search less effective, using first-party human testing, and calibrating the public, semi-private, and private evaluation sets to similar difficulty distributions. ARC-AGI guide.
The ARC Prize Foundation highlights three patterns the newer tasks are designed to challenge:
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- Symbolic interpretation: assigning meaning to a symbol beyond how it looks.
- Compositional reasoning: applying multiple rules together, particularly when they interact.
- Contextual rule application: choosing a rule based on context instead of relying on a superficial pattern.
These are design challenges, not a claim that every task tests all three. ARC-AGI-2 is not a different kind of puzzle interface; it raises the intended complexity while retaining the grid-task format.
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How scoring and evaluation work
A solver’s output is compared with the validated answer. It is scored as correct only when the grid matches in shape, colors, and positions; a nearly correct image is not a solved task. This exact-match requirement makes clear what the score represents, but it also means the score should be read alongside the benchmark edition and evaluation split.
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The current guide lists 1,000 public training tasks and 120 public evaluation tasks, plus separate 120-task semi-private and private evaluation sets for leaderboard and competition use. These counts and protocols are version-specific and can change. The guide warns that repeatedly tuning against evaluation scores can leak information from the evaluation set into system development, undermining the separation between development and measurement. ARC-AGI guide.
When quoting a result, identify the benchmark edition, split, scoring protocol, and date. For example, the ARC Prize 2025 technical report records 1,455 teams and 15,154 entries, and reports that the first-place NVARC entry scored 24.03% on the ARC-AGI-2 private evaluation set. That is a result from the 2025 competition, reported by the ARC Prize Foundation in 2026—not a live leaderboard reading or a score for AI systems as a whole. ARC Prize technical reports and competition results.
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What the human baseline does—and does not—show
The ARC Prize Foundation’s 2025 technical-report page says its human study tested 400 people on 1,417 unique tasks. A task was retained if at least two people solved it within two attempts; each task was attempted by about nine to ten participants on average. This supports the conclusion that the retained tasks were human-solvable under those study conditions. It does not mean every participant solved every task or achieved a perfect score. ARC Prize technical reports.
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ARC-AGI offers a focused measure of a system’s ability to infer and transfer rules in a particular kind of visual puzzle. It does not, by itself, establish broad intelligence across all domains. For a result to be interpretable, check:
Quick Recap
- Edition: ARC-AGI-1 and ARC-AGI-2 use different task sets and should not be treated as interchangeable.
- Evaluation split: public, semi-private, and private results refer to different sets.
- Scoring protocol: exact-grid correctness is stricter than partial credit or approximate visual similarity.
- Date and context: competition results are dated snapshots, not necessarily current leaderboard standings.
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