Enterprise AI Analysis
ARC-AGI-2: A New Challenge for Frontier AI Reasoning Systems
The Abstraction and Reasoning Corpus for Artificial General Intelligence (ARC-AGI), introduced in 2019, established a challenging benchmark for evaluating the general fluid intelligence of artificial systems via a set of unique, novel tasks only requiring minimal prior knowledge. While ARC-AGI has spurred significant research activity over the past five years, recent AI progress calls for benchmarks capable of finer-grained evaluation at higher levels of cognitive complexity. We introduce ARC-AGI-2, an upgraded version of the benchmark. ARC-AGI-2 preserves the input-output pair task format of its predecessor, ensuring continuity for researchers. It incorporates a newly curated and expanded set of tasks specifically designed to provide a more granular signal to assess abstract reasoning and problem-solving abilities at higher levels of fluid intelligence. To contextualize the difficulty and characteristics of ARC-AGI-2, we present extensive results from human testing, providing a robust baseline that highlights the benchmark's accessibility to human intelligence, yet difficulty for current AI systems. ARC-AGI-2 aims to serve as a next-generation tool for rigorously measuring progress towards more general and human-like AI capabilities.
Key Executive Takeaways
ARC-AGI-2 represents a significant leap in AI benchmarking, designed to push the boundaries of general fluid intelligence. It effectively addresses the limitations of its predecessor, offering a more robust and nuanced evaluation of advanced reasoning capabilities. While humans achieve high accuracy (75%), current AI systems struggle significantly (3%), underscoring the substantial gap that remains in achieving human-like abstract reasoning.
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Top AI Score ARC-AGI-2
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Top AI Score ARC-AGI-1 (Prior Gen)
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From Pioneering to Progress
ARC-AGI-1, launched in 2019, introduced a novel benchmark for evaluating fluid intelligence through unique, grid-based reasoning tasks. It challenged AI systems to infer underlying rules from minimal examples, relying on innate human cognitive priors rather than vast domain knowledge.
55.5%
Highest ARC-AGI-1 Score (MindsAI Team, 2024)
This score, achieved by the MindsAI team in the ARC Prize 2024 competition, represented a significant breakthrough using Test-Time Adaptation, yet still fell short of human-level performance.
Despite significant research and scaling of large language models, ARC-AGI-1 revealed limitations. A substantial portion of tasks proved susceptible to computationally intensive brute-force search strategies, diluting the benchmark's focus on genuine abstract reasoning. Furthermore, inconsistencies in difficulty and a lack of first-party human baselines complicated progress measurement.
Elevating the Standard: ARC-AGI-2 Design Principles
ARC-AGI-2 was conceived in late 2021 to build upon the successes of its predecessor while directly addressing identified limitations. It retains the core task format and principles, emphasizing unique, non-memorizable challenges that require only elementary core knowledge.
Enterprise Process Flow
Maintain Core Principles
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Minimize Brute Force
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Extensive Human Testing
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Wider Difficulty Spectrum
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Calibrated Subsets
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Efficient Adaptation Focus
A primary objective is to create a benchmark that is significantly less brute-forcible, pushing AI research towards more efficient and adaptive reasoning. This is achieved through meticulously designed tasks that demand higher levels of compositional generalization and abstract thought, providing a wider "signal bandwidth" for measuring AI capabilities.
The Human Baseline: Robust Calibration
To ensure ARC-AGI-2's effectiveness, extensive first-party human testing was conducted. 407 participants across diverse professional backgrounds engaged in controlled 90-minute sessions, solving tasks presented via a custom user interface. This rigorous protocol established a robust human baseline, confirming task accessibility and difficulty.
75%
Average Human Accuracy on ARC-AGI-2 Test Pairs
This high human solvability, with a median solve time of 2.3 minutes, highlights ARC-AGI-2's feasibility for human intelligence while setting a clear target for AI systems.
Tasks were selected and curated based on strict criteria, including solvability by at least two independent participants within two attempts. Difficulty was carefully calibrated across Public, Semi-Private, and Private sets to ensure comparable distributions, enhancing the reliability of performance interpretations. Redundancy checks and a two-layer validation process further ensured task uniqueness and logical consistency.
Current AI Frontier: A Significant Gap Remains
Evaluations of state-of-the-art AI models on the ARC-AGI-2 Semi-Private Evaluation set reveal a stark contrast to human performance. While these models demonstrated significant progress on the original ARC-AGI-1 benchmark, they struggle to generalize to the increased complexity and uniqueness of ARC-AGI-2 tasks.
| Model | ARC-AGI-1 Score | ARC-AGI-2 Score |
|---|---|---|
| 03-mini (High) | 34.5% | 3.0% |
| 03 (Medium) | 53.0% | 3.0% |
| ARChitects (ARC Prize 2024) | 56.0% | 2.5% |
| 04-mini (Medium) | 41.8% | 2.4% |
| Icecuber (ARC Prize 2020) | 17.0% | 1.6% |
| ol-pro (Low) | 23.3% | 0.9% |
| Claude 3.7 (8K) | 21.2% | 0.9% |
Current top-performing models achieve scores generally below 5% on ARC-AGI-2, a level considered indicative of noise or incidental pattern fits rather than consistent abstract reasoning. This performance sharply contrasts with their ARC-AGI-1 results, highlighting ARC-AGI-2's success in posing a tougher, more generalizable challenge for frontier AI systems.
The Core Challenge: Deeper Compositional Reasoning
ARC-AGI-2 tasks are designed to probe deeper into compositional generalization, requiring AI to combine known rules and concepts in novel, multi-faceted ways. This goes beyond simple pattern recognition, demanding true understanding and flexible application of logic.
Multi-rule Compositional Reasoning
Unlike ARC-AGI-1, which often involved a single high-level transformation, ARC-AGI-2 tasks frequently demand the simultaneous application of multiple interacting rules. For instance, a task might require cropping an input grid to a framed area, then rescaling specific colored objects within that frame, and finally placing these rescaled objects into corresponding holes of the same shape, all in one coherent transformation. This requires an AI to understand and coordinate complex operations.
Multi-step Compositional Reasoning
Many ARC-AGI-2 tasks feature rules that depend sequentially on previous steps. An example might involve iteratively placing objects, where the correct position and orientation of the N-th object are determined by the placement of the N-1th object. Such tasks are virtually impossible to solve without executing each step in order, demanding a robust planning and execution capability.
Contextual Rule Application
ARC-AGI-2 introduces tasks where the core transformation rule's application is modulated by specific contextual elements within the grid. For example, an AI might need to isolate shapes and stack them, but the side to which they are stacked (left or right) depends on a contextual cue, such as the color of the shape's outline. This requires forming a control flow based on in-context information.
In-context Symbol Definition
Many tasks feature "symbols" whose meaning is defined purely within the task itself. For instance, colored rectangles with specific numbers of holes might encode the color to be used for other shapes with the same number of holes. This dynamic, on-the-fly symbolic assignment presents a major hurdle for current AI systems, which often lack the flexibility to infer and apply such ephemeral definitions.
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Comprehensive assessment of your current systems, identification of high-impact AI opportunities, and definition of measurable ROI metrics aligned with ARC-AGI-2 principles.
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Development and deployment of a proof-of-concept, rigorous testing with real-world data, and iterative refinement based on performance feedback and your operational insights.
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