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ARC-AGI-3 Leaderboard Tracks AI Performance and Efficiency in Adaptive Environments
Key points
- ARC-AGI-3 evaluates AI adaptation in novel interactive environments.
- Leaderboard tracks performance against cost-per-task for efficiency.
- Entries include Reasoning Systems, Base LLMs, and Kaggle Systems.
- Systems must cost under $10,000 to run for inclusion.
Evolution of ARC-AGI Evaluation
The ARC-AGI leaderboard has advanced to its third iteration, ARC-AGI-3, which focuses on assessing AI agents' capacity for on-the-fly adaptation within new interactive environments. This marks a shift from earlier versions, ARC-AGI-1 and ARC-AGI-2, which primarily measured passive fluid intelligence.
Measuring Performance and Efficiency
A key aspect of the ARC-AGI-3 leaderboard is its visualization of the relationship between cost-per-task and performance. This metric emphasizes efficiency, recognizing that effective AI solutions not only solve problems but do so with minimal resource expenditure. Only systems with a run cost under $10,000 are included on the leaderboard.
Categorization of AI Solutions
The leaderboard categorizes AI solutions into three main types. "Reasoning Systems Trend Line" solutions show how performance changes with increased reasoning time. "Base LLMs" solutions, such as GPT-4.5 and Claude 3.7, represent raw model performance without extended reasoning. "Kaggle Systems" solutions are competition-grade entries from the ARC Prize, designed for efficiency under strict computational constraints, specifically a $50 compute budget for 120 evaluation tasks.
Verification and Data Interpretation
The leaderboard includes a verification policy for testing. For models that do not produce full test outputs, remaining tasks are marked as incorrect. Results labeled "preview" are unofficial and may be based on incomplete testing. Provisional cost estimates are sometimes used, such as those based on Gemini 3 Pro pricing, with retesting planned upon model release.
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Reporting from
The ARC-AGI-3 leaderboard has been updated to evaluate AI agents on their ability to adapt to novel interactive environments, moving beyond passive fluid intelligence. It measures both performance and cost-per-task, highlighting the efficiency of AI solutions. The leaderboard categorizes entries into Reasoning Systems, Base LLMs, and Kaggle Systems to show different approaches to AI problem-solving.