New AGI Test Proves Challenging, Stumps Majority of AI Models

# agi

The Arc Prize Foundation, co-founded by renowned AI researcher François Chollet, recently unveiled a new benchmark called ARC-AGI-2 in a blog post. This test aims to push the boundaries of AI's general intelligence, and so far, it's proving to be a tough nut to crack for most AI models.

According to the Arc Prize leaderboard, even advanced "reasoning" AI models like OpenAI's o1-pro and DeepSeek's R1 are only managing scores between 1% and 1.3%. Meanwhile, powerful non-reasoning models such as GPT-4.5, Claude 3.7 Sonnet, and Gemini 2.0 Flash are hovering around the 1% mark.

ARC-AGI tests challenge AI systems with puzzle-like problems, requiring them to identify visual patterns in grids of different-colored squares and generate the correct "answer" grid. These problems are designed to test an AI's ability to adapt to new, unseen challenges.

To establish a human baseline, the Arc Prize Foundation had over 400 people take the ARC-AGI-2 test. On average, these "panels" of humans achieved a 60% success rate, significantly outperforming the AI models.

François Chollet took to X to claim that ARC-AGI-2 is a more accurate measure of an AI model's true intelligence compared to its predecessor, ARC-AGI-1. The Arc Prize Foundation's tests are designed to assess whether an AI can efficiently learn new skills beyond its training data.

Chollet emphasized that ARC-AGI-2 prevents AI models from relying on "brute force" computing power to solve problems, a flaw he acknowledged in the first test. To address this, ARC-AGI-2 introduces an efficiency metric and requires models to interpret patterns on the fly rather than relying on memorization.

In a blog post, Arc Prize Foundation co-founder Greg Kamradt stressed that intelligence isn't just about solving problems or achieving high scores. "The efficiency with which those capabilities are acquired and deployed is a crucial, defining component," he wrote. "The core question being asked is not just, 'Can AI acquire [the] skill to solve a task?' but also, 'At what efficiency or cost?'"

ARC-AGI-1 remained unbeaten for about five years until December 2024, when OpenAI's advanced reasoning model, o3, surpassed all other AI models and matched human performance. However, o3's success on ARC-AGI-1 came at a significant cost. The version of OpenAI's o3 model, o3 (low), which scored an impressive 75.7% on ARC-AGI-1, only managed a paltry 4% on ARC-AGI-2, using $200 worth of computing power per task.

The introduction of ARC-AGI-2 comes at a time when many in the tech industry are calling for new, unsaturated benchmarks to measure AI progress. Thomas Wolf, co-founder of Hugging Face, recently told TechCrunch that the AI industry lacks sufficient tests to measure key traits of artificial general intelligence, such as creativity.

Alongside the new benchmark, the Arc Prize Foundation announced the Arc Prize 2025 contest, challenging developers to achieve 85% accuracy on the ARC-AGI-2 test while spending only $0.42 per task.

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