Source URL: https://simonwillison.net/2025/Mar/25/greg-kamradt/
Source: Simon Willison’s Weblog
Title: Quoting Greg Kamradt
Feedly Summary: Today we’re excited to launch ARC-AGI-2 to challenge the new frontier. ARC-AGI-2 is even harder for AI (in particular, AI reasoning systems), while maintaining the same relative ease for humans. Pure LLMs score 0% on ARC-AGI-2, and public AI reasoning systems achieve only single-digit percentage scores. In contrast, every task in ARC-AGI-2 has been solved by at least 2 humans in under 2 attempts. […]
All other AI benchmarks focus on superhuman capabilities or specialized knowledge by testing “PhD++" skills. ARC-AGI is the only benchmark that takes the opposite design choice – by focusing on tasks that are relatively easy for humans, yet hard, or impossible, for AI, we shine a spotlight on capability gaps that do not spontaneously emerge from "scaling up".
— Greg Kamradt, ARC-AGI-2
Tags: evals, ai
AI Summary and Description: Yes
Summary: The launch of ARC-AGI-2 introduces a new benchmark specifically designed to highlight the limitations of AI reasoning systems in performing tasks that are relatively easy for humans. Unlike other AI benchmarks that emphasize superhuman skills, ARC-AGI-2 focuses on identifying capability gaps within AI, providing valuable insights for professionals engaged in AI security and performance evaluation.
Detailed Description: ARC-AGI-2 represents a significant advancement in the field of AI benchmarking by focusing on tasks that challenge AI reasoning systems while remaining accessible to human counterparts. The unique design of this benchmark aims to expose areas where AI still falls short compared to human thinking and reasoning abilities.
– Key highlights include:
– **Benchmarking Focus**: Most existing benchmarks assess AI based on their ability to perform at superhuman levels or require specialized knowledge. In contrast, ARC-AGI-2 aims to evaluate how AI systems cope with simpler human tasks that prove difficult for they are not designed to replicate human thought processes.
– **Task Difficulty**: Pure language models (LLMs) score only 0% on ARC-AGI-2, indicating significant gaps in their capabilities for reasoning tasks that humans can solve easily.
– **Human Performance**: The benchmark has shown that every task was successfully completed by at least two humans within an average of two attempts, underscoring the fundamental differences between human and AI capabilities.
– **Insight for AI Development**: By spotlighting the limitations of AI, ARC-AGI-2 provides developers and security professionals with insights to improve AI systems, fostering the development of more robust and secure AI applications that address real-world challenges.
Overall, ARC-AGI-2 not only serves as a tool for evaluating AI systems but also highlights critical areas for improvement in AI security, which is essential for professionals engaged in developing and deploying AI technologies responsibly. This focus on capability gaps encourages a deeper investigation into AI’s limitations, reinforcing the need for compliance and governance around AI applications.