High-quality benchmarks are being designed to assess cognitive abilities of frontier models, focusing on reasoning, acting, and judgment rather than just recall. These benchmarks aim to measure progress toward artificial general intelligence (AGI).

Automated scanning reveals that top AI models frequently achieve high benchmark scores that do not accurately reflect their capabilities. The reliance on these benchmarks has led to a misrepresentation of model performance in the AI industry.

ARC-AGI-3 is the first interactive reasoning benchmark designed to evaluate human-like intelligence in AI agents. It requires agents to explore novel environments, acquire goals dynamically, build adaptable world models, and learn continuously, with a perfect score indicating performance that matches or exceeds human efficiency in every game.

SkillsBench is a benchmarking framework designed to evaluate the effectiveness of agent skills across 86 tasks in 11 domains. It includes curated skills and deterministic verifiers to assess their impact on large language model (LLM) agents during inference.

A new benchmark evaluates outcome-driven constraint violations in autonomous AI agents to enhance safety and alignment with human values. This benchmark addresses limitations of existing safety assessments that mainly focus on harmful actions.