AI can adapt and generalize beyond the exact examples it has seen

Can it transfer what it knows across domains, tasks, and languages?

Transfers knowledge across domains

Global-MMLU-Lite provides cross-language and cross-domain evidence that capabilities transfer beyond one narrow task family. It is a meaningful but still bounded indicator of broader generalization.

Can it handle problems meaningfully unlike those it has seen before?

Handles unfamiliar problem types

ARC-AGI-2 is designed to test generalization to novel problem structures rather than memorized patterns. That makes it a strong benchmark proxy for out-of-distribution generalization, even if it captures only one slice of the capability.

Can it use long context without losing important details?

Uses long context reliably

Artificial Analysis Long Context Reasoning directly evaluates reasoning over long contexts. This supports meaningful progress here, while still reflecting a benchmarked rather than fully real-world long-memory setting.