AI can understand what a task is really asking

Can it identify missing constraints and ask clarifying questions?

Checks whether the system notices when a request is underspecified and asks the right follow-up before executing.

Ambiguity handling is now solid in many assistant workflows when the model is allowed to pause and clarify before acting.

Can it separate relevant from irrelevant information?

Measures whether the system can ignore distracting context and extract the task-critical variables.

Relevance filtering is competitive on many structured and semi-structured tasks, though high-noise adversarial settings still reduce accuracy.

Can it map ambiguous requests to the right task structure?

Tests whether the system chooses the correct frame for planning, retrieval, analysis, generation, or execution.

Models often choose the right frame, but still misclassify edge cases where requests could plausibly be handled in more than one way.

Can it keep the same interpretation across paraphrases?

Looks for stable task understanding when the same request is reworded in different ways.

Paraphrase stability has improved materially on broad assistant tasks, especially where the intent is ordinary and the context is clean.