AI knows when it may be wrong and can recover

Can it abstain when evidence is insufficient?

Tests whether the system declines or narrows an answer when the available evidence does not justify a confident response.

Abstention behavior exists, but it is still not reliably triggered in all of the cases where it should be.

Can it detect internal contradictions?

Measures whether the system notices when its own intermediate claims or outputs conflict with one another.

Self-diagnosis works in many simpler cases, but hidden contradictions inside longer outputs still slip through too often.

Can it self-correct after feedback or tool results?

Tests whether new evidence, critique, or tool output leads to an improved answer rather than defensive persistence.

Explicit critique and tool feedback often lead to materially better second-pass answers in a wide range of workflows.

Can it escalate uncertainty instead of bluffing in high-risk cases?

Measures whether the system hands off or narrows scope when risk is high and confidence is weak.

Escalation behavior is present in some high-stakes copilots, but trigger quality and consistency remain uneven.