An agent says it is confident in its next action. That might mean the action looks locally sensible, not that the current plan is likely to finish successfully.
An October 13 paper trains per-step confidence against eventual episode outcomes. Its protocol separates depth, task difficulty and attempt-level signals. The authors find improvements, while noting that recognising difficult tasks is easier than distinguishing successful and failed attempts on the same task. Read the research paper.
Confidence needs the right target
If a confidence score merely ranks easy tasks above hard ones, it can improve aggregate risk selection without warning about a particular mistake. Operators need to know which ability they are buying.
Outcome labels also depend on what happens after the reported step. A locally correct action may receive a failure label because the agent errs much later.
Evaluate matched attempts on the same task and inspect confidence before recoverable and terminal errors. Compare selective execution with the cost of human handoffs.
The findings support useful calibration in tested environments, not reliable introspection in general. Confidence becomes actionable when its meaning is explicit: probability of local correctness, eventual completion and recognition of an impending failure are different quantities.
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