SYSE.45:5.2 - Construct a corrected continuation from a recorded history

A recorded history H contains the permitted outcome-lookup operation, the original attempt A17, an acknowledgement followed by a lost reply, and the at-most-one-effect requirement. The recorded next action was an unsafe repeat of the mutation. An experience-informed teacher, using that failure and other qualified episodes, proposes lookup_outcome(attempt=A17).

The engineer checks that H itself supplies the attempt identity, supported lookup and unresolved effect that warrant this correction. The student receives H without the teacher’s extra experience; its action target is the lookup, not the old repeated mutation or the following observation. The old next observation remains evidence about the old action. To establish what the correction does, exercise that lookup in a new qualified test occurrence or use applicable environment evidence; relabelling the old observation would fabricate its consequence.

The training example fits only the corrected agent action. If H had lost A17 or the lookup contract, restore that input or target obtaining the missing contribution instead. A final success reward alone would not identify whether safe recovery or a lucky duplicate caused the result; the supplied intermediate attempt/effect state localizes the unsafe replay decision. A contrasting acknowledgement-without-effect case tests the feedback rule.

For a preference branch, the service owner may compare two factually supported reports and prefer one that exposes unresolved status before optional explanation. Record that owner, task and report pair with the judgement. Both reports must still meet the effect and factual requirements before the pair can support that preference target.

Untouched trials compare the trained policy with the baseline on new attempt identities, known and unknown outcomes, a missing lookup operation and earlier useful no-replay behavior. An episode-local update declares when the adapter resets; a durable update tests that older behavior after the new target is learned. The result is a candidate and bounded evidence, not an inferred successful deployment.