Which Revisions Are Worth Applying? Risk-Aware Repair Decisions for Translation Self-Correction

Large language models can refine machine translations, but a proposed revision may also damage content that is already correct. This matters more as initial translations improve, i.e., stronger translations contain fewer beneficial revisions and offer smaller attainable gains, motivating self-correction as a selective decision over whether and how to revise. We formulate translation repair as maximizing expected bounded improvement subject to a population degradation budget; the Lagrangian yields a net-value rule that compares each repair with retaining the current translation. We instantiate this rule in Reliable Correction via Action Selection (RCAS), which generates candidates along multiple repair directions, estimates their benefit and degradation relative to the current translation without test-time references, and applies the highest positive-net-value repair or retains the current translation. Across five WMT test sets, RCAS achieves the highest mean COMET among the evaluated repair pipelines on four datasets. Frozen-pool controls show that overriding its retain decisions substantially increases population regression, and a blinded audit of 250 executed edits finds that 23.2% repair a major error while only 2.0% introduce one. These results show that reliable repair depends as much on declining unjustified revisions as on generating good ones.