Default-Preserving Clarification: Repair, Regression, and Interaction Cost

With the increasing deployment of language-model agents in complex task environments, proactive clarification has become a standard paradigm to resolve instruction ambiguities. However, an informative clarification can still make the resulting solution worse. When a language-model agent incorporates a reply by regenerating its output, it may repair a failed solution or mistakenly replace a correct one with an incorrect revision. We study this trade-off relative to an explicit default draft. A transition-based account expresses the value of asking as expected repair minus regression and interaction cost. It motivates Default-Preserving Clarification (DPC), a training-free method combining context- and cost-aware selection with a retain-or-revise instruction. In three paired runs on 100 ClarifyCodeBench variants, preservation reaches 49.0% pass@1 versus 47.3% for draft-aware rewriting, while reducing estimated incremental revision API cost by 26.9%. It repairs seven failed defaults and causes no observed regression among the 61 matched-answer pairs whose defaults already pass. With delivered questions and execution pathways held fixed, the cascade and a one-turn SAGE adaptation each solve 83 of 160 tasks, asking 29 and 34 questions, respectively. Ranking and cost studies on coding and translation panels characterize the selection-computation trade-off, with mixed transfer results. These findings support treating answer incorporation as part of the clarification policy, with benefits that depend on both the default and the executor.