Reactance-Aware Recursive Alignment for Human–AI Translation: Modeling Translator Agency and Task-Specific Critical Automation Capability

Abstract

Professional translators may resist highly directive AI assistance when it threatens perceived autonomy, authorship, and control. This study introduces the Recursive Control and Alignment Protocol (ReCAP), a reactance-aware framework for interactive machine translation. ReCAP combines a reactance-potential model with a recursive loop of Intent Inference, Reactance-Guided Generation, and Critical Feedback Integration. Its Adversarial Reactance Minimization objective balances translation quality against estimated autonomy costs, and the task-specific Critical Automation Capability Index (CACI) provides an exploratory behavioral summary of critical engagement. In an evaluation with 156 professional translators across four English-based bidirectional language-pair settings, ReCAP achieved a mean COMET × 100 score of 36.8, 2.8 points above QE-guided MT (34.0), the strongest baseline, across five seed-matched runs per method–language-pair configuration. Mixed-effects estimates showed lower reported reactance for ReCAP than QE-guided MT (β = −0.55, 95% CI [−0.71, −0.39]) and higher exploratory CACI scores than CHORUS (β = 0.64, 95% CI [0.44, 0.84]). Post hoc CACI checks remained task-specific and exploratory. These findings support a run-level translation-quality advantage and task-specific human-outcome associations under the evaluated conditions.