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

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.