When Evidence Fails to Persuade: Tracing Internal Resistance to Evidence-Driven Preference Revision

Evidence resistance, the tendency of language models to resist external correction despite explicit supporting evidence, poses a fundamental challenge to external knowledge augmentation and evidence-dependent reasoning. Despite its practical importance, the mechanisms underlying this behavior remain poorly understood, with limited conceptual separation between the model’s resistant behavior and the contextual–parametric conflict underlying its emergence. To address this gap, we formulate evidence resistance as a quantitative attribution problem over the internal dynamics of evidence-driven preference revision, isolating component-level responses that systematically counteract adaptation toward externally supported alternatives. Building on this formulation, we develop Contextual Shift Attribution, a signed attribution framework that decomposes evidence-induced answer shifts into directional component contributions and quantifies their relative influence on the model’s response to external evidence. By tracing component responses along a controlled evidence-strength trajectory, our framework distinguishes mechanistically relevant resistance from incidental activation changes across progressively reinforced evidence conditions. Across six language models and three knowledge-conflict benchmarks, targeted intervention on the identified components improves the average evidence-following rate by at least 9.5% over existing baselines while largely preserving evidence-free model behavior. Our analysis reveals that evidence resistance reflects a stable pattern of internal computation sustained by shared structure across heterogeneous evidence settings, rather than an instance-specific response to knowledge conflict. These findings move the study of evidence integration beyond output-level conflict toward the internal computations that actively resist preference revision, providing a foundation for more precise diagnosis and control of evidence-integration failures.