Pragmatic inference is inherently graded.Different lexical items give rise to pragmatic enrichment to different degrees.Scalar implicature exemplifies this property through scalar diversity, where implicature strength varies across scalar items.However, evaluations of pragmatic inference in large language models (LLMs) often rely on prompt-based manipulations.Beyond prompt-level effects, this study introduces Continuous Interpretive Steering (CIS), a method that probes graded pragmatic interpretation by treating activation-level steering strength as a continuous experimental variable.To support this analysis, this study introduces a new dataset, GraSD, which encodes graded scalar diversity.Experiments on four LLMs show that uniform activation steering increases pragmatic interpretations globally but collapses item-level variation, whereas graded activation steering yields differentiated interpretive shifts aligned with scalar diversity grades.It indicates that graded sensitivity is encoded in the representation space and can be systematically recovered through controlled intervention.Together, CIS and GraSD provide a principled framework for evaluating graded pragmatic sensitivity in LLMs.