SURGELLM introduces a unified transformer framework with surgical feature gating, task-conditioned prefix tokens, and Instance-Weighted Normalization to address inductive bias mismatches, class imbalance, and lack of lexical knowledge integration. The IWN variant achieves macro-F1 of 0.940 across four tasks, outperforming baselines by 0.036 overall and 0.130 on authorship detection, with gains confirmed as lexical rather than parametric.