Fine-tuned encoders deployed across heterogeneous NLP tasks face three compounding problems: mismatched inductive biases, class-imbalance corruption of feature statistics, and no mechanism to condition attention on external lexical knowledge. We introduce SURGELLM, a unified transformer framework that addresses each with a dedicated lightweight module: a surgical feature gate (learned per-dimension sigmoid over curated lexical indicators and [CLS]; provably degenerates to identity when features are uninformative), task-conditioned prefix tokens (quantized feature values and task identity prepended to every input), and Instance-Weighted Normalization (IWN; removes class-prior bias from gate statistics). We prove an excess-risk bound linking gate benefit to surgical feature alignment. Across four tasks, SST-2, multi-hop retrieval, LLM-prompt attribution, and authorship detection, covering 17,830 examples and eleven model variants over three seeds, the IWN variant achieves macro-F1 0.940 (+0.036 over the strongest non-IWN baseline; +0.130 on authorship detection). A random-vocabulary control (-0.028 avg. F1) confirms gains are lexical, not parametric. Code, vocabularies, and a 99.5%-recovery auto-extraction recipe are released.

Accuracy remains the dominant criterion for selecting NLP classifiers, yet it fails to characterize the operational reliability required for deployment under class imbalance, distribution shift, and demographic heterogeneity. We introduce TNMM++ (Trustworthy NLP Multi-axis Monitoring, extended), a theoretically grounded reliability audit that evaluates classifiers along seven complementary axes, utility, calibration, robustness, safety, fairness, selective prediction, and uncertainty quantification, and formalises model selection as constrained multi-objective optimization. Beyond metric aggregation, TNMM++ contributes six algorithmic results: a reliability tensor, a Pareto convex-hull theorem, variance-adaptive weighting, a boundary-predictor criterion with exclusion rule for majority-class collapse, a Bayesian risk decomposition, and the TAMS algorithm with optimality bound. Evaluating eight lightweight Transformer encoders (MiniLM, ELECTRA, mBERT, DistilBERT, MobileBERT, ALBERT, RoBERTa, XLM-R) and excluding models that failed to learn the task, we find that properly trained mid-tier models exhibit balanced utility and calibration superior to collapsed high-accuracy counterparts. TrustScore identifies mBERT (0.6685), ELECTRA (0.6630), and DistilBERT (0.6627) as deployment-reliable. Sensitivity analysis under 1,000 Dirichlet weight perturbations confirms ranking stability in 92.4% of configurations, and multi-seed evaluation (5 seeds) demonstrates conclusions are not seed artifacts. Our findings establish that single-metric selection is statistically unreliable and motivate multi-axis reliability auditing as the default protocol for trustworthy NLP deployment.