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We introduce \\textbf{\\surgellm}, a unified transformer framework that addresses each with a dedicated lightweight module: a \\emph{surgical feature gate} (learned per-dimension sigmoid over curated lexical indicators and \\texttt{[CLS]}; provably degenerates to identity when features are uninformative), \\emph{task-conditioned prefix tokens} (quantized feature values and task identity prepended to every input), and \\emph{Instance-Weighted Normalization} (IWN; removes class-prior bias from gate statistics). We prove an excess-risk bound linking gate benefit to \\emph{surgical feature alignment}. Across four tasks, SST-2, multi-hop retrieval, LLM-prompt attribution, and authorship detection, covering 17","title":"SURGELLM: Rethinking Multi-Task Evaluation through Task-Aware Feature Gating with Class-Balanced Normalization","url":"https://arxiv.org/abs/2606.24259","vendor":"arxiv_cs_ai"},"summary":"arXiv:2606.24259v1 Announce Type: cross \nAbstract: 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 \\textbf{\\surgellm}, a unified transformer framework that addresses each with a dedicated lightweight module: a \\emph{surgical feature gate} (learned per-dimension sigmoid over curated lexical indicators and \\texttt{[CLS]}; provably degenerates to identity when features are uninformative), \\emph{task-conditioned prefix tokens} (quantized feature values and task identity prepended to every input), and \\emph{Instance-Weighted Normalization} (IWN; removes class-prior bias from gate statistics). We prove an excess-risk bound linking gate benefit to \\emph{surgical feature alignment}. 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