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Unlike conventional DRO, which relies on dense Shannon-entropy exponentiated-gradient updates, c","title":"STaR-DRO: Stateful Tsallis Reweighting for Group-Robust Structured Prediction","url":"https://arxiv.org/abs/2604.09737","vendor":"arxiv_cs_ai"},"summary":"arXiv:2604.09737v2 Announce Type: replace-cross \nAbstract: Structured prediction with large language models requires outputs that are label-accurate, ontology-constrained, structurally valid, and evidence-grounded under label imbalance and heterogeneous group difficulty. We present a unified framework for ontology-constrained generation. First, we introduce a modular prompt-engineering architecture combining XML-style structure, expert disambiguation rules, chain-of-thought reasoning, metadata-aware decision logic, schema contracts, and a self-validation gate. It targets recurrent in-context failures, including format drift, label ambiguity, evidence hallucination, and metadata-conditioned confusion. 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