Text detoxification is increasingly used in content moderation and dialogue systems, where systems must produce safe rewrites while revealing what was changed and why. Existing approaches typically trade off interpretability and end-to-end optimization. Monolithic rewriting methods treat detoxification as a single opaque generation step, whereas edit-based pipelines make edits explicit but are trained in separate stages, preventing joint optimization. We propose STEER, a framework that models detoxification as a structured edit chain formulation and optimizes the entire editing process end-to-end with Group Relative Policy Optimization (GRPO). To make this formulation trainable, we construct synthetic edit-chain supervision using a multi-agent pipeline, perform supervised fine-tuning (SFT) to teach the model the structured editing format, and then apply GRPO with a veto-aligned multiplicative reward and granular format scoring. Experiments on ParaDetox and two out-of-domain benchmarks show that STEER achieves the highest aggregate J score among the compared systems on ParaDetox and remains competitive under out-of-domain evaluation.

Text detoxification should produce safer rewrites while retaining identity mentions from the original text. However, classifier-guided methods can remove identity descriptors alongside genuinely harmful content when toxicity classifiers associate identity mentions with harmfulness and guidance relies on absolute scores. We propose CIDER (Contrastive Identity-preserving Detoxification via Explicit Reference), a default-relative, margin-gated selective-guidance method for masked diffusion language models. At each selected masked position, CIDER evaluates every candidate in the admissible top-k set relative to the model-default token in the same partially decoded state and commits a non-default candidate only when the best candidate lowers the one-step proxy score by more than a preset margin. On the primary LLaDA-8B CivilComments×Jigsaw setting, CIDER improves retention by +0.06 to +0.16 over the interpolated absolute-score frontier at matched residual-toxicity levels. At the reported aggressive endpoints, the retention advantage recurs across four data pairings on LLaDA-8B and three bases on CivilComments×Jigsaw. Controlled ablations indicate that the selection rule is an important contributor to the improvement.

Despite advances in legal NLP, no comprehensive evaluation of Transformer-based models customized for legal tasks (referred to as ’legal-specific’ models in this paper) exists for contract classification tasks. To address this gap, we present an evaluation of 13 legal-specific transformer-based models on 3 English-language contract classification tasks and compare them with 9 generalist models. The results show that legal-specific models consistently outperform generalist models, especially on tasks requiring nuanced legal understanding. They also help reduce misclassification of rare classes in imbalanced datasets. Legal-BERT and Contracts-BERT establish new SOTAs on two of the three tasks, despite having 69% fewer parameters than the best-performing generalist models. We also identify CaseLaw-BERT and LexLM as strong additional baselines for contract classification. Our results highlight the shortcomings of generalist models, emphasizing the need for domain-specific customization, particularly in the context of legal applications.