The open-source ecosystem has accelerated the democratization of Large Language Models (LLMs) through the public distribution of specialized Low-Rank Adaptation (LoRA) modules. However, integrating these third-party adapters often induces catastrophic forgetting of the base model's foundational safety alignment. Restoring these guardrails via fine-tuning on safety data introduces an opposing failure mode: the severe degradation of the specialized domain knowledge the adapter was originally designed to provide. To overcome this zero-resource challenge, we propose Neural Weight Translation (NeWTral), a framework that directly maps unsafe, domain-specific adapters onto a safe alignment manifold while rigorously preserving their core expertise. NeWTral operates as a non-linear translation module pre-trained on a diverse corpus of unsafe-to-safe adapter pairs. By executing this mapping entirely within the parameter space, NeWTral utilizes an adaptive Mixture of Experts (MoE) routing strategy to autonomously blend high-fidelity surgical translators and aggressive alignment experts. We evaluate our framework across four architectural families (Llama, Mistral, Qwen, and Gemma) at scales up to 72B parameters across eight diverse scientific and professional domains. Our results demonstrate that the MoE variant achieves a radical reduction in the average Attack Success Rate (ASR), dropping from 70% in unsafe experts to just 13%, while maintaining an exceptional 90\% average knowledge fidelity. Much like the crowdsourced adapters it remedies, the NeWTral module is designed as a standalone, downloadable asset that allows practitioners to restore safety alignment instantly without requiring access to original training data or hardware-intensive retraining.

翻译：开源生态系统通过公开分发专门的低秩适配模块，加速了大语言模型的民主化进程。然而，整合这些第三方适配器常常导致基础模型基础安全对齐的灾难性遗忘。通过安全数据微调恢复这些防护机制会引发另一种失败模式：适配器原本旨在提供的专业领域知识严重退化。为克服这一零资源挑战，我们提出神经权重转换框架NeWTral，该框架直接将不安全的领域特异性适配器映射到安全对齐流形上，同时严格保留其核心专长。NeWTral作为非线性转换模块运行，在多样化的不安全到安全适配器对语料库上进行预训练。通过完全在参数空间内执行该映射，NeWTral采用自适应混合专家路由策略，自主融合高保真外科翻译器与激进对齐专家。我们在Llama、Mistral、Qwen和Gemma四个架构家族（参数规模高达72B）上，跨八个不同科学与专业领域评估了该框架。结果表明，混合专家变体将平均攻击成功率从不安全专家的70%急剧降低至仅13%，同时保持了卓越的90%平均知识保真度。与它要补救的众包适配器类似，NeWTral模块设计为独立的可下载资产，使从业者能够即时恢复安全对齐，无需访问原始训练数据或进行硬件密集型的重新训练。