As large language models (LLMs) are increasingly deployed in financial services, a single non-compliant interaction can expose institutions to regulatory penalties and direct consumer harm. Existing guard models are built around general harm taxonomies and overlook violations grounded in specific financial regulations. We address this gap with a regulation-driven pipeline that operates directly on regulatory documents, inducing a financial compliance risk taxonomy and synthesizing grounded training data without any predefined violation categories. Instantiating the pipeline on Chinese financial regulations, we release FinGuard-Bench, to our knowledge the first benchmark for financial regulatory compliance detection, with expert-annotated labels at both the query and response levels. We further train FinGuard, a financial compliance detection model built on Qwen3-8B and trained on the regulation-grounded data via supervised fine-tuning and self-play reinforcement learning. On FinGuard-Bench, FinGuard substantially outperforms all baselines, including dedicated guard models and much larger general-purpose LLMs such as Qwen3.5-397B-A17B and GPT-5.1. Furthermore, FinGuard also preserves general safety capabilities and adapts to unseen institution-specific policies using policy documents alone. We will publicly release the code, prompts, and resources used in this work on GitHub.

“端到端语音翻译具有跨模态和跨语言的特性,如何有效地利用这些特性是一个具有挑战性的问题。本文基于多任务学习框架,提出两种新颖辅助任务。语音增强的文本翻译任务通过在文本翻译任务中融入语音模态信息来缓解语音和文本的模态差异,最终提升语音翻译任务的性能。全局感知条件掩码语言建模任务能够同时建模转录文本和译文进而利用文本的全局上下文信息指导翻译模型的训练。在MuST-C数据集8个语向的实验结果表明,本文的方法显著优于基线系统,并且达到了与其它端到端语音翻译系统可竞争的性能水平。进一步的分析实验表明,本文的方法能够缓解语音和文本之间的模态差异并且在不损害文本翻译任务性能的情况下提升语音翻译任务的性能。”