The prevalence of rapidly evolving slang, neologisms, and highly stylized expressions in informal user-generated text, particularly on Chinese social media, poses significant challenges for Machine Translation (MT) benchmarking. Specifically, we identify two primary obstacles: (1) data scarcity, as high-quality parallel data requires bilingual annotators familiar with platform-specific slang, and stylistic cues; and (2) metric limitations, where traditional evaluators like COMET often fail to capture stylistic fidelity and non-standard expressions. To bridge these gaps, we first introduce CSM-MTBench, an expert-curated benchmark covering five Chinese→foreign language directions and comprising 10,915 total instances, organized into two subsets: **Fun Posts**, featuring context-rich content with frequent slang and neologisms, and **Social Snippets**, focusing on concise, emotion- and style-driven expressions. Next, we propose tailored evaluation approaches for each subset, including slang translation success rate and style-preservation metrics combining embeddings and LLM-asa-judge evaluation. Experiments on over 20 models reveal substantial variation in how current MT systems handle semantic fidelity, slang translation, and stylistic nuances. We further look into model behaviors through prompt sensitivity analysis and qualitative case studies, highlighting the challenges of translating these informal expressions. CSM-MTBench thus serves as a rigorous testbed for advancing MT systems capable of mastering real-world Chinese social media texts.

As a primary medium for modern information dissemination, social networking services (SNS) have experienced rapid growth, which has proposed significant challenges for platform content management and interaction quality improvement. Recently, the development of large language models (LLMs) has offered potential solutions but existing studies focus on isolated tasks, which not only encounter diminishing benefit from the data scaling within individual scenarios but also fail to flexibly adapt to diverse real-world context. To address these challenges, we introduce RedOne, a domain-specific LLM designed to break the performance bottleneck of single-task baselines and establish a comprehensive foundation for the SNS. RedOne was developed through a three-stage training strategy consisting of continue pretraining, supervised fine-tuning, and preference optimization, using a large-scale real-world dataset. Through extensive experiments, RedOne maintains strong general capabilities, and achieves an average improvement up to 14.02% across 8 major SNS tasks and 7.56% in SNS bilingual evaluation benchmark, compared with base models. Furthermore, through online testing, RedOne reduced the exposure rate in harmful content detection by 11.23% and improved the click page rate in post-view search by 14.95% compared with single-tasks baseline models. These results establish RedOne as a robust domain-specific LLM for SNS, demonstrating excellent generalization across various tasks and promising applicability in real-world scenarios.