While Large Language Models have achieved state-of-the-art performance in general machine translation, they frequently struggle to adhere to complex stylistic nuances and lexical constraints strictly and often revert to a neutral style. The scarcity of style-specific parallel data hinders existing solutions, whereas standard prompting lacks rigorous verification mechanisms. To bridge this gap, we propose Style-Ruler, an inference-only agentic framework that operationalizes style via unsupervised extraction and neuro-symbolic verification. Style-Ruler orchestrates three specialized agents: (1) a Style-Reverse Analyst (SRA) that mines quantifiable linguistic constraints from monolingual reference texts via unsupervised extraction; (2) a Constraint-Adaptive Translator (CAT) that employs a Style-Chain-of-Thought mechanism to plan stylistic adherence before generation explicitly; and (3) a Sandbox-Augmented Auditor (SAA) that provides heterogeneous feedback through deterministic code execution. These components form a closed-loop refinement mechanism where symbolic logic guides neural generation. Extensive experiments on the GuoFeng Webnovel dataset and WMT24++ benchmark demonstrate the superiority of our approach. Style-Ruler achieves a CSR of 82.4% on stylized translation, surpassing standard Chain-of-Thought prompting by over 50%, while improving BLEU scores by +1.0 on En→Zh.

Large Language Models (LLMs) remain unreliable for extremely low-resource languages whose writing systems conflict with high-resource pretraining priors. We study Bai-Chinese translation in the Bowen script, where Chinese characters are frequently borrowed phonetically rather than semantically, causing severe literal-translation hallucinations. We propose MAS-Bai, a linguistically informed adaptive multi-agent framework for Bowen-script Bai-Chinese translation. MAS-Bai routes each input through either a lightweight retrieval-translation path or a deeper philological path with etymological and syntactic agents, and uses cycle-consistency with a lightweight reverse translator as an inference-time reliability signal. We construct a 6,536-sentence Bai-Chinese corpus and evaluate on a 1,536-sentence test set. MAS-Bai achieves 16.23 BLEU and 62.15 COMET, improving over Standard RAG by 8.11 BLEU and 6.85 COMET. Forward LoRA, expert ablations and controlled routing experiments distinguish the roles of supervised adaptation, Bai-specific expertise and conditional routing. The largest gains occur on Bowen phonetic loans and divergent Bai word order, while human evaluation and reverse-verifier analyses support the automatic results.

Large Language Models (LLMs) have demonstrated remarkable capabilities in machine translation. However, maintaining discourse coherence and terminological consistency remains a persistent challenge in document-level translation (DocMT). Existing solutions, such as memory-based agents, predominantly rely on explicit context concatenation. This paradigm treats historical context as a static external resource, which often leads to context dilution, high inference latency, and superficial knowledge integration. To address these limitations, we propose AdaDPI, an adaptive agentic framework that shifts the DocMT paradigm from static retrieval to dynamic parametric internalization. Specifically, we design a linguistic uncertainty monitor (LUM) to actively detect critical discourse discontinuities by the model’s epistemic uncertainty. Upon detection, a context-to-parameter integrator (CPI) compiles retrieved external constraints directly into the model’s intrinsic state via an online parameter adaptation mechanism. Through the online parameter adaptation on a lightweight adapter, AdaDPI internalizes document-specific norms into the model’s intrinsic representations, enabling a progressive evolution of the translation strategy as the discourse unfolds. Extensive experiments on the discourse-rich GuoFeng and IWSLT2017 datasets demonstrate that AdaDPI significantly outperforms the SoTA baselines by more than 5 points on the consistency metric.