DANTINOX：多范式语言建模的统一框架
DANTINOX: A Unified Framework for Multi-Paradigm Language Modeling
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中文总结 AI 辅助
DantinoX是一个开源JAX/Flax库，用单一模块化Transformer主干统一支持自回归、离散掩码扩散和连续流匹配三种语言生成范式，仅需配置更改即可切换，实现公平的跨范式比较。
中文摘要 AI 辅助
语言生成研究日益涵盖三种范式：自回归解码、离散掩码扩散和连续流匹配。由于每种范式都存在于独立的代码库中，比较它们十分困难，因此测得的差异往往反映的是实现细节而非范式本身。我们提出了DantinoX，一个开源的JAX/Flax库，其中单个模块化Transformer主干支持所有三种范式。切换生成范式、注意力机制或硬件拓扑仅需更改配置，而主干架构、分词器、初始化策略和训练基础设施保持一致。这使得在同一个API内，能够对训练、流式推理和基准测试进行受控的跨范式比较。
英文摘要
Language generation research increasingly spans three paradigms: autoregressive decoding, discrete masked diffusion, and continuous flow-matching. Comparing them is difficult because each lives in a separate codebase, so measured differences often reflect implementation details rather than the paradigms themselves. We present DantinoX, an open-source JAX/Flax library in which a single modular Transformer backbone serves all three paradigms. Switching the generation paradigm, attention mechanism, or hardware topology requires only a configuration change, while the backbone architecture, tokenizer, initialization strategy, and training infrastructure remain consistent. This enables controlled cross-paradigm comparisons within one API for training, streaming inference, and benchmarking.
