MOSS-VL技术报告

摘要

我们提出MOSS-VL，一个开放视觉-语言模型系列，将实时交互——边说边感知——视为一项首要能力。该模型在整个技术栈上协同设计：语言解码器仅通过门控交叉注意力访问视觉信息，使得模型在生成时能自然感知新到的帧；一个合成的交互语料库监督何时发言、何时保持沉默、以及何时修正；一个分阶段的课程将全部实时特定训练集中在一个轻量级的最终阶段，并建立在强大的离线基础之上。在离线条件下，MOSS-VL-Instruct在可比规模上具有竞争力，并在时间推理视频集上领先。在四个流式基准测试中，MOSS-VL-Realtime在开源流式模型中于三个基准上取得最佳平均成绩（在第四个上位列第二），并全面覆盖直接测试主动行为的三个子集——在OmniMMI主动警报上达到66.0，而最佳基线为37.5。MOSS-VL拥有11.3B参数，但视觉token位于解码序列之外，随着视觉上下文的增长，其相对于同骨干Qwen3-VL-8B的首token时间优势从2.8倍扩大到5.1倍。我们已在https://github.com/OpenMOSS/MOSS-VL上发布全部五个检查点、训练课程以及实时推理代码。

English

We present MOSS-VL, an open vision-language model family that treats real-time interaction -- perceiving while it speaks -- as a first-class capability. It is co-designed across the stack: the language decoder attends to vision only through gated cross-attention, so the model can naturally see incoming frames while generating; a synthesized interaction corpus supervises when to speak, when to stay silent, and when to revise; and a staged curriculum concentrates all real-time-specific training in one light final stage over a strong offline foundation. Offline, MOSS-VL-Instruct is competitive at comparable scale and leads temporal-reasoning video sets. Across four streaming benchmarks, MOSS-VL-Realtime posts the best average on three (second on the fourth) among open-source streaming models, sweeping the three subsets that squarely test proactive behavior -- 66.0 vs. 37.5 for the best baseline on OmniMMI Proactive Alerting. With 11.3B parameters but visual tokens outside the decoded sequence, MOSS-VL widens its time-to-first-token advantage over same-backbone Qwen3-VL-8B from 2.8x to 5.1x as visual context grows. We release all five checkpoints, the training curriculum, and the real-time inference code at https://github.com/OpenMOSS/MOSS-VL.