MOSS-VL Technical Report
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...
| Authors | Pengyu Wang, Chenkun Tan, Shaojun Zhou et al. |
|---|---|
| Primary category | cs.CV |
| Published | 15 August 2026 |
| Last updated at source | 15 August 2026 |
Connected in the knowledge graph
- OpenMOSS-Team/MOSS-VL-Realtime-SGLANGModel · cited by
- OpenMOSS-Team/MOSS-VL-Realtime-NF4Model · cited by
- OpenMOSS-Team/MOSS-VL-Instruct-0708Model · cited by
- OpenMOSS-Team/MOSS-VL-Instruct-0408Model · cited by
- OpenMOSS-Team/MOSS-VL-Instruct-0708-NF4Model · cited by
- OpenMOSS-Team/MOSS-VL-Instruct-0708-FP8Model · cited by
- OpenMOSS-Team/MOSS-VL-RealtimeModel · cited by
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