SenseNova-Loop-9B-Preview Multimodal Model Released
SenseNova-Loop-9B-Preview is a multimodal language model designed for long-context and agentic workflows, featuring a shared looped decoder and two-stream manifold-constrained hyper-connections (mHC).
SenseNova-Loop-9B-Preview is a multimodal language model developed for long-context and agentic workflows, accepting text and images while supporting up to 256K token context lengths.
Model Highlights:
- Shared looped block: 24 decoder layers are applied twice in sequence, producing 56 decoder-layer executions from 32 distinct layers.
- Two-stream mHC: Learned mappings route and combine information across two residual streams in the looped section.
- Hybrid attention: The decoder interleaves linear and full attention in a repeating four-layer pattern.
- Long-context multimodal training: Recipes include text, images, code, and tool trajectories at sequence lengths up to 256K tokens.