sensenova/SenseNova-Loop-9B-Preview · Hugging Face

Overview SenseNova-Loop-9B-Preview is a multimodal language model developed for long-context and agentic workflows. It accepts text and images and generates text for applications that combine document understanding, coding, and interaction with external tools. The model combines a looped decoder with two-stream manifold-constrained hyper-connections (mHC) . A contiguous block of decoder layers is executed twice with shared weights, while learned mappings manage information flow between two residual streams in the looped section. Together, these components provide a deeper execution path without duplicating the repeated block's parameters. Documented training recipes combine long sequences, multimodal inputs, and supervised task trajectories, with a maximum training sequence length of 262,144 tokens. For the best model performance, we recommend using our documented deployment environment and configuration parameters. 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: documented recipes include text, images, code, and tool trajectories at sequence lengths up to 256K tokens.