OpenSenseNova (SenseNova) · Looped multimodal LLM

SenseNova-Loop-9B-Preview

SenseNova-Loop-9B-Preview is a multimodal language model from OpenSenseNova, built for long-context and agentic workflows. It takes text and images as input and produces text. It uses a looped decoder with two-stream manifold-constrained hyper-connections (mHC): layers 4-27 are run twice with shared weights, giving 56 decoder-layer executions from 32 distinct layers. The model has about 9B total parameters and about 14.6B inference parameters. Attention is hybrid, with three linear-attention layers followed by one full-attention layer, and the training context reaches 262,144 tokens. The README includes a benchmark chart covering BIRD-Dev, Terminal Bench 2.1, AutomationBench and others, but the page gives no figures. MTP is available for faster inference, while Dspark and Dflash2 are still in training. It is released under Apache-2.0 on Hugging Face and GitHub, announced on 30 October 2026, and runs with Transformers 5.18.0 and custom remote code.