SenseTime open-sources SenseNova-Loop-9B-Preview, a 9B looped Transformer with about 14.6B inference parameters for long-context and agentic work
On Oct 30, SenseTime released SenseNova-Loop-9B-Preview (Apache 2.0) on its official X account and Hugging Face. It runs layers 4 to 27 of a 32-layer decoder twice with the same weights, so each forward pass executes 56 layer computations while storing about 9B parameters, about 14.6B at inference, and it uses two-stream mHC (manifold-constrained hyper-connections) to manage the residual streams. It takes text and images, with training sequences up to 262,144 tokens. SenseTime charts show it beating Qwen3.5 and Qwen3.6 peers on BIRD-Dev, WorkBuddy Bench, and other agent and data tasks, while it still trails on Terminal Bench 2.1; the technical report is not out yet. The official launch post drew about 910 likes.
Key Takeaways
- Structure: 32 distinct decoder layers, with layers 4 to 27 looped twice on shared weights for 56 layer computations per forward pass; about 9B stored and about 14.6B inference parameters (model card)
- Vendor-reported scores: BIRD-Dev 71.38 (Qwen3.5-27B 69.84), WorkBuddy Bench 66.89 (Qwen3.6-35B-A3B 61.98), AutomationBench 13.72 (Qwen3.5-27B 10.17)
- Weak spots from the same vendor chart: Terminal Bench 2.1 at 40.45, below Qwen3.6-27B at 47.19; tau3-Banking at 18.56, slightly below Qwen3.5-27B at 19.59
- Training context up to 262,144 tokens; hybrid attention uses three linear-attention layers plus one full-attention layer per four-layer group; MTP speedup is available, DSpark and DFlash2 are still training
- Apache 2.0, requires
trust_remote_code=True; official examples cover Transformers 5.18.0, a 4-GPU LightLLM setup, and an SGLang PR, and the technical report is still to come
Linked Models & Agents:
