“SenseNova-U1.5: Towards Native Unified Visual Intelligence” Most multimodal models still use separate representations for seeing and generating images. SenseNova-U1.5 instead uses one 8B encoder-free, VAE-free model to understand, reason about, generate, and edit images directly in pixel space, including native 4K generation. It then trains specialist RL experts for aesthetics, text, infographics, and editing, and distills them back into one unified model. The result is a stronger case that visual understanding and generation can share the same native representation rather than being separate systems.

Introducing SenseNova-Loop-9B-Preview: a 9B open-weight multimodal model with 256K context for document understanding, coding, and agent workflows. 📜 Apache-2.0 License. 🤖 • Leads six of eight tasks in the team’s reported comparison: Scores 71.38 on BIRD-Dev, 66.89 on WorkBuddy Bench, and 58.87 on AIDABench. • A deeper path without duplicate weights: Its 24-layer middle block runs twice with shared weights, producing 56 layer executions from 32 distinct decoder layers. • Trained for long, multimodal work: Training recipes combine text, images, code, and tool-use trajectories at sequence lengths up to 262K tokens. • Acceleration and serving paths are available: MTP is supported now, with documented Transformers, LightLLM, SGLang, and vLLM deployment paths. Note: This is a Preview release; the technical report is still forthcoming. Examples created with Raccoon Work 👇

🎨 A new unified visual AI where "seeing" and "creating" get trained to reinforce each other in one shared model. Title: SenseNova-U1.5: Towards Native Unified Visual Intelligence URL: 🧩 Overview OpenSenseNova's 8B Mixture-of-Transformers model drops external vision encoders and VAEs entirely, processing raw pixels directly while handling image understanding, reasoning, generation, and editing in one model. ⚙️ Problem it solves Most unified models route understanding and generation through separate latent spaces, causing consistency loss. This work aims to truly unify them on one shared representation. 🛠 Methodology Visual tokens are projected onto a 2D feature field and reconstructed with a spatial decoder that exchanges info across neighboring 32×32 regions. Four specialized experts (aesthetics, OCR, editing, infographics) are trained separately with RL, then merged into one model via On-Policy Distillation. 📊 Results GenEval overall hits 0.92, ImgEdit reaches 4.59 (up from 3.90 in U1), and BizGenEval jumps from 51.4% to 72.2% — outperforming its predecessor and competitors across understanding, generation, and editing alike. #UnifiedMultimodalAI# #ImageGeneration#