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Running on Zero
| title: MJev-Spatial | |
| emoji: 🧭 | |
| colorFrom: yellow | |
| colorTo: blue | |
| sdk: gradio | |
| sdk_version: 6.30.0 | |
| app_file: app.py | |
| short_description: Spatial VLM that decides in one forward pass | |
| python_version: "3.12" | |
| startup_duration_timeout: 30m | |
| # MJev-Spatial | |
| Demo of **MJev-Spatial: System One Spatial Intelligence via Native Attention Matching** | |
| ([arXiv 2610.22276](https://arxiv.org/abs/2610.22276) · [GitHub](https://github.com/Fr0zenCrane/MJev-Spatial)). | |
| MJev-Spatial is a 4B multimodal decision model built on Qwen3.5-4B. It turns a spatial | |
| question into a finite candidate-selection problem and scores those candidates in a | |
| **single forward pass** with no generated answer tokens, reusing the language backbone's | |
| final full-attention layer's native query/key projections as the readout. | |
| The demo exposes the three answer spaces from the paper: | |
| - **Choice** — pick one of the supplied options (e.g. relative-direction questions). | |
| - **Scalar** — estimate a nonnegative measurement in meters via an interval hierarchy. | |
| - **Pointing** — point at a requested target in the image; the predicted point is | |
| drawn on the image (the model makes three 3×3 region decisions, refining a crop). | |
| Example image: `examples/images/scene.png` from the official | |
| [MJev-Spatial repository](https://github.com/Fr0zenCrane/MJev-Spatial) (Apache-2.0). |