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SenseNova-SI-1.3-Qwen3-VL-8B

Name: SenseNova-SI-1.3-Qwen3-VL-8B
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Qwen/Qwen3-VL-8B-Instruct

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apache-2.0

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SenseNova-SI: Scaling Spatial Intelligence with Multimodal Foundation Models

Code

arXiv

Code

Overview

Despite remarkable progress, multimodal foundation models still exhibit surprising deficiencies in spatial intelligence. In this work, we explore scaling up multimodal foundation models to cultivate spatial intelligence within the SenseNova-SI family, built upon established multimodal foundations including visual understanding models (i.e., Qwen3-VL and InternVL3) and unified understanding and generation models (i.e., Bagel). We take a principled approach to constructing high-performing and robust spatial intelligence by systematically curating SenseNova-SI-8M: eight million diverse data samples under a rigorous taxonomy of spatial capabilities. SenseNova-SI demonstrates unprecedented performance across a broad range of spatial intelligence benchmarks, while maintaining strong general multimodal understanding. More importantly, we analyze the impact of data scaling, discuss early signs of emergent generalization capabilities enabled by diverse data training, analyze the risk of overfitting and language shortcuts, present a preliminary study on spatial chain-of-thought reasoning, and validate the potential downstream application. SenseNova-SI is an ongoing project, and this report will be updated continuously. All newly trained multimodal foundation models are publicly released to facilitate further research in this direction. In the future, SenseNova-SI will be integrated with larger-scale in-house models.

Models Zoo

Model	Base Architecture	SI Dataset Scale	EASI-8	Other Remarks
SenseNova-SI-1.5-InternVL3-8B	SenseNova-SI-1.4-InternVL3-8B	1.5M	64.4	Enhanced capability in solid geometry
SenseNova-SI-1.4-InternVL3-8B	InternVL3	29M	63.7	Enhanced capability in grounding and depth estimation
SenseNova-SI-1.3-InternVL3-8B	InternVL3	14M	65.2	Best in spatial intelligence, with enhanced capabilities for open-ended short QA
SenseNova-SI-1.3-Qwen3-VL-8B	Qwen3-VL	14M	61.4	Enhanced capability for open-ended short QA
SenseNova-SI-1.2-InternVL3-8B	InternVL3	10M	64.5	-
SenseNova-SI-1.1-InternVL3-8B	InternVL3	8M	61.5	-
SenseNova-SI-1.1-InternVL3-2B	InternVL3	8M	49.4	-
SenseNova-SI-1.1-Qwen3-VL-8B	Qwen3-VL	8M	58.1	-
SenseNova-SI-1.1-Qwen2.5-VL-7B	Qwen2.5-VL	8M	51.0	-
SenseNova-SI-1.1-Qwen2.5-VL-3B	Qwen2.5-VL	8M	45.7	-
SenseNova-SI-1.1-BAGEL-7B-MoT	BAGEL	8M	48.6	Unified understanding and generation model

Release Information

Currently, we build SenseNova-SI upon popular open-source foundation models to maximize compatibility with existing research pipelines. In this release, we present SenseNova-SI-1.3-Qwen3-VL-8B, SenseNova-SI-1.5-InternVL3-8B, SenseNova-SI-1.4-InternVL3-8B, SenseNova-SI-1.3-InternVL3-8B, SenseNova-SI-1.2-InternVL3-8B, SenseNova-SI-1.1-Qwen2.5-VL-3B, SenseNova-SI-1.1-Qwen2.5-VL-7B, and SenseNova-SI-1.1-Qwen3-VL-8B.

SenseNova-SI-1.3-Qwen3-VL-8B demonstrates strong spatial intelligence across a wide range of benchmarks. It also improves open-ended spatial question-answering capabilities compared to previous versions.

Model	VSI	MMSI	MindCube-Tiny	ViewSpatial	SITE	BLINK	3DSRBench	EmbSpatial-Bench
Open-source Models (~2B)
InternVL3-2B	32.9	26.5	37.5	32.5	30.0	50.8	47.7	60.1
Qwen3-VL-2B-Instruct	50.3	28.9	34.5	36.9	35.6	53.2	47.5	70.1
MindCube-3B-RawQA-SFT	17.2	1.7	51.7	24.1	6.3	35.1	2.8	37.0
SpatialLadder-3B	44.8	27.4	43.4	39.8	27.9	43.0	42.8	58.2
SpatialMLLM-4B	46.3	26.1	33.4	34.6	18.0	40.5	36.2	50.0
VST-3B-SFT	57.9	30.2	35.9	52.8	35.8	58.8	54.1	69.0
Cambrian-S-3B	57.3	25.2	32.5	39.0	28.3	37.7	50.9	63.5
Open-source Models (~8B)
InternVL3-8B	42.1	28.0	41.5	38.6	41.1	53.5	44.3	76.4
Qwen3-VL-8B-Instruct	57.9	31.1	29.4	42.2	45.8	66.7	53.9	77.7
BAGEL-7B-MoT	31.4	31.0	34.7	41.3	37.0	63.7	50.2	73.1
SpaceR-7B	41.5	27.4	37.9	35.8	34.2	49.6	40.5	66.9
ViLaSR-7B	44.6	30.2	35.1	35.7	38.7	51.4	46.6	67.3
VST-7B-SFT	60.6	32.0	39.7	50.5	39.6	61.9	54.6	73.7
Cambrian-S-7B	67.5	25.8	39.6	40.9	33.0	37.9	54.8	72.8
SenseNova-SI-1.3-Qwen3-VL-8B	67.8	39.5	68.3	55.8	57.5	63.0	57.3	82.1
Proprietary Models
Gemini-2.5-pro-2025-06	53.5	38.0	57.6	46.0	57.0	73.5	59.3	78.9
Grok-4-2025-07-09	47.9	37.8	63.5	43.2	47.0	56.4	54.9	75.7
GPT-5-2025-08-07	55.0	41.8	56.3	45.5	61.8	68.0	60.3	81.6

QuickStart

Installation

We recommend using uv to manage the environment.

uv installation guide: https://docs.astral.sh/uv/getting-started/installation/#installing-uv

bash

git clone git@github.com:OpenSenseNova/SenseNova-SI.git
cd SenseNova-SI/
uv sync --extra cu124 # or one of [cu118|cu121|cu124|cu126|cu128|cu129], depending on your CUDA version
uv sync
source .venv/bin/activate

Hello World

A simple image-free test to verify environment setup and download the model.

bash

python example.py \
  --question "Hello" \
  --model_path sensenova/SenseNova-SI-1.3-Qwen3-VL-8B

Examples

Example 1

This example is from SITE-Bench:

bash

python example.py \
  --image_paths examples/Q1_1.png \
  --question "Consider the real-world 3D locations of the objects. Which is closer to the sink, the toilet paper or the towel?\nOptions: \nA. toilet paper\nB. towel\nGive me the answer letter directly. The best answer is:" \
  --model_path sensenova/SenseNova-SI-1.3-Qwen3-VL-8B
# --model_path sensenova/SenseNova-SI-1.3-InternVL3-8B

Details of Example 1

Q:Consider the real-world 3D locations of the objects. Which is closer to the sink, the toilet paper or the towel?\nOptions: \nA. toilet paper\nB. towel\nGive me the answer letter directly. The best answer is:

GT: A

Example 2

This example is from MMSI-Bench:

bash

python example.py \
  --image_paths examples/Q2_1.png examples/Q2_2.png \
  --question "If the landscape painting is on the east side of the bedroom, where is the window located in the bedroom?\nOptions: A. North side, B. South side, C. West side, D. East side\nAnswer with the option's letter from the given choices directly. Enclose the option's letter within ``." \
  --model_path sensenova/SenseNova-SI-1.3-Qwen3-VL-8B
# --model_path sensenova/SenseNova-SI-1.3-InternVL3-8B

Details of Example 2

Q:If the landscape painting is on the east side of the bedroom, where is the window located in the bedroom?\nOptions: A. North side, B. South side, C. West side, D. East side\nAnswer with the option's letter from the given choices directly. Enclose the option's letter within ``.

GT: C

Example 3

This example is from MMSI-Bench and tests the model on open-ended short-answer questions:

bash

python example.py \
  --image_paths examples/Q3_1.png examples/Q3_2.png examples/Q3_3.png \
  --question "The robot is making tea. What is the order in which the pictures were taken?" \
  --model_path sensenova/SenseNova-SI-1.3-Qwen3-VL-8B

Details of Example 3

Q:The robot is making tea. What is the order in which the pictures were taken?

GT: Second, first, third

Test Multiple Questions in a Single Run

Prepare a file similar to examples/examples.jsonl, where each line represents a single question.

The model is loaded once and processes questions sequentially. The questions remain independent of each other.

For more details on the jsonl format, refer to the documentation for Single-Image Data and Multi-Image Data.

bash

python example.py \
  --jsonl_path examples/examples.jsonl \
  --model_path sensenova/SenseNova-SI-1.3-Qwen3-VL-8B
# --model_path Qwen/Qwen3-VL-8B-Instruct

Evaluation

To reproduce the benchmark results above, please refer to EASI to evaluate SenseNova-SI on mainstream spatial intelligence benchmarks.

Citation

bib

@InProceedings{sensenova-si,
  title = {Scaling Spatial Intelligence with Multimodal Foundation Models},
  author = {Cai, Zhongang and Wang, Ruisi and Gu, Chenyang and Pu, Fanyi and Xu, Junxiang and Wang, Yubo and Yin, Wanqi and Yang, Zhitao and Wei, Chen and Sun, Qingping and Zhou, Tongxi and Li, Jiaqi and Pang, Hui En and Qian, Oscar and Wei, Yukun and Lin, Zhiqian and Shi, Xuanke and Deng, Kewang and Han, Xiaoyang and Chen, Zukai and Fan, Xiangyu and Deng, Hanming and Lu, Lewei and Pan, Liang and Li, Bo and Liu, Ziwei and Wang, Quan and Lin, Dahua and Yang, Lei},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2026}
}