SenseNova-SI: Scaling Spatial Intelligence with Multimodal Foundation Models

[EASI Codebase] [EASI Leaderboard]

Overview

Despite remarkable progress, leading multimodal models still exhibit notable deficiencies in spatial intelligence: the ability to make metric estimations, understand spatial relationships, handle viewpoint changes, and integrate information across complex scenes. We take a scaling perspective: constructing and curating a large-scale, comprehensive collection of spatial intelligence data, and through continued training on powerful multimodal foundations, cultivating multi-faceted spatial understanding within the SenseNova-SI family of models. In the future, SenseNova-SI will be integrated with larger-scale in-house models.

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-InternVL3-2B and SenseNova-SI-InternVL3-8B, which achieve state-of-the-art performance among open-source models of comparable size across four recent spatial intelligence benchmarks: VSI, MMSI, MindCube, and ViewSpatial.

Model	VSI	MMSI	MindCube-Tiny	ViewSpatial
Open-source Models (~2B)
InternVL3-2B	32.98	26.50	37.50	32.56
Qwen3-VL-2B-Instruct	50.36	28.90	34.52	36.97
MindCube-3B-RawQA-SFT	17.24	1.70	51.73	24.14
MindCube-3B-Aug-CGMap-FFR-Out-SFT	29.60	29.10	41.06	30.90
MindCube-3B-Plain-CGMap-FFR-Out-SFT	29.93	30.40	39.90	31.20
SpatialLadder-3B	44.86	27.40	43.46	39.85
SpatialMLLM-4B	45.98	26.10	33.46	34.66
SenseNova-SI-InternVL3-2B	58.47	35.50	71.35	40.62
Open-source Models (~8B)
InternVL3-8B	42.14	28.00	41.54	38.66
Qwen3-VL-8B-Instruct	57.90	31.10	29.42	42.20
BAGEL-7B	30.90	33.10	34.71	41.32
SpaceR-7B	36.29	27.40	37.98	35.85
ViLaSR-7B	44.63	30.20	35.10	35.71
SenseNova-SI-InternVL3-8B	62.80	37.90	89.33	53.92
Proprietary Models
Gemini-2.5-pro-2025-06	53.57	38.00	57.60	46.06
Grok-4-2025-07-09	47.92	37.80	63.56	43.23
GPT-5-2025-08-07	55.03	41.80	56.30	45.59