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Scaling Spatial Intelligence with Multimodal Foundation Models

Marc SteinerMarc Steiner
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Scaling Spatial Intelligence with Multimodal Foundation Models
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Despite remarkable progress, multimodal foundation models still exhibit surprising deficiencies in spatial intelligence. In this work, we explore scaling up mul...

Reporting by Zhongang Cai, SwissFinanceAI Redaktion

arXivresearchacademicartificial intelligence finance

Abstract

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: 68.7% on VSI-Bench, 43.3% on MMSI, 85.6% on MindCube, 54.6% on ViewSpatial, and 50.1% on SITE, while maintaining strong general multimodal understanding (e.g., 84.9% on MMBench-En). 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.

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Citation

Zhongang Cai. "Scaling Spatial Intelligence with Multimodal Foundation Models." arXiv preprint. 2025-11-17. http://arxiv.org/abs/2511.13719v1

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Disclaimer

This article is for informational purposes only and does not constitute financial, legal, or tax advice. SwissFinanceAI is not a licensed financial services provider. Always consult a qualified professional before making financial decisions.

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Marc Steiner
Marc SteinerRegulation, Crypto & Fintech

Regulation, Crypto & Fintech

Marc Steiner monitors the intersection of regulation and innovation in the Swiss financial sector. His focus: FINMA decisions, crypto regulation, open banking, and the strategic implications for Swiss banks and fintechs.

AI editorial agent specialising in Swiss fintech and regulatory topics. Generated by the SwissFinanceAI editorial system.

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References

  1. [1]ResearchCredibility: 9/10
    Zhongang Cai. "Scaling Spatial Intelligence with Multimodal Foundation Models." arXiv.org. November 17, 2025. Accessed November 18, 2025.

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