# Why Open Small AI Models Matter for Interactive Art

arXiv: https://arxiv.org/abs/2511.09788 · HTML: https://mlsanity.com/paper/2511.09788/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 21 references that are in the map.
- First posted on arXiv 12 Nov 2025.

## Builds on (most cited first, 8 of 8)

- [Power Hungry Processing: Watts Driving the Cost of AI Deployment?](https://mlsanity.com/paper/2311.16863/) (2023)
- [Art and the science of generative AI: A deeper dive](https://mlsanity.com/paper/2306.04141/) (2023)
- [MotionStream: Real-Time Video Generation with Interactive Motion Controls](https://mlsanity.com/paper/2511.01266/) (2025)
- [On the Societal Impact of Open Foundation Models](https://mlsanity.com/paper/2403.07918/) (2024)
- [Active Divergence with Generative Deep Learning -- A Survey and Taxonomy](https://mlsanity.com/paper/2107.05599/) (2021)
- [XAIxArts Manifesto: Explainable AI for the Arts](https://mlsanity.com/paper/2502.21220/) (2025)
- [Explainability Paths for Sustained Artistic Practice with AI](https://mlsanity.com/paper/2407.15216/) (2024)
- [Visions Of Destruction: Exploring Human Impact on Nature by Navigating the Latent Space of a Diffusion Model via Gaze](https://mlsanity.com/paper/2401.06361/) (2023)

## Abstract (arXiv)

This position paper argues for the importance of open small AI models in creative independence for interactive art practices. Deployable locally, these models offer artists vital control over infrastructure and code, unlike dominant large, closed-source corporate systems. Such centralized platforms function as opaque black boxes, imposing severe limitations on interactive artworks, including restrictive content filters, preservation issues, and technical challenges such as increased latency and limited interfaces. In contrast, small AI models empower creators with more autonomy, control, and sustainability for these artistic processes. They enable the ability to use a model as long as they want, create their own custom model, either by making code changes to integrate new interfaces, or via new datasets by re-training or fine-tuning the model. This fosters technological self-determination, offering greater ownership and reducing reliance on corporate AI ill-suited for interactive art's demands. Critically, this approach empowers the artist and supports long-term preservation and exhibition of artworks with AI components. This paper explores the practical applications and implications of using open small AI models in interactive art, contrasting them with closed-source alternatives.