# Extending Data Spatial Semantics for Scale Agnostic Programming

arXiv: https://arxiv.org/abs/2504.03109 · HTML: https://mlsanity.com/paper/2504.03109/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Apr 2025.

## Built on by (most cited first, 2 of 2)

- [GraphMend: Code Transformations for Fixing Graph Breaks in PyTorch 2](https://mlsanity.com/paper/2509.16248/) (2025)
- [Prompt Less, Smile More: MTP with Semantic Engineering in Lieu of Prompt Engineering](https://mlsanity.com/paper/2511.19427/) (2025)

## Abstract (arXiv)

We introduce extensions to Data Spatial Programming (DSP) that enable scale-agnostic programming for application development. Building on DSP's paradigm shift from data-to-compute to compute-to-data, we formalize additional intrinsic language constructs that abstract persistent state, multi-user contexts, multiple entry points, and cross-machine distribution for applications. By introducing a globally accessible root node and treating walkers as potential entry points, we demonstrate how programs can be written once and executed across scales, from single-user to multi-user, from local to distributed, without modification. These extensions allow developers to focus on domain logic while delegating runtime concerns of persistence, multi-user support, distribution, and API interfacing to the execution environment. Our approach makes scale-agnostic programming a natural extension of the topological semantics of DSP, allowing applications to seamlessly transition from single-user to multi-user scenarios, from ephemeral to persistent execution contexts, and from local to distributed execution environments.