# XTC, A Research Platform for Optimizing AI Workload Operators

arXiv: https://arxiv.org/abs/2512.16512 · HTML: https://mlsanity.com/paper/2512.16512/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 23 references that are in the map.
- First posted on arXiv 18 Dec 2025.

## Builds on (most cited first, 7 of 7)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Ansor: Generating High-Performance Tensor Programs for Deep Learning](https://mlsanity.com/paper/2006.06762/) (2020)
- [Learning to Optimize Tensor Programs](https://mlsanity.com/paper/1805.08166/) (2018)
- [MLIR: A Compiler Infrastructure for the End of Moore's Law](https://mlsanity.com/paper/2002.11054/) (2020)
- [TinyIREE: An ML Execution Environment for Embedded Systems from Compilation to Deployment](https://mlsanity.com/paper/2205.14479/) (2022)
- [The MLIR Transform Dialect. Your compiler is more powerful than you think](https://mlsanity.com/paper/2409.03864/) (2024)
- [Exo 2: Growing a Scheduling Language](https://mlsanity.com/paper/2411.07211/) (2024)

## Abstract (arXiv)

Achieving high efficiency on AI operators demands precise control over computation and data movement. However, existing scheduling languages are locked into specific compiler ecosystems, preventing fair comparison, reuse, and evaluation across frameworks. No unified interface currently decouples scheduling specification from code generation and measurement. We introduce XTC, a platform that unifies scheduling and performance evaluation across compilers. With its common API and reproducible measurement framework, XTC enables portable experimentation and accelerates research on optimization strategies.