Hi, Our paper "The Hidden Bloat in Machine Learning Systems" won the best paper award in MLSys this year. The paper introduces Negativa-ML, a tool that reduces the device code size in ML frameworks by up to 75% and the host code by up to 72%, resulting in total size reductions of up to 55%. The paper shows that the device code is a primary source of bloat within ML frameworks. Debloating results in reductions in peak host memory usage, peak GPU memory usage, and execution time by up to 74.6%, 69.6%, and 44.6%, respectively. We will be open sourcing the tool here, however, there is a second paper that need to be accepted first : [https://github.com/negativa-ai/](https://github.com/negativa-ai/) Link to paper: [https://mlsys.org/virtual/2025/poster/3238](https://mlsys.org/virtual/2025/poster/3238)