XStack: A Benchmark for ML Repo Migration
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
The Flow has not summarised this story yet — read it at arXiv AI.
ML code should run where researchers choose, not where it was first written. Today, machine learning code mainly lives in two ecosystems, PyTorch on NVIDIA GPUs and JAX on Google TPUs. Migrating repos...
The study investigates whether coding agents can reliably migrate PyTorch machine learning projects from GPUs to TPUs. Using Claude Code, ten projects were migrated, with eight meeting the project‑goal criterion but 76% of execution attempts failing, often due to compilation issues or silent changes in computation. The research highlights significant human effort (average 2.4 interventions) and variable costs, noting that while some TPUs can be faster and cheaper, failures and accuracy drops remain common.
arXiv:2605.29054v3 Announce Type: replace-cross Abstract: Migrating a machine-learning codebase across frameworks--for example from PyTorch to JAX--is a routine but costly engineering task that is fu...
arXiv:2607. 02469v1 Announce Type: cross Abstract: Software tests and code evolve together: a code change should be followed by new or updated tests that record the new software behavior.
arXiv:2608. 08189v1 Announce Type: new Abstract: LLM-driven program discovery relies on rapid evaluator feedback, but many scientific and engineering tasks require high-fidelity simulations, hardware execution, or physical experiments, making each evaluation expensive.
arXiv:2603. 16011v3 Announce Type: replace-cross Abstract: Large language model (LLM) coding agents increasingly operate at the repository level, motivating benchmarks that evaluate their ability to optimize entire codebases under realistic constraints.