arXiv cs.AI· Linrong Cai, Taiming Lu, Haozhe Chen, Zhuang Liu·· 11 小时前AI 评分45
XStack：评估 AI 智能体迁移 ML 仓库的基准
XStack: A Benchmark for ML Repo Migration
AI 导读
研究者推出 XStack 基准，用于评估 AI 智能体将 PyTorch 仓库迁移到 JAX/TPU 生态的能力，智能体需交付通过评分标准的 JAX 仓库。测试中 Claude Opus 5 通过率 75%，GPT-5.6-sol 为 45%，其余智能体均不超过 40%。该基准已发布，旨在让跨框架、跨硬件的可靠迁移变得可衡量。
正文 · 原文
Abstract: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 repositories between them means building and testing across frameworks and hardware. This migration is (1) a practical need, since it unlocks different hardware for researchers, and (2) a natural benchmark: it involves different frameworks and hardware, covers whole repositories, and is long-horizon and challenging. We introduce XStack, a benchmark that evaluates agents on migrating ML repositories between these two ecosystems. Each agent receives a PyTorch repository and a clean TPU machine and must deliver a JAX repository. Its output passes if it meets our grading criteria, based on the source repository's published result, authenticated and calibrated by us. Migration by agents remains unreliable: Claude Opus 5 passes 75%, GPT-5.6-sol 45%, and every other agent at most 40%. XStack is a step toward freeing ML code from its ecosystem. We release XStack to make progress toward reliable migration across frameworks and hardware measurable.
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2611.08472 [cs.LG] |
| (or arXiv:2611.08472v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2611.08472 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Linrong Cai [view email]
[v1]
Thu, 5 Nov 2026 18:55:54 UTC (367 KB)
来源：arXiv cs.AI · arxiv.org