T2J-BENCH: Benchmarking Coding Agents on Execution-Grounded PyTorch-to-JAX Repository Migration
Migrating a machine-learning codebase across frameworks--for example from PyTorch to JAX--is a routine but costly engineering task that is fundamentally cross-paradigm: eager, stateful, object-oriented modules must be rewritten as pure, jittable functions over an explicit parameter pytree. We ask whether coding agents can perform such whole-repository migrations correctly, and argue that existing code benchmarks, which grade unit-test pass rates or surface-level similarity to a reference translation, cannot answer this: they neither require the migrated system to \emph{behave} the same nor prevent a JAX reimplementation from faking equivalence by secretly importing PyTorch itself. We intr...
| Authors | Tao Feng, Jiefeng Chen, Linxin Song et al. |
|---|---|
| Primary category | cs.SE |
| Published | 27 May 2026 |
| Last updated at source | 14 October 2026 |
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