# Code Repair

Code assistants often emit Python calls that belong to an older release and no longer exist in the version a user has installed. PyPI API Drift statically extracts, without importing, the public APIs of 55,565 releases across the 1,872 most-downloaded libraries, recording signatures and release-to-release changes while separating true removals from names that merely left the documented API. In a preregistered code-repair experiment, four models from one family raised API-compatible, intent-preserving migrations to 68.5%–69.9% with this API history, against 7.0%–56.6% unaided.

Code assistants often emit Python calls that belong to an older release and no longer exist in the version a user has installed. PyPI API Drift statically extracts, without importing, the public APIs of 55,565 releases across the 1,872 most-downloaded libraries, recording signatures and release-to-release changes while separating true removals from names that merely left the documented API. In a preregistered code-repair experiment, four models from one family raised API-compatible, intent-preserving migrations to 68.5%–69.9% with this API history, against 7.0%–56.6% unaided.

Automatic repair of PDE solvers now runs on a discrete diffusion language model: instead of rewriting the whole file left to right, it re-masks only the faulty code span and infills it with both left and right context visible. On five PDEBench families, with the same checkpoint, localized infilling raised average Pass@32 from 0.4938 to 0.5063 and cut per-attempt repair latency from 64.85 s to 0.748 s; from-scratch generation remains slower than same-scale autoregressive models, and worst-case end-to-end cost averages 55.5 s.

Automatic repair of PDE solvers now runs on a discrete diffusion language model: instead of rewriting the whole file left to right, it re-masks only the faulty code span and infills it with both left and right context visible. On five PDEBench families, with the same checkpoint, localized infilling raised average Pass@32 from 0.4938 to 0.5063 and cut per-attempt repair latency from 64.85 s to 0.748 s; from-scratch generation remains slower than same-scale autoregressive models, and worst-case end-to-end cost averages 55.5 s.