# DiffPDE

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.