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Updated through the day. Every headline links straight to the source. The two lines underneath are ours.
This is a real problem: many papers are written for novelty, not reproducibility. If you're building systems that must turn papers into working code—whether for automation or verification—this benchmark matters. The dataset is substantial enough to be useful. It also signals something uncomfortable: the paper-to-code gap is large enough that it needs its own research infrastructure.
This is the kind of unglamorous infrastructure work that actually tells you how much of published ML research holds up, and a 2,200-paper sample size is large enough to draw real conclusions from. Worth reading for anyone deciding which papers are worth building on versus citing uncritically. The reproducibility rate itself, whatever it turns out to be, is more useful than any single paper's claimed result.