r/MachineLearning found the 1,200-paper list useful, but the thread immediately separated “has a link” from “can reproduce the result.” Comments pointed to missing papers, 404s, and the gap between public code and runnable research.
r/MachineLearning reacted because the sample was small but painfully familiar: one user said 4 of 7 paper claims they checked this year did not reproduce, with 2 still sitting as unresolved GitHub issues. The comments moved from resignation about reviewers not running code to concrete demands for submission-time reproducibility reports.
A new paper discussed in r/MachineLearning argues that unofficial model-access providers can quietly substitute models and distort both research and production results.
A prominent r/MachineLearning thread highlighted arXiv 2603.01919, which audits shadow APIs claiming GPT-5 and Gemini-2.5 access and reports large performance drift, unstable safety behavior, and frequent identity-verification failures.