MIT asks arXiv to withdraw preprint of paper on AI and scientific discovery

1. The data in most of the plots (see the appendix) look fake. Real life data does not look that clean.

2. In May of 2022, 6 months before chatGPT put genAI in the spotlight, how does a second-year PhD student manage to convince a large materials lab firm to conduct an experiment with over 1,000 of its employees? What was the model used? It only says GANs+diffusion. Most of the technical details are just high-level general explanations of what these concepts are, nothing specific.

"Following a short pilot program, the lab began a large-scale rollout of the model in May of 2022." Anyone who has worked at a large company knows -- this just does not happen.

  % gunzip -c arXiv-2412.17866v1.tar.gz | tar xOf - main.tex | grep '\bI have\b'
  To summarize, I have established three facts. First, AI substantially increases the average rate of materials discovery. Second,  it  disproportionately benefits researchers with high initial productivity. Third, this heterogeneity is driven almost entirely  by differences in judgment. To understand the mechanisms behind these results, I investigate the dynamics of human-AI collaboration in science.
          \item Compared to other methods I have used, the AI tool generates potential materials that are more likely to possess desirable properties.
          \item The AI tool generates potential materials with physical structures that are more distinct than those produced by other methods I have used.
  % gunzip -c arXiv-2412.17866v1.tar.gz | tar xOf  - main.tex | grep '\b I \b' | wc
      25    1858   12791
  %