Beichen Lu, Honglin Bao, Qi Huang, Qingguo Meng, James A. Evans · 2026 · arXiv
DOI: 10.48550/arXiv.2610.27296 · open-access PDF
人工知能の進歩は、より多くの研究者、論文、投資が発見を加速させるべきであると広く考えられています。テクノロジー成長の理論は、代わりに発見の困難をアイデアの領域に位置づけています。ここでは、規模も空間も十分ではないことを示しています:進歩は多様な注意に依存し、集団的集中が枯渇させることができます。コンピュータサイエンスの36年間を通じて、私たちは72089件の論文、73089件の同僚レビューと370万人の研究者とそのトピックの転換を分析しています。
Machine translation (Meta M2M100); the English original follows.
Progress in artificial intelligence is widely assumed to scale with effort: more researchers, papers and investment should accelerate discovery. Theories of technological growth instead locate the difficulty of discovery in the space of ideas. Here we show that neither scale nor space is enough: breakthroughs depend on diverse attention, which collective concentration can deplete. Across 36 years of computer science, we analyze 7.2 million papers, 73,089 peer reviews and 3.7 million researchers and their topic shifts. The field expanded while its attention narrowed sharply after the Transforme
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