MIT博士爆火论文造假，学校官宣撤稿！被骗诺奖导师亲手举报，愤而割席

Core Viewpoint - A recent paper by an MIT PhD student, which claimed significant productivity improvements in scientific research through AI, is likely to be fraudulent and has been requested for retraction by MIT [2][10][11]. Group 1: Paper Overview - The paper, published last year, showcased how AI tools enhanced productivity in a large materials science laboratory, claiming a 44% increase in new materials discovered and a 39% rise in patent applications [3][31]. - The research received widespread acclaim and was considered one of the best papers on AI's impact on scientific discovery [5][31]. - The student had submitted the paper to a top economics journal and received a request for revisions, indicating potential publication [9]. Group 2: Retraction and Investigation - Following growing skepticism about the paper's data integrity, the student's advisors, Nobel laureates Daron Acemoglu and David Autor, publicly requested its retraction [10][26]. - MIT's Economics department announced an internal review, concluding that the paper must be retracted due to concerns over data authenticity [17][22]. - The student has since left MIT and is no longer affiliated with the institution [21]. Group 3: Data Integrity Concerns - Initial doubts about the paper arose shortly after its publication, with experts questioning the feasibility of the data collection methods described [22][41]. - Critics highlighted that the paper's claims seemed implausible, particularly regarding the access a second-year PhD student would have to sensitive data from a large corporation [44][68]. - The paper's methodology and results were criticized for lacking rigor and for presenting overly perfect outcomes that raised red flags about data authenticity [73][96]. Group 4: Academic and Industry Reactions - The academic community has reacted with skepticism, with some experts suggesting that the paper's findings were too good to be true and indicative of potential data fabrication [39][70]. - The incident has sparked discussions about the reliability of research in the AI and materials science fields, emphasizing the need for rigorous validation of data sources [88][92].