AI论文的破绽不在句子，在整篇的推理链
AI写的论文，单看每一句都像人话，但整篇连起来就露馅——段落之间该有的因果推理断了。研究者做了个390篇的对照测试：同一研究问题，AI写一篇、人写一篇，靠六个维度(结构、论证、痕迹)去挑，挑中AI的准确率85.9%，比现有最强检测器高17个百分点。更狠的是，这个破绽跟论文质量直接挂钩：AI味越重的论文，在ICLR评审里分越低，而且从2017到2025每年都能靠它区分录用和拒稿。但反过来想用这个指标去教AI改掉毛病，直接优化会翻车——AI学会刷指标而不是真改。于是他们换了个思路：不让AI自由发挥，只允许它在实验数据支持的地方改，这样AI和人的差距缩小了63%。这不是你明天能用的工具，但它说明一件事：AI的短板不在文笔，在全局推理，而想修这个短板，靠的是证据约束，不是润色。
📄 原文摘要(英文)
AI-generated content, often called AI slop, is increasingly common everywhere, particularly in academia. Slop in AI-generated scientific papers, however, has more complex patterns that cannot be easily detected by existing token-based AI detectors. Each part of such a paper looks plausible while the scientific reasoning that connects the parts breaks down, which can mislead how readers assess the work. We benchmark these failures as scientific slop through six measures across Structure, Argument, and Artifacts. We construct SciSlopBench with 390 AI-generated papers, mostly in computer science but spanning the life, social, and natural sciences, each paired with a human-written paper matched by research problem and contribution type. Our measures identify the AI paper in each pair with 85.9% accuracy, compared with 68.7% for Binoculars. Higher scientific slop accompanies lower ICLR ratings and distinguishes rejected from accepted papers above chance in every year from 2017 to 2025. Reducing these patterns, however, is not as simple as directly optimizing the measures. We therefore propose SciSlopHarness, a harness-level framework that guides a fixed LLM to revise slop only where the experiment records support the change. While standard revisions leave residual slop and direct slop-aware prompting triggers reward hacking, SciSlopHarness reduces the remaining AI-human gap by 63% over the strongest revision baseline without requiring human reference targets. Overall, we demonstrate that AI-generated scientific papers leave fundamental traces in their global reasoning, and that responsible mitigation demands strict evidentiary grounding rather than mere prose refinement.