安德森角
独立科学家的崛起
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多年来，我一直持有科学论文独有作者地位在可信度不利。 这并非一个不知情的偏见；点击独创的论文
最糟糕的是，独创的论文暗示了弗兰肯斯坦的影子 — — 执着于一个想法的中央人物，但由于这个想法的非理性或边缘性，无法找到合作者；然后独自发表，以防他们的边缘观点在读者中找到一个回应的声音。
(不过，我应当指出，除了计算机科学之外，还有其他几个类别，其中唯一作者身份即使不是规范也是常见的，例如人文科学中的各种分支，他们领域一些最重要的作品是单智的产物).
人人为一
然而，最近，我注意到，在Arxiv预印服务器上出现的唯一作者论文不仅数量更多，甚至到了我不再感到厌恶的地步；而且令人惊讶的是，这些论文的质量似乎明显优于往年。
这一断断续续的观察在今天的一项新研究中得到了直接的处理 — — 这项研究来自一位主要使用Claude Opus 5.5的独家作者，该研究在arxiv.org分析提交统计数据，而发现则认为，自2025年末以来，独家作者撰写的科学论文比例急剧上升 — — 特别是在数学、凝聚物物理和定量生物学方面。
2026年头9个月，新论文* 称，单作者论文在数学方面增加了70%；在凝聚物物理方面增加了71%；在定量生物学方面增加了44%. 在数学方面，一个人撰写的论文比例从长期平均28.8%上升到2026年9月的37.7%。
值得注意的是，实验粒子物理学没有出现这种转变，而大型研究小组仍然是必不可少的。 论文作者本人在论文的结论中承认，他的全部贡献是 " 与[Claude]的约60次互动 "，这表明代理AI可能正在授权科学家个人从事以前需要合作的研究。
研究还发现，科学论文中的爆炸并非像Arxiv本身所认为的那样，主要是几个具有超生产力的个人在提交材料时淹没arXiv的工作；而是更多的科学家在发表 — — 并且越来越多地单独发表：
`每个子体随附的arXiv公告将激增部分归因于少数提交大量论文的作者。 对于增加最多的数学领域，元数据并不支持将此作为主要机制。
全部新增上线
单作者增长效应在数学中最为明显，一年之内提交量翻了一番，而数学中依靠抽象推理而不是计算功能的分支的增长最为明显。 在这方面，文件建议，大赦国际可能正在从事以前被认为难以自动化的智力工作。
作者在伦敦帝国学院物理系工作时，也考虑这种 " 暗藏 " 或感知的自动化可能侵犯新研究的专利性的程度，或研究者决定发布与QQ的许可证限制程度的能力：
`归属也具有法律层面。 根据美国法律，人工智能系统生成的材料没有充足的人类创造性控制，因此没有资格获得版权[19,20]. 随着人对纸张的贡献从执行转向方向，作者和创作的作品之间的界限，以及个人智力信用的基础，将更加难以划分。
`结果可能是重新谈判个人著作权的模式，或者为共同工具产生的结果而向集体信用迈进；或者改变学术生涯的组成。
‘这些问题与其力学一样，也涉及学术研究的目的。 提交记录的变化速度表明，他们需要在几年内而不是几十年内得到答复。
秘密革命组织
自然，也许并非最不重要的是，由于上述未知的法律影响，为新工作研究的Arxiv提交元数据并未特别显示在制作过程中使用代理AI的情况有所增加。
相反，作者从arXiv历史增长率的急剧上升中推断出这一点，在2026年初，平均每年增长约9 %，而2026年初，也就是在AI模型发布后不久，这种模型能够在没有监督的情况下开展延长的研究任务：
2022至2025年间，arXiv的加速增长在很大程度上是由AI研究的繁荣推动的，论文称；但自2025年12月以来，计算机科学以外的提交量一直比预期水平高出17%,AI研究本身没有类似的加速。
这与目前流行的观点相悖，即计算机科学类单独飞升，而相邻类则不受影响：
本文还称，这一变化的时机不同于2022年末ChatGPT的到来，这导致计算机科学以外的提交量没有明显增加：
`2022年以后总量的加速[可归结]完全归因于计算机科学和AI研究量的增长；广泛对话的LLM使用期在其他领域的提交量中没有可探测的痕迹 '
因此，早期的人工智能助理似乎对总体科学产出影响不大，而代理系统的到来显然恰逢出版活动发生重大变化。
虽然证据是间接的，但计算机科学以外的成长时间和集中似乎支持了作者的解释：
‘arXiv提交记录经过分析，没有试图检测机器生成的文本，它包含了2026年初与AI研究增长分开的明显断裂，跟随代理模型的到来，集中在可以完全在一个办公桌进行研究的领域中，并伴随着转向单作者论文。
作者提出，ArXiv对每个帐户提交文件的每月2个新限制可能无法遏制这一增加，因为大部分额外材料来自越来越多的科学家每份只提交一至两份论文，而不是少数可以被新政策 " 限制费率 " 的异常大量贡献者。
作者指出进一步表示，数学论文的激增融资质疑出版计数是否仍然是科学成就的公平衡量标准，特别是因为提交书的翻一番并不一定表明既定结论的相应增加：
`就数学而言，一年内提交材料翻一番是科学要求最高的证明和审查惯例领域产出的一个前所未有的变化；额外论文是否代表既定结果的相应增加，是提交记录本身无法回答的问题，也是数学界最适于审查的问题。
结论
AI的通过是一个非常秘密的革命 — — 尤其是在公共和私人研究中 — — 一种出乎意料的反公司公开决议，甚至连AI诉讼的过量都可能影响从历史数据中受益的当前研究状况(几乎全部如此)。
因此，具有讽刺意味的是，我们只能推断出人工智能对一般研究的影响 — — 特别是科学研究，其中变量的定义更好，更容易自动化。
因此，将新文件视为“投机”很容易。 但是，正如“神秘”历史的任何其他分支一样，一旦投机成为你的全部，它就会升级为“分析”。
至于“龙狼”的科学家 — — 大部分科学学科 — — 男性独创者能够真正做出任何损害的学科 — — 不管怎样，都必然以团队为基础。 无论如何，这种陈规定型显然具有侮辱性，作为代理性AI，以及一般AI，显然在文献中提供了不受支持或资源不足的声音。
* 标题是`启示录在我们身上吗？预印成长的突破与代理AI同时发生。
我用作者的内在引用代替超链接。
初刊于2026年10月28日，星期三
本文由 AI图鉴自动抓取并翻译，版权归原作者所有；如需查看原文请点下方链接。
Anderson's Angle
The Rise of the Lone Scientist Under Agentic AI
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I have for some years held scientific papers with sole authorship at a disadvantage in terms of credibility. It’s not an uninformed prejudice; too often a click on a sole-authored paper with an interesting premise leads into a self-indulgent and clearly under-challenged series of suppositions.
At their worst, solo-author papers suggest shades of Frankenstein – obsessive central figures fixated on an idea, but unable to find collaborators due to the irrationality or marginality of that idea; and then going it alone and publishing anyway, in case their fringe viewpoint should find an answering voice among the readership.
(I should note, however, that there are several categories besides Computer Science where sole authorship is common, if not the norm, such as the various strands in the Humanities, where some of the most significant works in their field are the products of single minds)
All For One
Lately, however, I have noticed not only that there are a greater number of sole-author papers turning up in the Arxiv preprint server, even to the point where I can no longer indulge my aversion; but, surprisingly, that the quality of these papers seems to be notably better than in prior years.
This intermittent observation has today been directly addressed in a new study – itself from a sole author heavily using Claude Opus 5.5 – that analyzes submission statistics at arxiv.org, and finds that the proportion of scientific papers written by lone authors has risen steeply since late 2025 – particularly in mathematics, condensed matter physics and quantitative biology.
In the first nine months of 2026, the new paper* asserts, single-author papers increased by 70% in mathematics; 71% in condensed matter physics; and 44% in quantitative biology. And, in mathematics, the share of papers written by one person had risen from a longstanding average of 28.8% to 37.7% by September of 2026:
Of note is that the shift is absent from experimental particle physics, where large research teams are still essential. The author of the paper – who himself confesses at the paper’s conclusion that his entire contribution to it ‘consisted of some 60 interactions with [Claude]’ – suggests that agentic AI may be empowering individual scientists to undertake research that would previously have required collaborators.
The study also found that the explosion in scientific papers is not chiefly the work of a few hyperproductive individuals flooding arXiv with submissions, as Arxiv itself contends; but rather that far more scientists are publishing – and, increasingly, doing so alone†:
‘The arXiv announcement accompanying the per-submitter limit attributes the surge in part to a small number of authors submitting very large numbers of papers. For mathematics, the field with the largest increase, the metadata do not support this as the principal mechanism.’
It All Adds Up
The lone-author growth effect is most evident in mathematics, where submissions doubled within a year, with the increase most pronounced in branches of mathematics that rely on abstract reasoning rather than computing. In regard to this, the paper suggests that AI may be taking on intellectual work previously considered difficult to automate.
The author, working from the Department of Physics at Imperial College London, also considers the extent to which this ‘tacit’ or perceived automation may encroach on the patentability of novel research, or the ability of researchers to decide how restrictive a license it is released with†:
‘Attribution also has a legal dimension. Under US law, material generated by an AI system without sufficient human creative control is not eligible for copyright [19, 20]. As the human contribution to a paper shifts from execution towards direction, the boundary between authored and generated work, and with it the basis of individual intellectual credit, will become harder to draw.
‘The outcome may be a renegotiated model of individual authorship, or a move towards collective credit for results produced with shared tools; either would change what an academic career consists of.
‘These questions concern the purpose of academic research as much as its mechanics. The pace of the change in the submission record suggests that they will need answers within years rather than decades.’
Secret Revolution
Naturally, perhaps not least because of the unknown, aforementioned legal implications, the Arxiv submission metadata studied for the new work does not disclose ad hoc a rise in the use of agentic AI during the production.
Instead, the author infers this from a steep rise in arXiv’s historical growth rate, which averaged around 9% annually for three decades before accelerating markedly in early 2026 – shortly after the release of AI models capable of conducting extended research tasks with little supervision:
Between 2022 and 2025, arXiv’s accelerating growth was largely driven by the boom in AI research, the paper asserts; but since December 2025, submissions outside computer science have been running 17% above their expected levels, with no comparable acceleration in AI research itself.
This defies the currently-popular idea that the Computer Science category is ballooning alone, while adjacent categories remain unaffected:
The paper also finds that the timing of this change is distinct from the arrival of ChatGPT in late 2022, which produced no detectable increase in submissions outside computer science:
‘The acceleration of the aggregate after 2022 is [attributable] entirely to growth in the volume of computer-science and AI research; the period of widespread conversational LLM use left no detectable trace in the volume of submissions in other fields’
Earlier generations of AI assistants therefore appear to have had little effect on overall scientific output, whereas the arrival of agentic systems apparently coincides with a substantial change in publishing activity.
While the evidence is circumstantial, the timing and the concentration of growth outside computer science appear to support the author’s interpretation:
‘The arXiv submission record, analysed without any attempt to detect machine-generated text, contains a distinct break in early 2026 that is separate from the growth of AI research, follows the arrival of agentic models, is concentrated in fields whose research can be carried out entirely at a desk, and is accompanied by a shift to single-author papers.’
The author suggests that arXiv’s new 2-per-month limits on submissions per account may not succeed in containing the increase, since much of that additional material comes from a growing number of scientists submitting just one or two papers each, rather than a handful of unusually prolific contributors who can be ‘rate-limited’ by the new policy.
The author notes further that the surge in mathematics submissions raises questions about whether publication counts remain a fair measure of scientific achievement – particularly since the doubling of submissions does not necessarily indicate a corresponding increase in established findings:
‘For mathematics in particular, the doubling of submissions within a year is an unprecedented change in the output of a field whose conventions of proof and review are among the most demanding in science; whether the additional papers represent a corresponding increase in established results is a question the submission record alone cannot answer, and one that the mathematical community is best placed to examine.’
Conclusion
AI adoption is very much a secret revolution – not least in public and private research, where an unexpectedly anti-corporate public resolution to even one of the glut of pending AI lawsuits could affect the status of current research that has benefited from historical data (that’s pretty much all of it).
Therefore, ironically, inference is all we have with which to estimate the effect that agentic AI is having on research in general – and particularly science research, where the variables are better-defined and more susceptible to automation.
So it’s easy to dismiss the new paper as ‘speculation’. But, as in any other branch of ‘occult’ history, once speculation is all you have, it gets upgraded to ‘analysis’.
As for the ‘lone wolf’ scientist – well, most of the scientific disciplines where a maleficent sole creator could really do any damage are necessarily team-based anyway. And, in any case, this stereotype is clearly degrading, as agentic AI, and AI in general, is clearly offering unsupported or under-resourced voices a chance to be heard in the literature.
* Titled ‘Is the arXiv Apocalypse upon us? A break in preprint growth coincident with agentic AI’.
† My substitution of the author’s inline citations for hyperlinks.
First published Wednesday, October 28, 2026
Auto-fetched and translated by AI Atlas. All rights belong to the original author.