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An AI Breakthrough Has Made Decades of Computer Science Research ‘Irrelevant’

Linköping University professor Peter Jonsson is set to have his work presented in two weeks’ time at one of the most prestigious conferences in theoretical computer science. As of Monday, much of that work is “dead,” he says, after AI showed that a 27-year-old hypothesis underpinning it is wrong.

林雪平大学教授彼得·琼森（Peter Jonsson）的研究即将在两周后于理论计算机科学领域最负盛名的会议之一上发表。然而他表示，自周一起，他的大部分研究成果已经“作废”，因为人工智能证明了一个支撑该研究长达27年的假设是错误的。

Both OpenAI and Anthropic claimed this week to have disproved the exponential time hypothesis (ETH). Vast swaths of theoretical computer science research on complexity and algorithm design have been built on the premise that the effort it takes to solve certain problems increases exponentially with the size of the problem.

本周，OpenAI 和 Anthropic 均声称已推翻了指数时间假设（ETH）。此前，大量关于复杂性和算法设计的理论计算机科学研究都建立在一个前提之上，即解决特定问题所需的工作量会随着问题规模的扩大而呈指数级增长。

Jonsson expects his predicament will be “very common” at the IEEE Symposium on Foundations of Computer Science, where his work will be presented by one of his coauthors. “People will start the talks: ‘ETH is false, half of my paper does not work,’” he says. It’s a vexing problem, Jonsson says, “but at the same time, we learn a lot from it. And this is what science is about.”

琼森预计，在即将召开的 IEEE 计算机科学基础研讨会上，他的处境将变得“非常普遍”，届时他的研究将由合著者代为发表。他说：“人们在演讲时会这样开头：‘ETH 是错误的，我论文的一半内容失效了。’”琼森认为这是一个令人烦恼的问题，“但与此同时，我们从中获益良多。这就是科学的意义所在。”

Researchers haven’t independently verified, let alone had time to fully understand, the AI-produced proofs, which both firms have published on GitHub in both natural language and its machine-checkable Lean format. OpenAI has asked the UC Santa Barbara computer scientists who originally proposed the hypothesis, plus an MIT academic who has refuted a version of ETH, to rally researchers to unpack the proofs. So far, this effort includes a Google form asking for volunteers and feedback, and a commitment from OpenAI to fund a conference on the topic.

研究人员尚未独立验证这些由人工智能生成的证明，更没有时间完全理解它们。两家公司已将这些证明以自然语言和机器可校验的 Lean 格式发布在 GitHub 上。OpenAI 已邀请最初提出该假设的加州大学圣塔芭芭拉分校计算机科学家，以及一位曾反驳过 ETH 变体的麻省理工学院学者，共同召集研究人员来解析这些证明。目前，这项工作包括一份征集志愿者和反馈的谷歌表格，以及 OpenAI 对资助相关会议的承诺。

UC Santa Barbara professor Daniel Lokshtanov plans to join the effort, as he estimates the result—if confirmed—will render him “galactically wrong” and make around 50 of his published papers either “pointless” or in need of a revisit.

加州大学圣塔芭芭拉分校教授丹尼尔·洛克什塔诺夫（Daniel Lokshtanov）计划加入这项工作，因为他估计如果该结果得到证实，他将变得“错得离谱”，且他已发表的约 50 篇论文要么变得“毫无意义”，要么需要重新审视。

“It’s just a giant bomb dropped into the middle of the field,” he says. “It’s really cool and terrible at the same time.”

“这就像一颗巨型炸弹投向了该领域，”他说，“这既非常酷，又非常可怕。”

Thore Husfeldt, head of the algorithms research group at the IT University of Copenhagen, had spent most of this year thinking about one single problem in his subfield. “As far as I can tell, it’s now all irrelevant,” he tells WIRED. Having first formulated a version of the hypothesis now disproved by OpenAI, he has been writing calculations on his blackboard to figure out what other parts of his work might have become obsolete due to the “once-in-a-generation result.” Unlike the ennui that has gripped math researchers who have faced similarly field-upending proofs generated by AI models, though, Husfeldt expressed enthusiasm about the new results.

哥本哈根信息技术大学算法研究组负责人托尔·胡斯费尔特（Thore Husfeldt）今年大部分时间都在思考其子领域中的一个单一问题。“据我所知，现在这一切都无关紧要了，”他告诉《连线》杂志。由于他曾率先提出过一个现已被 OpenAI 推翻的假设版本，他一直在黑板上进行计算，以弄清楚他的哪些其他研究成果可能因这一“一代人一遇的成果”而过时。然而，与那些面对人工智能生成的颠覆性证明而感到倦怠的数学研究者不同，胡斯费尔特对这些新结果表现出了热情。

The finding is “fantastic,” he says. “Being a mathematician is a lot like stumbling about in a completely dark room,” he explains. “It’s like somebody just illuminated an entire subfield in floodlights of knowledge, and now we just run around like overexcited bunnies and look at all the nooks and crannies that we can suddenly explore. So it’s a super exciting time.”

他认为这一发现“棒极了”。他解释道：“做数学家就像在完全黑暗的房间里摸索。这就像有人刚刚用知识的探照灯照亮了整个子领域，现在我们就像兴奋过头的兔子一样四处奔跑，观察那些我们突然可以探索的角角落落。所以这是一个超级令人兴奋的时刻。”

For centuries, science has advanced through making hypotheses and proving them wrong. It also isn’t unusual for an academic to be scooped or have their work otherwise undercut by another research group. But AI has accelerated this, obliterating whole research programs and spooking researchers in specific fields of math and science who wonder where they stand now that machines can outperform them.

几个世纪以来，科学通过提出假设并证明其错误而不断进步。学术界中，研究被他人抢先发表或工作被其他研究小组削弱也并不罕见。但人工智能加速了这一进程，它摧毁了整个研究项目，并让数学和科学特定领域的研究人员感到恐慌，他们开始怀疑在机器能够超越人类的今天，自己的立足点在哪里。

“The last few months have been really tough. It’s a lot of doom and gloom, because every week, or every second week, the results announced by AI just annihilate the ongoing research projects of many people,” Husfeldt says. “That is psychologically taxing for many people,” he says, adding that his department has considered hiring an occupational psychologist because “some people are really depressed and really sad.”

“过去几个月真的很艰难。到处都是悲观情绪，因为每隔一两周，人工智能宣布的结果就会摧毁许多人正在进行的研究项目，”胡斯费尔特说，“这对许多人来说在心理上是一种负担，”他补充道，他的部门甚至考虑聘请一位职业心理学家，因为“有些人真的感到非常沮丧和悲伤。”

Researchers have also been overwhelmed by the volume of content now being produced, thanks to AI, including slop-level results. Midway through the interview, Husfeldt receives what he suspects to be an AI-generated 17-page paper on the consequences of the ETH result from a random Gmail account. He won’t read it: “Reading math is a very big investment of cognitive time,” he says.

研究人员还被人工智能产生的大量内容所淹没，其中包括质量低劣的研究结果。在采访进行到一半时，胡斯费尔特收到了一封来自陌生 Gmail 账号的邮件，他怀疑这是一篇由人工智能生成的关于 ETH 结果影响的 17 页论文。他拒绝阅读：“阅读数学论文需要投入大量的认知时间，”他说。

OpenAI has directly contributed to this; earlier this month, it dumped 722 manuscripts on 400 problems on GitHub, to the annoyance of academics, who complain this makes it difficult to digest the results. Adding to the frustration were several documents that contained errors, reflecting what some mathematicians saw as a hasty rollout designed to juice the company’s planned initial public offering rather than moving the field forward. This time around, OpenAI’s 60-odd-page paper on ETH seems to be written better than the companies’ past publications, academics tell WIRED.

OpenAI 直接导致了这种情况；本月初，它在 GitHub 上倾倒了 400 个问题的 722 份手稿，这引起了学术界的反感，他们抱怨这使得消化这些结果变得困难。更令人沮丧的是，其中几份文档包含错误，一些数学家认为这是为了推动公司计划中的首次公开募股（IPO）而进行的仓促发布，而非为了推动该领域的发展。不过，学术界人士告诉《连线》，OpenAI 这次关于 ETH 的 60 多页论文似乎比该公司过去的出版物写得更好。

They are now scrambling to unpack the paper and find what other results they can quickly deduce from it.

他们现在正争分夺秒地解析这篇论文，并寻找能从中快速推导出的其他结果。

If ETH is false and it doesn’t, in fact, take exponentially more time to solve problems as they become more complex, that’s “exciting,” says Victor Lagerkvist, an associate professor at Linköping. He plans to return to problems he had previously given up on under the assumption ETH was accurate.

林雪平大学副教授维克多·拉格奎斯特（Victor Lagerkvist）表示，如果 ETH 是错误的，且解决问题所需的时间实际上并不会随着复杂性的增加而呈指数级增长，那将是“令人兴奋的”。他计划重新研究那些此前因假设 ETH 正确而放弃的问题。

OpenAI’s finding could also have impacts on the cryptography that underpins web security, which is based on the assumption that certain mathematical problems take an impractically long time to solve. While this particular result doesn’t directly affect cryptography, “it should definitely scare some cryptosystem designers,” says Lokshtanov.

OpenAI 的这一发现还可能对支撑网络安全的密码学产生影响，因为密码学的基础是某些数学问题需要极长的时间才能解决。洛克什塔诺夫表示，虽然这一特定结果并不直接影响密码学，“但它绝对应该让一些密码系统设计者感到恐慌。”

That’s why continued human and AI-assisted work in theoretical computer science is important, Husfeldt adds: “We want the open scientific process to identify our misunderstandings about computation, in particular because lots of modern digital infrastructure is built on assumptions about computation.”

这就是为什么在理论计算机科学领域持续进行人类与人工智能辅助的研究非常重要，胡斯费尔特补充道：“我们希望通过开放的科学进程来识别我们对计算的误解，特别是考虑到许多现代数字基础设施都是建立在关于计算的假设之上的。”

As for Linköping’s Jonsson, he plans to revisit both his own and others’ papers that hinge on ETH being true, and believes his conference paper may eventually be salvageable through another method. He’s excited to look into

至于林雪平大学的琼森，他计划重新审视自己和他人的那些以 ETH 为真前提的论文，并相信他的会议论文最终可以通过其他方法挽救。他很兴奋能去研究