林雪平大学教授彼得·约恩松的研究成果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。截至周一，他表示，其中大部分工作已“失效”，因为人工智能显示，支撑其研究的一个27年假设是错误的。
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.
OpenAI和Anthropic本周均声称已推翻指数时间假设（ETH）。理论计算机科学中关于复杂性和算法设计的大量研究，均建立在这样一个前提之上：解决某些问题所需的努力会随问题规模呈指数级增长。
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.
约恩松预计，他的处境在IEEE计算机科学基础研讨会上将“非常普遍”，他的研究成果将由一位合著者在会上发表。“人们会开场就说：‘ETH是错的，我的论文有一半行不通，’”他说。约恩松称，这是一个令人沮丧的问题，“但与此同时，我们从中学到了很多。这正是科学的本质。”研究人员尚未独立验证，更不用说有时间完全理解，这些由人工智能生成的证明，两家公司已将其以自然语言和可机器检查的Lean格式发布在GitHub上。
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.”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已请最初提出该假设的加州大学圣巴芭拉分校计算机科学家，以及一位曾反驳过ETH版本的麻省理工学院学者，号召研究人员来拆解这些证明。迄今为止，这项工作包括一份征求志愿者和反馈的谷歌表单，以及OpenAI承诺资助一场关于该主题的会议。
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.
加州大学圣巴芭拉分校教授丹尼尔·洛克斯塔诺夫计划加入这项工作，他估计，如果这一结果得到证实，将使他“彻底错误”，并使他大约50篇已发表的论文要么“毫无意义”，要么需要重新审视。
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.
“这就像在田野中央投下了一颗巨型炸弹，”他说，“既非常酷，又非常可怕。”哥本哈根IT大学算法研究组组长托雷·胡斯费尔特（Thore Husfeldt）今年大半时间都在思考其细分领域的一个单一问题。“据我所知，现在这一切都变得无关紧要了，”他告诉《连线》（WIRED）杂志。作为最早提出现已被OpenAI证伪的假设版本的人之一，他一直在黑板上进行计算，试图弄清由于这一“千载难逢的成果”，他的工作还有哪些部分可能已变得过时。
“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.”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.”
不过，与面对AI模型生成的同样颠覆性证明而陷入厌倦感的数学研究人员不同，胡斯费尔特对新结果表现出了热情。
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.
这一发现“太棒了，”他说。“做数学家很像在完全黑暗的房间里摸索前行，”他解释道，“就像有人突然用知识的探照灯照亮了整个细分领域，现在我们就像过度兴奋的小兔子到处乱跑，查看那些突然变得可以探索的每一个角落和缝隙。所以这是一个超级令人兴奋的时刻。”几个世纪以来，科学都是通过提出假设并证伪来进步的。
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.”For centuries, science has advanced through making hypotheses and proving them wrong.
学者被其他研究组抢先发表成果，或其工作被以其他方式削弱，也并不罕见。但AI加速了这一进程，摧毁了整个研究项目，让数学和科学特定领域的研究人员感到恐慌，他们不知道自己现在处于何种地位，因为机器已能超越他们。
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.
“过去几个月真的很艰难。到处都是悲观情绪，因为每周，或者每隔一周，AI 公布的结果就会让许多人的在研项目瞬间归零，”Husfeldt 说。“这对许多人来说是心理上的巨大打击，”他补充道，他的系里甚至考虑聘请一名职业心理学家，因为“有些人真的很抑郁、很难过”。得益于 AI，研究人员现在产出的内容量激增，其中不乏低质量成果，这也让他们不堪重负。
“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.”Researchers have also been overwhelmed by the volume of content now being produced, thanks to AI, including slop-level results.
采访进行到一半时，Husfeldt 收到一份来自随机 Gmail 账号、长达 17 页、疑似由 AI 生成的论文，探讨 ETH 结果的后果。他不打算读：“读数学需要投入大量的认知时间，”他说。
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.
OpenAI 直接助推了这一局面；本月初，它在 GitHub 上一次性抛出 722 份手稿，涉及 400 个问题，引发学界不满，学者们抱怨这让人难以消化这些结果。更令人沮丧的是，其中几份文件包含错误，这在一些数学家眼中反映出这是一次仓促发布，旨在为公司计划中的首次公开募股（IPO）造势，而非推动领域发展。学界人士告诉《连线》（WIRED），这一次，OpenAI 那篇 60 多页的 ETH 论文写得比公司以往的出版物要好。
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.
他们现在正争分夺秒地拆解这篇论文，看看还能从中快速推导出哪些其他结果。
They are now scrambling to unpack the paper and find what other results they can quickly deduce from it.
“如果 ETH 不成立，解决问题所需的时间实际上并不随复杂度呈指数级增长，那将是‘令人振奋’的，”林雪平大学副教授 Victor Lagerkvist 说。他打算重新研究那些他在 ETH 成立的假设下曾放弃的问题。
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.
OpenAI 的这一发现也可能对支撑网络安全的密码学产生影响，后者建立在这样一个假设之上：某些数学问题需要极长的时间才能解决，实际上无法破解。虽然这一特定结果并不直接影响密码学，但 Lokshtanov 表示：“这绝对应该让一些密码系统设计师感到恐惧。”
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.
Husfeldt 补充道，这就是为什么持续进行人类与 AI 协助的理论计算机科学研究非常重要：“我们希望开放的科学过程能揭示我们对计算的误解，特别是因为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。”
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.”
至于林雪平大学的 Jonsson，他计划重新审视自己和他人那些建立在 ETH 为真这一前提上的论文，并认为他的会议论文最终可能通过另一种方法得以挽救。他很期待深入研究 OpenAI 证明的细节，看看能从中学到什么并应用到新问题上。尽管这推翻了他工作所依赖的假设，但能对 ETH 得出定论，是他以前只能“梦寐以求”的，他说道。“这是激动人心的时刻，”他补充道，“大多数人在职业生涯中永远不会经历这样的事情。”
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 the details of OpenAI’s proof and see what he can learn from it and apply it to new problems. Even though it undoes the assumptions underpinning his work, having a verdict for ETH is something he could only “dream of” before, he says. “These are exciting times,” he adds. “Most people would never experience anything like this in their career.”
“我不在乎这是人类撰写的证明，还是 AI 撰写的，又或者是外星人传送给我们的证明。如果它是真的，那么我们未来就必须直接使用它。”
“I don’t care if it's a human-written proof, or it's written by AI, or if an alien transmitted this proof to us. If it’s true, then we must simply use it in the future.’’