YesNoError：AI能否审阅科学论文并推动DeSci行业的链上牛市？

明天，期待已久的$BIO将正式上线。作为币安支持的DeSci板块项目，市场猜测$BIO是否会上线

Tomorrow will see the long-awaited launch of $BIO. As a DeSci sector project supported by Binance, the market is speculating whether the launch of $BIO will drive the DeSci sector's on-chain bull market and take away some of the liquidity of the AI sector.

明天我们将看到期待已久的 $BIO 的发布。作为币安支持的DeSci板块项目，市场猜测$BIO的推出是否会带动DeSci板块的链上牛市，并带走AI板块的部分流动性。

But are AI and Decsi necessarily in competition? No. YesNoError, a Solana on-chain project that has been widely discussed recently, has taken a path of integrating DeSci with AI, using AI technology to review and discover errors in scientific research papers.

但 AI 和 Decsi 一定是竞争吗？不是的。最近被广泛讨论的 Solana 链上项目 YesNoError 走了一条 DeSci 与 AI 结合的道路，利用 AI 技术来审查和发现科研论文中的错误。

Its token $YNE quickly reached a market value of 60 million US dollars on the day of its launch on December 20, and was subsequently repeatedly promoted by Andrew Kang (hereinafter referred to as AK), a well-known Twitter KOL. Its current market value is around 50 million US dollars.

其代币$YNE在12月20日上线当天迅速达到6000万美元市值，并随后得到知名Twitter KOL Andrew Kang（以下简称AK）的多次宣传。目前其市值约为5000万美元。

Is it really necessary for AI to review scientific papers?

人工智能真的有必要评审科学论文吗？

If you don’t understand the usefulness of YesNoError, here’s an illustrative tweet from Ben Parr, a member of the YesNoError team, that illustrates the need to review misinformation in scientific papers:

如果您不明白 YesNoError 的用处，这里有来自 YesNoError 团队成员 Ben Parr 的一条说明性推文，它说明了审查科学论文中的错误信息的必要性：

In October 2024, a research paper claimed that black plastic kitchenware contained toxins, and the news quickly spread in the media. The Atlantic Monthly even published an article titled "Throw away your black plastic kitchenware", causing public panic. Even Ben Parr himself began to clean up his kitchenware. However, Joe Schwartz, director of the Office of Science and Society at McGill University, discovered a major mathematical error in the study - a simple multiplication error caused the reported toxicity level to be 10 times higher than the actual level. This case shows that even seemingly authoritative research can have major errors, and these errors often have a substantial impact on the lives of ordinary people.

2024年10月，一篇研究论文声称黑色塑料厨具含有毒素，这一消息很快在媒体上传播开来。 《大西洋月刊》甚至发表了一篇题为《扔掉你的黑色塑料厨具》的文章，引发公众恐慌。甚至本·帕尔本人也开始清理他的厨具。然而，麦吉尔大学科学与社会办公室主任乔·施瓦茨发现了研究中的一个重大数学错误——一个简单的乘法错误导致报告的毒性水平比实际水平高出10倍。这个案例表明，即使是看似权威的研究也可能存在重大错误，而这些错误往往对普通人的生活产生实质性影响。

If AI technology is used to review research papers, these low-level errors in numerical calculations can be avoided to the greatest extent. YesNoError was born based on this demand.

如果利用人工智能技术来审阅研究论文，就可以最大程度地避免这些数值计算中的低级错误。 YesNoError就是基于这样的需求而诞生的。

YesNoError was founded by Matt Schlicht and uses OpenAI’s o1 model as its technical foundation. The project works very directly: the team uses AI to review research papers and then publicly publishes the issues they find on their website yesnoerror.com and official Twitter.

YesNoError由Matt Schlicht创立，使用OpenAI的o1模型作为其技术基础。该项目的工作方式非常直接：团队使用人工智能来审查研究论文，然后在其网站 yesnoerror.com 和官方 Twitter 上公开发布他们发现的问题。

This transparent operation allows the scientific community and the public to be informed of possible problems in important research in a timely manner. Although the project has only just started, it has already achieved some significant results and discovered errors in several studies.

这种透明的运作使得科学界和公众能够及时获知重要研究中可能出现的问题。尽管该项目才刚刚开始，但已经取得了一些重大成果，并发现了多项研究中的错误。

The token $YNE is also given practical use cases. Holders can spend $YNE to use YesNoError AI to give priority review to their papers.

代币 $YNE 还给出了实际用例。持有者可以花费 $YNE 使用 YesNoError AI 对其论文进行优先审查。

So far, YesNoError AI has reviewed 2,219 papers and has indeed found errors in quite a few papers.

到目前为止，YesNoError AI 已审阅了 2219 篇论文，确实发现了相当多论文中的错误。

Approval or doubt, some voices in the market

认可还是质疑，市场的一些声音

AK is optimistic and posted a lot of praise

AK很乐观，发了很多赞

On the day when the $YNE token was launched, AK, who has always been optimistic about DeSci, expressed his appreciation for the YesNoError project.

在$YNE代币上线当天，一直看好DeSci的AK表达了对YesNoError项目的赞赏。

AK said, "The core value of YesNoError lies in the real implementation of cryptocurrency x AI x DeSci."

AK表示，“YesNoError的核心价值在于加密货币x AI x DeSci的真正实现。”

YesNoError takes advantage of the characteristics of the cryptocurrency ecosystem. In this special environment, capital does not need a return on investment in the traditional sense. As long as you can attract enough attention, you can get sufficient financial support. (That is, the attention economy, if someone pays attention, someone will buy the token.)

YesNoError利用了加密货币生态系统的特点。在这种特殊的环境下，资本并不需要传统意义上的投资回报。只要能引起足够的关注，就能获得足够的资金支持。 （就是注意力经济，有人关注，就会有人买代币。）

At the same time, YesNoError has also found a good application direction for cryptocurrencies. In the right scenario, tokens are no longer pure air, but can actually support public products that are difficult to maintain with traditional business models.

同时，YesNoError也为加密货币找到了一个很好的应用方向。在合适的场景下，代币不再是纯粹的空气，而是可以真正支撑传统商业模式难以维护的公共产品。

Perhaps because he is really optimistic about it (or he holds a lot of positions?), on December 31, AK published another article to introduce and praise the necessity and practicality of YesNoError from a data perspective.

或许是因为他确实看好它（或者他持有很多职位？），12月31日，AK又发表了一篇文章，从数据角度介绍和赞扬了YesNoError的必要性和实用性。

AK said that YesNoError has the ability to review errors in more than 90 million papers in the global scientific literature library, which can be completed in just a few weeks or months. If converted to manual review, it would take tens of thousands of years. Even if a team of 5,000 PhDs is formed, it will take nearly ten years (and it will not be able to keep up with the speed of new papers being published during this decade), and it is conservatively estimated to cost $5.4 billion.

AK表示，YesNoError有能力审查全球科学文献库中超过9000万篇论文的错误，只需几周或几个月即可完成。如果换成人工审核，则需要数万年的时间。即使组建一个由5000名博士组成的团队，也需要近十年的时间（而且无法跟上这十年间新论文发表的速度），保守估计耗资54亿美元。

The optimized AI model only costs about $30 million ($0.3 per paper) to complete a more accurate and standardized review work - the cost is less than 1% of the manual method.

优化后的AI模型仅花费约3000万美元（每篇论文0.3美元）即可完成更准确、标准化的审稿工作——成本不到人工方法的1%。

If it is in the traditional scientific field, raising $30 million is also a big project, but it is

如果是在传统科学领域，筹集3000万美元也是一个大工程，但