YesNoError：AI能否審查科學論文並推動DeSci產業的鏈上牛市？

明天，期待已久的$BIO將正式上線。作為幣安支持的DeSci板塊項目，市場猜測$BIO是否會上線

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.

明天我們將看到期待已久的 $BIO 的發布。作為幣安支持的DeSci板塊項目，市場猜測$BIO的推出是否會帶動DeSci板塊的鏈上牛市，並帶走AI板塊的部分流動性。

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 一定是競爭嗎？不是的。

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.

其代幣$YNE在12月20日上線當天迅速達到6000萬美元市值，並隨後得到知名Twitter KOL Andrew Kang（以下簡稱AK）的多次宣傳。目前其市值約5000萬美元。

Is it really necessary for AI to review scientific papers?

人工智慧真的有必要評審科學論文嗎？

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 的用處，這裡有來自 YesNoError 團隊成員 Ben Parr 的一條說明性推文，它說明了審查科學論文中的錯誤訊息的必要性：

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，一篇研究論文聲稱黑色塑膠廚具含有毒素，這項消息很快就在媒體上傳開。 《大西洋月刊》甚至發表了一篇題為《扔掉你的黑色塑膠廚具》的文章，引發公眾恐慌。甚至連本·帕爾本人也開始清理他的廚具。然而，麥吉爾大學科學與社會辦公室主任喬·施瓦茨發現了研究中的一個重大數學錯誤——一個簡單的乘法錯誤導致報告的毒性水平比實際水平高出10倍。這個案例表明，即使是看似權威的研究也可能存在重大錯誤，而這些錯誤往往對普通人的生活產生實質影響。

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.

如果利用人工智慧技術來審查研究論文，就可以最大程度地避免這些數值計算中的低階錯誤。 YesNoError就是基於這樣的需求而誕生的。

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創立，使用OpenAI的o1模型作為其技術基礎。這個專案的工作方式非常直接：團隊使用人工智慧來審查研究論文，然後在其網站 yesnoerror.com 和官方 Twitter 上公開發布他們發現的問題。

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.

這種透明的運作使得科學界和公眾能夠及時獲知重要研究中可能出現的問題。儘管該計畫才剛開始，但已經取得了一些重大成果，並發現了多項研究中的錯誤。

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

代幣 $YNE 也給出了實際用例。持有者可以花費 $YNE 使用 YesNoError AI 對其論文進行優先審查。

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

到目前為止，YesNoError AI 已審查了 2219 篇論文，確實發現了相當多論文中的錯誤。

Approval or doubt, some voices in the market

認可還是質疑，市場的一些聲音

AK is optimistic and posted a lot of praise

AK很樂觀，發了很多讚

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代幣上線當天，一直看好DeSci的AK表達了對YesNoError計畫的讚賞。

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

AK表示，“YesNoError的核心價值在於加密貨幣x AI x DeSci的真正實現。”

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利用了加密貨幣生態系統的特徵。在這種特殊的環境下，資本並不需要傳統意義上的投資報酬。只要能引起足夠的關注，就能獲得足夠的資金支持。 （就是注意力經濟，有人關注，就會有人買代幣。）

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.

同時，YesNoError也為加密貨幣找到了一個很好的應用方向。在適當的場景下，代幣不再是純粹的空氣，而是可以真正支撐傳統商業模式難以維護的公共產品。

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.

或許是因為他確實看好它（或者他持有很多職位？），12月31日，AK又發表了一篇文章，從數據角度介紹和讚揚了YesNoError的必要性和實用性。

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有能力審查全球科學文獻庫中超過9,000萬篇論文的錯誤，只需幾週或幾個月即可完成。如果換成人工審核，則需要數萬年的時間。即使組成一個由5,000名博士組成的團隊，也需要近十年的時間（而且無法跟上這十年間新論文發表的速度），保守估計耗資54億美元。

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.

優化後的AI模型僅花費約3000萬美元（每篇論文0.3美元）即可完成更準確、標準化的審查工作——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

如果是在傳統科學領域，籌集3000萬美元也是一個大工程，但