咒语的倒塌（OM）以及为什么人工智能（AI）可以阻止90％的令牌崩溃

在几个小时内，该资产的市值从超过60亿美元增加到约5亿美元。

The fall of Mantra (OM), the native token of the layer-1 real-world asset blockchain Mantra, shook the crypto market on April 13. Within hours, the asset saw its market cap plunge from over $6 billion to around $500 million.

曼特拉（OM）的堕落是第1层现实资产区块链咒语的本地令牌，于4月13日震惊了加密货币市场。在几小时内，该资产的市值从超过60亿美元增加到约5亿美元。

In a market already scarred by billion-dollar collapses, the collapse of Mantra’s native asset proved yet again that hacks aren’t the only enemy to the industry—crypto has been crippled by negligence. The team behind Mantra blamed “forced liquidations” for the 90% token crash, which is only half of the story.

在一个已经被十亿美元崩溃的市场中，口头禅的土著资产的崩溃再次证明了黑客不是该行业的唯一敌人-Crypto被疏忽大使。 Mantra背后的团队将90％的令牌撞车事故归咎于“强迫清算”，这只是故事的一半。

As more data surfaces, it’s becoming clear that the collapse wasn’t just a case of unfortunate timing or high market volatility. It was a preventable disaster that had many catalysts, like overleveraged positions, weak liquidity, and various gaps in its automated risk management systems.

随着越来越多的数据表面，很明显，崩溃不仅是不幸的时机或高市场波动的情况。这是一场可预防的灾难，有许多催化剂，例如其自动化风险管理系统中的过度杠杆，流动性弱和各种差距。

Ironically, artificial intelligence, the technology that crypto evangelists have been praising over the last three years, could have predicted, flagged, and even prevented this crash, had it been implemented properly.

具有讽刺意味的是，在过去三年中，加密传教士一直在赞美加密福音的技术，可以预测，标记甚至阻止这种崩溃，如果它得到了正确的实施。

AI-driven liquidity stress testing

AI驱动的流动性压力测试

The problem with traditional financial stress testing is that it is designed for stable, regulated markets and conventional assets like stocks and bonds, where extreme volatility is rare. Cryptocurrencies, on the other hand, operate in a different reality where wild price swings and sudden liquidity crashes are pretty common and part of the market game. Legacy risk frameworks that rely on historical patterns fail to capture these shocks.

传统的金融压力测试的问题在于，它是为稳定的，受监管的市场和常规资产（如股票和债券）而设计的，股票和债券极为罕见。另一方面，加密货币在不同的现实中运作，在不同的现实中，野生价格波动和突然的流动性崩溃非常普遍，并且是市场游戏的一部分。依靠历史模式的旧风险框架无法捕获这些冲击。

AI-driven stress testing offers a dynamic alternative. Instead of relying on static historical data, machine learning models adapt to real-time conditions, analyzing market sentiment, on-chain metrics, and liquidity patterns.

AI驱动的压力测试提供了动态替代方案。机器学习模型不依赖静态历史数据，而是适应实时条件，分析市场情感，链界指标和流动性模式。

A new method called kurtosis-based stress testing focuses on reducing the risk of extreme outlier losses, precisely the “fat tail” events that characterize crypto market failures. This technique can help firms in “less predictable, high-impact” events like the recent Mantra and the 2022 Terra (LUNA) crashes. During the Terra collapse in 2022, traditional risk models failed because they didn’t anticipate how quickly a stablecoin de-peg could spiral into a $60 billion wipeout.

一种称为基于峰度的压力测试的新方法旨在降低极端异常损失的风险，这正是特征是加密货币市场失败的“胖尾巴”事件。这项技术可以帮助企业进行“较不可预测的高影响力”事件，例如最近的咒语和2022 Terra（Luna）崩溃。在2022年的Terra倒塌期间，传统风险模型失败了，因为他们没有预料到稳定的de-peg可以在600亿美元的消灭中旋转的速度。

The research shows that portfolios designed to reduce extreme risk swings delivered a 491% return with the kurtosis model, beating the simpler ‘buy-and-hold’ approach at 426% and even outperforming those built around traditional Sharpe ratio strategies, with a 384% return.

该研究表明，旨在减少极端风险波动的投资组合通过峰度模型带来了491％的回报，以426％的速度击败了更简单的“买入和居住”方法，甚至胜过围绕传统夏普比率策略而建立的方法，而回报率为384％。

A high kurtosis value indicates a higher probability of extreme volatility. In crypto, these events aren’t anomalies—they’re part of the landscape.

高峰度值表明极端波动的可能性更高。在加密货币中，这些事件不是异常，它们是景观的一部分。

Mantra’s exposure to thin weekend liquidity and token concentration could have been flagged well in advance with AI-powered stress testing methods, offering stakeholders a window to act before catastrophe struck.

通过AI驱动的压力测试方法，咒语暴露于稀薄的周末流动性和代币浓度可能会提前标记，从而为利益相关者提供了在灾难袭来之前采取行动的窗口。

Tracking and flagging movements with AI

使用AI跟踪和标记运动

Blockchain’s transparency is its greatest strength, yet monitoring millions of transactions manually is impossible. This is where AI excels. Autonomous AI agents can continuously scan on-chain activity and flag unusual patterns that might indicate impending market manipulation, all without the need for human involvement.

区块链的透明度是其最大的优势，但是不可能手动监视数百万笔交易。这是AI擅长的地方。自主的AI代理可以不断扫描链活动和标志异常模式，这些模式可能表明即将来临的市场操纵，而无需人工参与。

In Mantra’s case, blockchain data analyzed after the crash revealed telling signs. Just days before the collapse, a wallet linked to Laser Digital reportedly transferred 6.5 million OM tokens to another wallet, which then sent them to OKX, where they were liquidated. An AI monitoring system could have detected these movements in real time, issuing immediate alerts to exchanges, regulators, and the broader community.

在咒语的情况下，崩溃后分析的区块链数据显示了迹象。就在崩溃的前几天，据报道，与激光数字有关的钱包将650万个OM令牌转移到另一个钱包，然后将它们送往OKX，然后将其清算。 AI监测系统可以实时检测到这些运动，向交换，监管机构和更广泛的社区发出立即警报。

AI agents can distinguish routine market behavior from potential manipulations since they don’t just track transactions but also build behavioral profiles across wallet networks.

AI代理可以将常规市场行为与潜在的操作区分开，因为它们不仅跟踪交易，而且还可以在钱包网络上建立行为概况。

Predicting order book vulnerabilities

预测订单书籍漏洞

Perhaps the most direct way AI could have prevented the Mantra crash is through sophisticated order book analysis. Order books reveal the true health of a market, but their complexity demands more than just surface-level analysis.

AI可以阻止咒语崩溃的最直接方法是通过复杂的订单分析。订单书揭示了市场的真正健康状况，但它们的复杂性不仅需要表面级别的分析。

Deep learning models, particularly Convolutional Neural Networks and Long Short-Term Memory networks, have proven to deliver promising results in forecasting price movements based on order book data. One study found that temporal CNNs can predict Bitcoin (BTC) price shifts with up to 76% accuracy.

深度学习模型，尤其是卷积神经网络和长期的短期记忆网络，已证明可以根据订单数据数据提供有希望的结果。一项研究发现，时间CNN可以预测比特币（BTC）的价格转移，精度最高为76％。

AI-driven analysis of market depth would have highlighted the risk of significant slippage from large sell orders—conditions ripe for a cascading price collapse. Consequently, these models could have exposed Mantra’s fragility by identifying dangerously thin order books during weekend trading hours.

对市场深度的AI驱动分析将强调大型卖出订单的大幅滑倒的风险，这是级联价格崩溃的成熟条件。因此，这些模型可以通过在周末交易中识别出危险的稀薄订单书来揭露咒语的脆弱性。

With the help of AI and deep learning models, crypto firms can implement dynamic safeguards like circuit breakers triggered by sharp price drops and structural weaknesses in liquidity to flag or prevent situations similar to Mantra.

在AI和深度学习模型的帮助下，加密公司可以实施动态保障措施，例如由价格下降的急剧下降和流动性的结构弱点触发的断路器，以提高标志或防止类似于咒语的情况。

Building a resilient crypto ecosystem with AI

使用AI构建有弹性的加密生态系统

While blockchain technology promises decentralization and transparency, it remains vulnerable without advanced AI-powered risk management systems that can process millions of transactions and flag suspicious patterns. The collapse of high-profile assets like Mantra and Terra has proven the need for these systems.

尽管区块链技术有望权力下放和透明度，但如果没有先进的AI驱动风险管理系统，它仍然很容易受到伤害，该系统可以处理数百万笔交易并标记可疑模式。诸如Mantra和Terra之类的备受瞩目的资产的崩溃证明了对这些系统的需求。

Financial institutions with crypto exposure must prioritize

具有加密货币的金融机构必须优先考虑