咒語的倒塌（OM）以及為什麼人工智能（AI）可以阻止90％的令牌崩潰

在幾個小時內，該資產的市值從超過60億美元增加到約5億美元。

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）的墮落是第1層現實資產區塊鏈咒語的本地令牌，於4月13日震驚了加密貨幣市場。在幾小時內，該資產的市值從超過60億美元增加到約5億美元。

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.

在一個已經被十億美元崩潰的市場中，口頭禪的土著資產的崩潰再次證明了黑客不是該行業的唯一敵人-Crypto被疏忽大使。 Mantra背後的團隊將90％的令牌撞車事故歸咎於“強迫清算”，這只是故事的一半。

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.

隨著越來越多的數據表面，很明顯，崩潰不僅是不幸的時機或高市場波動的情況。這是一場可預防的災難，有許多催化劑，例如其自動化風險管理系統中的過度槓桿，流動性弱和各種差距。

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.

具有諷刺意味的是，在過去三年中，加密傳教士一直在讚美加密福音的技術，可以預測，標記甚至阻止這種崩潰，如果它得到了正確的實施。

AI-driven liquidity stress testing

AI驅動的流動性壓力測試

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.

傳統的金融壓力測試的問題在於，它是為穩定的，受監管的市場和常規資產（如股票和債券）而設計的，股票和債券極為罕見。另一方面，加密貨幣在不同的現實中運作，在不同的現實中，野生價格波動和突然的流動性崩潰非常普遍，並且是市場遊戲的一部分。依靠歷史模式的舊風險框架無法捕獲這些衝擊。

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驅動的壓力測試提供了動態替代方案。機器學習模型不依賴靜態歷史數據，而是適應實時條件，分析市場情感，鏈界指標和流動性模式。

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.

一種稱為基於峰度的壓力測試的新方法旨在降低極端異常損失的風險，這正是特徵是加密貨幣市場失敗的“胖尾巴”事件。這項技術可以幫助企業進行“較不可預測的高影響力”事件，例如最近的咒語和2022 Terra（Luna）崩潰。在2022年的Terra倒塌期間，傳統風險模型失敗了，因為他們沒有預料到穩定的de-peg可以在600億美元的消滅中旋轉的速度。

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.

該研究表明，旨在減少極端風險波動的投資組合通過峰度模型帶來了491％的回報，以426％的速度擊敗了更簡單的“買入和居住”方法，甚至勝過圍繞傳統夏普比率策略而建立的方法，而回報率為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.

高峰度值表明極端波動的可能性更高。在加密貨幣中，這些事件不是異常，它們是景觀的一部分。

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.

通過AI驅動的壓力測試方法，咒語暴露於稀薄的周末流動性和代幣濃度可能會提前標記，從而為利益相關者提供了在災難襲來之前採取行動的窗口。

Tracking and flagging movements with AI

使用AI跟踪和標記運動

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.

區塊鏈的透明度是其最大的優勢，但是不可能手動監視數百萬筆交易。這是AI擅長的地方。自主的AI代理可以不斷掃描鏈活動和標誌異常模式，這些模式可能表明即將來臨的市場操縱，而無需人工參與。

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.

在咒語的情況下，崩潰後分析的區塊鏈數據顯示了跡象。就在崩潰的前幾天，據報導，與激光數字有關的錢包將650萬個OM令牌轉移到另一個錢包，然後將其送往OKX，然後將其清算。 AI監測系統可以實時檢測到這些運動，向交換，監管機構和更廣泛的社區發出立即警報。

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代理可以將常規市場行為與潛在的操作區分開，因為它們不僅跟踪交易，而且還可以在錢包網絡上建立行為概況。

Predicting order book vulnerabilities

預測訂單書籍漏洞

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可以阻止咒語崩潰的最直接方法是通過複雜的訂單分析。訂單書揭示了市場的真正健康狀況，但它們的複雜性不僅需要表面級別的分析。

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.

深度學習模型，尤其是卷積神經網絡和長期的短期記憶網絡，已證明可以根據訂單數據數據提供有希望的結果。一項研究發現，時間CNN可以預測比特幣（BTC）的價格轉移，精度最高為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.

對市場深度的AI驅動分析將強調大型賣出訂單的大幅滑倒的風險，這是級聯價格崩潰的成熟條件。因此，這些模型可以通過在周末交易中識別出危險的稀薄訂單書來揭露咒語的脆弱性。

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和深度學習模型的幫助下，加密公司可以實施動態保障措施，例如由價格下降的急劇下降和流動性的結構弱點觸發的斷路器，以提高標誌或防止類似於咒語的情況。

Building a resilient crypto ecosystem with AI

使用AI構建有彈性的加密生態系統

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.

儘管區塊鏈技術有望權力下放和透明度，但如果沒有先進的AI驅動風險管理系統，它仍然很容易受到傷害，該系統可以處理數百萬筆交易並標記可疑模式。諸如Mantra和Terra之類的備受矚目的資產的崩潰證明了對這些系統的需求。

Financial institutions with crypto exposure must prioritize

具有加密貨幣的金融機構必須優先考慮