探索再质押:Symbiotic、Karak、EigenLayer纵览

Odaily星球日报2024-07-10 tarihinde yayınlandı2024-07-10 tarihinde güncellendi

Özet

总体而言,AVS和再质押技术消除了构建底层信任网络的负担。

原文作者:@poopmandefi

原文编译:Alex Liu,Foresight News

如果你对 Restaking(再质押)或 AVS(主动验证服务)感兴趣,本文将对 @eigenlayer@symbioticfi@Karak_Network 进行简单比较,并介绍相关概念,应该能帮到你。

探索再质押:Symbiotic、Karak、EigenLayer纵览

什么是 AVS 和再质押?

AVS 代表主动验证服务,该术语基本上描述了任何需要自己的验证系统的网络(例如,预言机、DA、跨链桥等)。

在本文中,AVS 可以理解为使用 re-stake 服务的项目。

探索再质押:Symbiotic、Karak、EigenLayer纵览

从概念上讲,再质押是一种「重新使用」已被质押的 ETH 进行额外验证 / 服务以获得更多质押奖励,而无需取消质押的方法。

再质押通常有两种形式:

  • 原生再质押

  • LST / ERC 20 / LP 再质押

探索再质押:Symbiotic、Karak、EigenLayer纵览

通过再质押,再质押者和验证者可以通过汇聚安全性来保护数千个新服务。

这有助于降低成本并帮助新的信任网络获取起步所需的安全保证。

在这些再质押协议中, @eigenlayer (EL)是最先推出的。

探索再质押:Symbiotic、Karak、EigenLayer纵览

EigenLayer

关键架构

从高层次来看, @eigenlayer (EL) 主要由 4 个部分组成:

  • 质押者

  • 运营商

  • AVS 合约(例如代币池、指定的罚没者)

  • 核心合约(例如,委托管理者、罚没管理者)

  • 探索再质押:Symbiotic、Karak、EigenLayer纵览

这些各方共同努力,允许质押者委托资产和验证者在 EigenLayer 中注册为运营商。

EL 上的 AVS 还可以自定义自己系统的法定人数和罚没条件。

再质押

EL 支持原生再质押和流动性再质押。

在其约 150 亿美元的 TVL (总锁仓价值)中:

  • 68% 的资产是原生 ETH

  • 32% 为 LST(Liquid Staking Token,流动性质押代币)。

EL 拥有约 16 万再质押者,但只有约 1500 运营商, 67.6% ( 约 103 亿美元 ) 的资产被委托给运营商 。

探索再质押:Symbiotic、Karak、EigenLayer纵览

EigenLayer 上的 AVS

EL 为 AVS 的自我设计提供了高度灵活性,它们可以决定:

  • 质押者的法定人数(例如, 70% ETH 质押者 + 30% AVS 代币 质押者)

  • 罚没条件

  • 费用模式(以 AVS 代币 / ETH 等方式支付)

  • 运营商要求

以及它们自身的 AVS 合约 ......

探索再质押:Symbiotic、Karak、EigenLayer纵览

EigenLayer 的角色?

EL 控制着

  • 委托管理者

  • 策略管理者

  • 罚没者管理者

希望成为 EL 运营者的验证者必须通过 EL 注册。

策略管理者负责再质押参与者的余额核算,并与委托管理者合作执行。

探索再质押:Symbiotic、Karak、EigenLayer纵览

罚没

每个 AVS 都有自己的罚没条件。

如果运营商有恶意行为或违反了 EL 的承诺(commitment),那它们将被罚没者罚没,每个罚没者都有自己的罚没逻辑。

如果运营商选择参与 2 个 AVS,则它们必须同时同意两个 AVS 的罚没条件。

探索再质押:Symbiotic、Karak、EigenLayer纵览

否决罚没委员会 (VSC)

在「错误罚没」的情况下,EL 有一个 VSC 可以逆转罚没结果。

EL 本身并不充当标准委员会,而是允许 AVS 和利益相关者建立自己喜欢的 VSC,从而为针对不同解决方案量身定制的 VSC 创建市场。

总结

简而言之,EL 提供:

  • 原生 + LST 质押

  • 资产委托(ETH 资产 + EIGEN )

  • AVS 可以高度灵活地设计自己的条款

  • 否决罚没委员会 (VSC)

  • 已上线的运营商(截止目前约 1500 个)

  • 探索再质押:Symbiotic、Karak、EigenLayer纵览

Symbiotic

@symbioticfi 通过支持 ENA 和 sUSDe 等资产的质押,将自己定位为再质押的「DeFi 中心」。

目前,其 TVL 的 74.3% 是 wstETH, 5.45% 是 sUSDe,其余由各种 LST 组成。

当下还没有原生再质押上线,但可能很快就会支持。

探索再质押:Symbiotic、Karak、EigenLayer纵览

Symbiotic ERC 20 

与 EL 不同, @symbioticfi 铸造相应的 ERC 20 代币来代表存款。

一旦质押品存入,资产就会被发送到「金库」,然后将其委托给相应的「运营商」。

探索再质押:Symbiotic、Karak、EigenLayer纵览

Symbiotic 上的 AVS

在 Symbiotic 中,AVS 合约 / 代币池被称为「Vaults」。

Vault 是 AVS 建立的合约,AVS 使用 Vault 进行记账、委托设计等。

AVS 可以通过插入外部合约来定制质押者和运营商奖励流程。

探索再质押:Symbiotic、Karak、EigenLayer纵览

Vault

与 EL 类似,Vault 可以被定制,例如可以有多运营商的 Vault 等。

Vault 与 EL 的一个显着区别是存在不可变的预配置金库,这些金库使用预先配置好的规则进行部署,以「锁定」设置并避免可升级合约的风险。

解析器

解析器约等于 EL 的否决委员会。

当发生错误的罚没时,解析器可以否决削减。

@symbioticfi 中,金库可以请求多个解析器来覆盖质押资产或者与争议解决方案(例如 @UMAprotocol )集成。

探索再质押:Symbiotic、Karak、EigenLayer纵览

总结

简而言之,Symbotic 提供:

  • 接受 LST + ERC 20 + 稳定币抵押品

  • ERC 20 铸造时收到收据代币

  • 尚无原生再质押,也无委托

  • 能自定义条款的 Vault

  • 具有更高设计灵活性的多解析器架构

  • 探索再质押:Symbiotic、Karak、EigenLayer纵览

Karak

Karak 使用一种称为 DSS 的系统,类似于 AVS。

在所有再质押协议中, @Karak_Network 接受最多样化的质押资产,包括 LST、stable、ERC 20 甚至 LP 代币。

质押资产可以通过 ARB、Mantle、BSC 等多个链存入。

探索再质押:Symbiotic、Karak、EigenLayer纵览

质押资产

在 Karak 约 8 亿美元的 TVL 中,大部分存款处于 LST 状态,且其中大部分在 ETH 链。

同时,约 7% 的资产通过 K 2 存放,K 2 是 Karak 团队开发的 L2 链,并由 DSS 提供担保。

探索再质押:Symbiotic、Karak、EigenLayer纵览

Karak 上的 DSS

到目前为止,Karak V1 为这些参与者提供平台:

保险库 + 监管者

资产委托监管者

架构方面,karak 提供了 Turnkey 式的 SDK + K 2 沙箱,让开发变得更加简单。

还需要更多信息来进行进一步分析。

探索再质押:Symbiotic、Karak、EigenLayer纵览

对比

直观上,质押资产是最明显的差异因素。

  • Eigenlayer

EL 提供原生 ETH 再质押和 EigenPods,其获得的 ETH 占它 TVL 的 68% ,并已成功吸引了约 1500  个运营商。

它们也很快接受 LST 和 ERC 20 代币。

探索再质押:Symbiotic、Karak、EigenLayer纵览

  • Symbiotic

通过与 @ethena_labs 合作成为「DeFi 中心」,并首先接受 sUSDe 和 ENA 。

  • Karak

因其多链质押存款而脱颖而出,允许跨不同链进行再质押,并在此基础上创建 LRT 经济。

在架构方面,它们也非常相似。

流程通常是从利益相关者 -> 核心合约 -> 委托 -> 运营商等。

只是 Symbiotic 允许多仲裁解析器,而 Eigenlayer 没有指定这一点,但这也是可能的。

探索再质押:Symbiotic、Karak、EigenLayer纵览

奖励制度

在 EL 中,选择加入的运营商从 AVS 服务中获得 10% 的佣金,其余部分则用于委托资产。

另一方面,Symbiotic 和 Karak 可能会提供灵活的选择,允许 AVS 设计自己的支付结构。

罚没

AVS / DSS 非常灵活,它们可以定制罚没条件、运营商要求、质押者法定人数等。

EL + Sym 有解析器 + 否决委员会来支持和恢复错误罚没行为。

而 Karak 尚未公布相关机制。

最后,代币

到目前为止,只有 EL 推出了代币 EIGEN ,并要求质押者将代币委托给与再质押相同运营商(但它们是不可转让的 )。

对 SYM 和 KARAK 的猜测是推动也是推动它们 TVL 的关键激励因素。

结论

在这几个协议中,显然 @eigenlayer 提供了更成熟的解决方案,以及最强大的经济安全 + 生态系统。

想要在起步阶段获取安全性的 AVS 将建立在 EL 的基础上,因为它有一个 150 亿美元的资金池,并有 1500 名运营商准备加入 + 一流的团队。

另一方面, @symbioticfi@Karak_Network 仍处于非常早期的阶段,仍有很大的发展空间。寻求 ETH 之外 / 多链资产收益机会的散户或投资者可能会选择 Karak 和 Symbiotic。

结语

总体而言,AVS 和再质押技术消除了构建底层信任网络的负担。

现在,项目可以专注于开发新功能以及更好的去中心化。

再质押不仅仅是一种创新,更是 ETH 的新时代。

原文链接

İlgili Okumalar

South Korean Institutions' Crypto Race: Dual Explosion of Stablecoins and RWA

**Summary: South Korea's Institutional Crypto Race: Stablecoins and RWA Take Off** South Korea is undergoing a structural shift in its crypto ecosystem, moving beyond its historical role as a major retail trading hub. Major financial institutions and internet platforms are now building institutional-grade blockchain infrastructure, with stablecoins and Real-World Asset (RWA) tokenization as the primary drivers. The push for a regulated Korean won stablecoin market is a major policy and corporate focus. This is driven partly by an estimated $115 billion outflow into dollar stablecoins like USDC, threatening the domestic financial system. Banks (e.g., KB Financial, Hana), payment giants (e.g., Shinhan Card, BC Card), and internet super-apps (KakaoPay, NAVER Pay) are all conducting pilots. The goal is to anchor future digital finance to the Korean won and local regulations. In RWA, South Korea is advancing rapidly within regulatory sandboxes, focusing on unique domestic assets beyond typical global templates like US Treasuries. Projects involve tokenizing ships (with Hyundai Heavy Industries), defense supply chain assets, and K-pop intellectual property, alongside more conventional assets. A legal framework is set for 2027, and platforms like NXT are preparing for regulated trading. Key opportunities for crypto-native projects lie in providing the underlying technology these traditional institutions lack: global distribution channels for tokenized assets, cross-chain liquidity solutions, and enabling infrastructure tools (e.g., for asset packaging and management). Partnerships, such as Solana with Shinhan Card or LayerZero with the Korea Gold Exchange, exemplify this proactive approach. Crucially, user access is being shaped by consumer platforms. NAVER's planned acquisition of Upbit's operator Dunamu and Kakao's development of a unified wallet aim to seamlessly integrate crypto with everyday payments for tens of millions of users. The race is now about which protocols and projects will become the foundational standards as regulation solidifies and institutional adoption accelerates.

Foresight News7 dk önce

South Korean Institutions' Crypto Race: Dual Explosion of Stablecoins and RWA

Foresight News7 dk önce

How to Detect AI-Generated Videos? A Review of Dynamic, Traceable, and Explainable Detection Systems

**How to Detect AI-Generated Videos: A Survey on Dynamic, Traceable, and Explainable Detection Systems** With rapid advances in AI video generation (e.g., Sora, Veo), creating highly realistic, multi-minute videos is now possible, widening the gap with detection research. Current AI video detection, often limited to unreliable binary classifications, is insufficient. This survey, accepted at ACL 2026, reframes the goal as **"factual fidelity verification"**—checking if a video's content (who, when, where, what) aligns with the real world perceptually and cognitively. It categorizes AI-generated videos into three paradigms: **Local Manipulation Videos (LMV**, e.g., face swaps), **Audio-Visual Editing (AVE**, e.g., lip-syncing), and **Generative Video Synthesis (GVS**, fully synthetic videos like Sora's). Detection challenges evolve from visual artifacts in LMV to multi-modal inconsistencies in AVE and higher-level world knowledge violations in GVS. The core proposal is a **Vision-Language Dual-View framework** with four hierarchical layers: 1. **Layer 1 (Intrinsic Visual Cues):** Analyzes low-level signal statistics, noise patterns, and physiological signals. 2. **Layer 2 (Spatiotemporal Consistency):** Checks for temporal coherence in object motion and scene dynamics. 3. **Layer 3 (Cross-Modal Consistency):** Verifies alignment between video, audio, and text within the video. 4. **Layer 4 (Language-Guided World-Level Reasoning):** Uses external knowledge, facts, and physical laws to judge semantic plausibility and factual correctness. The survey traces a shift in detection focus from lower layers (1 & 2) toward higher, language-involved layers (3 & 4). It also reviews evolving evaluation metrics and datasets tailored for each video paradigm. The conclusion advocates for a **dynamic, evidence-first detection system** that moves beyond simple classification. Future trustworthy detection requires combining visual evidence (from CV) with semantic reasoning and explanation (from NLP & multimodal AI), ultimately creating traceable and explainable judgments about a video's adherence to real-world constraints.

marsbit43 dk önce

How to Detect AI-Generated Videos? A Review of Dynamic, Traceable, and Explainable Detection Systems

marsbit43 dk önce

It Turns Out the First Real-World Application of AI x Crypto is in Security Auditing

The article explores the surprising trend where AI's first major impact on crypto has been in security auditing, not in areas like trading or analytics. It details how AI-powered tools are dramatically lowering the barrier to finding smart contract vulnerabilities, enabling attackers to scan thousands of contracts and execute exploits within minutes. This has rendered traditional, manually-produced audit reports with their month-long validity periods increasingly obsolete, creating a critical "structural crack" in the old security model. Cases like Drift Protocol and KelpDAO show that even extensively audited protocols can be hacked through social engineering, operational flaws, or infrastructure misconfigurations beyond pure code review. Attackers are also using AI to find and exploit vulnerabilities in years-old, deployed contracts. Notably, OpenZeppelin's co-founder has expressed a grim view that "all DeFi is insecure" due to AI's asymmetric advantage. In response, the audit industry is undergoing a fundamental shift. While there's a short-term spike in defensive re-audits, the long-term business model is changing. Firms are developing AI-assisted systems and moving from one-time report deliveries towards embedded, continuous services like real-time monitoring and formal verification. Examples include AI tools uncovering critical, previously missed vulnerabilities in heavily audited protocols like Curve Finance and Zcash. The conclusion is that security must become a continuous investment, not a one-time checkbox, and audit firms must rapidly evolve their tools and service models to survive.

marsbit49 dk önce

It Turns Out the First Real-World Application of AI x Crypto is in Security Auditing

marsbit49 dk önce

Never expected that the first tangible application of AI x Crypto is in security auditing

Unexpectedly, the initial major application of AI in the Crypto sphere has turned out to be security auditing. In 2026, DeFi has faced significant security challenges, with 121 hacking incidents resulting in approximately $942 million in losses. While AI was expected to first impact areas like quantitative trading, its initial breakthrough has instead transformed security auditing by drastically lowering the cost and skill barrier for finding smart contract vulnerabilities. The traditional audit model is facing obsolescence. Advanced AI models, such as Claude Mythos, enable attackers to scan thousands of contracts and identify vulnerability patterns at scale, compressing the time from discovery to execution to mere minutes. This renders the month-long validity of traditional audit reports ineffective. Notably, attacks now frequently target well-audited, established protocols by exploiting business logic flaws, operational security weaknesses, and even years-old historical contracts, demonstrating that old audit reports offer zero protection. This pressure is forcing a fundamental shift in the industry. In the short term, a wave of defensive re-auditing is occurring, driven by projects seeking to meet new AI-era security standards and regulatory requirements. In the long run, audit firms' business models are diverging. The one-time report delivery model is declining in value, as evidenced by platforms like Code4rena shutting down. Leading firms are now pivoting towards AI-powered defense, integrating continuous monitoring, real-time on-chain risk detection, and embedding security directly into the development phase, as seen with tools like OpenZeppelin's Skills system. Ultimately, the era of "audit once, secure forever" is over. Security must become a continuous, embedded infrastructure investment for projects. For audit companies, survival depends on proactively transforming from traditional service providers into platforms offering AI-native, ongoing security solutions.

链捕手57 dk önce

Never expected that the first tangible application of AI x Crypto is in security auditing

链捕手57 dk önce

İşlemler

Spot
活动图片