Chainfeeds Introduction:
The combination of decentralized finance and active verification services (AVSs) opens up a range of powerful new use cases, such as trustless off-chain computation, trustless off-chain data, and auction networks. This new paradigm is called Smart DeFi. EigenLayer Research has written an in-depth article exploring 10 use cases that demonstrate its potential.
Source:
https://www.techflowpost.com/article/detail_20455.html
Article author:
EigenLayer
Viewpoint:
EigenLayer: Exchanges: 1) Volume-based fee tiers: Implementing a volume-based fee system on a decentralized exchange (DEX) faces challenges. Dynamically calculating a trader's volume and tracking history adds complexity and on-chain costs, but coprocessors can solve these problems. With coprocessors, a trader's volume can be calculated off-chain, and the results are passed back to the chain using zero-knowledge proofs to ensure the credibility and security of the calculations; 2) Dynamic and asymmetric fees for AMMs: AMMs can improve the profitability of liquidity providers through dynamic and asymmetric fees. Increasing fees during high volatility protects liquidity providers, while reducing fees during low volatility stimulates trading volume. Asymmetric fees adjust the spread based on off-chain price data to be closer to the actual volatility of the market, relying on secure and reliable data sources such as zkTLS. 3) Auctions to reallocate MEV to AMM liquidity providers: Through decentralized auctions, AMMs can recapture LVR and distribute it to LPs in proportion, thereby improving profitability. Auctions require decentralized operators to conduct leaderless auctions to ensure process integrity and avoid centralized operations. Derivatives: 1) Advanced margin system: Decentralized exchanges (DEX) can customize risk engines through complex calculations of coprocessors, implement more flexible delta-neutral strategies, and dynamically adjust margin requirements in real time. This mechanism allows leverage to be adjusted according to market liquidity, improving the flexibility of margin calculation and user experience; 2) Pricing of option automated market makers (AMMs) Option AMMs need to rely on off-chain data (such as volatility, historical prices) combined with on-chain trading mechanisms to ensure accurate pricing and low slippage. These data-driven models (such as the Black-Scholes model) are the key to the success of option AMMs, improving trading efficiency and capital utilization. Lending: 1) Parameter AI system: Through artificial intelligence (AI) models, decentralized lending platforms can achieve real-time updates of parameters, automatically respond to changes in on-chain and off-chain liquidity, thereby reducing governance delays and improving the efficiency of the lending market. 2) Customized loans through account history and liquidation risk: Decentralized finance (DeFi) can customize loan conditions through users' historical data. The coprocessor assesses the user's liquidation risk and provides more favorable loan terms to low-risk users, such as lower collateral requirements and lower interest rates. 3) Compliant privacy mixers: Compliant privacy protocols can solve the regulatory challenges of current privacy mixers. Through real-time APIs, smart contracts can ensure that only compliant funds are accepted, thereby providing a legal path for privacy protection while ensuring compliance. 4) Automated rebalancing income agreements: Using AI models, income agreements can automatically adjust portfolios and optimize returns. AI models dynamically allocate between multiple asset sources to ensure that returns can cover transaction fees and provide efficient data-driven investment management. 5) Ultra-precise incentive plans: Through coprocessors, protocols can issue incentives based on complex conditions in real time, avoid unpopular point systems, improve user experience, and only reward the most valuable behaviors. This helps reduce capital costs and drive long-term growth in DeFi.
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