FAQ
Last updated
Last updated
: Founder, Architect / Entrepreneur and full-stack developer with 10 years of experience. Skilled in building several DeFi protocols. / ex. SushiSwap Core Dev
: Marketer, BD / ex. SushiSwap Marketer, Across Bridge Marketer
: Frontend Dev. / Senior frontend engineer with over 8 years of experience in web development and 3 years building DeFi and NFT project / ex. DeFi Co-founder
No. Unlike traditional bridges that custody assets, xDLOL does not rely on third-party trust assumptions. It achieves decentralized cross-chain liquidity synchronization through:
1. LayerZero’s read protocol (lzRead) for cross-chain liquidity data fetching.
2. Merkle-based liquidity proofs for data integrity across chains.
3. On-chain liquidity settlement logic, ensuring correctness without relying on external validators.
• Base liquidity sync latency: 10-12 seconds, which is LayerZero’s lzRead delay.
• Periodic syncs: Open to anyone and can technically happen every 1-2 seconds on L2s, but due to gas costs, the recommended sync interval is 30-60 seconds to balance usability and gas efficiency.
Since liquidity updates are not event-driven but state-based, the most recent settled liquidity is always fetched when queried. If a protocol requires more frequent updates, it can initiate on-demand syncs, but at the cost of gas fees.
Liquidity syncs do not happen automatically—they require external triggers (e.g., dApps, users, bots) to update liquidity states. This ensures cost efficiency while maintaining an open, decentralized synchronization model.
• MEV bots can utilize cross-chain liquidity visibility to identify arbitrage opportunities that would otherwise be hidden due to fragmented liquidity.
• However, since liquidity updates happen at fixed intervals (10-12 sec for lzRead, 30-60 sec for optimized syncs), there is less unpredictability, reducing toxic MEV extraction compared to real-time bridging.
xD ERC20 extends the OFT standard by:
• Dynamic yield accrual based on where liquidity is most efficiently deployed.
• Global balance synchronization to prevent the need for manual bridging.
• Seamless cross-chain usability, allowing tokens to be used as native assets on any chain.
Yield is not restricted to a single chain but instead follows a dynamic allocation model:
1. Identifies the highest-yielding chain at any given time.
2. Redirects a portion of assets there to optimize APY.
3. Reflects accrued yield across all chains, so balances increase globally.
Yes, but protocols must integrate with xDLOL to utilize xD ERC20’s liquidity synchronization features. Non-integrated protocols will only see local liquidity, not the aggregated cross-chain balance.
• Batching Syncs: Instead of calling syncs every transaction, aggregate liquidity updates at optimal intervals (e.g., every 30-60 sec).
• Using Local Data First: If real-time accuracy is not required, use the most recently settled liquidity instead of triggering a fresh sync.
• Asynchronous Execution: Combine cross-chain liquidity reads with local execution logic to avoid unnecessary on-chain gas expenses.
Yes. Developers can fetch total available liquidity across all chains using:
or for a specific token pair:
This enables liquidity-aware execution without requiring complex bridging mechanics.
If you’re building an AMM, lending protocol, or yield aggregator, integration involves:
• Accepting xD ERC20 as a valid liquidity source.
• Utilizing Liquidity Matrix for real-time liquidity access.
• Ensuring native execution logic doesn’t assume single-chain liquidity.
This allows protocols to tap into cross-chain liquidity without requiring separate liquidity provisioning.
No. Unlike bridges that custody assets, xDLOL:
• Does not hold user funds—liquidity remains in existing decentralized pools.
• Uses Merkle proofs to verify cross-chain state, eliminating reliance on trusted relayers.
• Relies on LayerZero’s trust-minimized security model, preventing malicious liquidity syncs.
Since liquidity syncs are open to anyone, the system is self-healing:
• If no sync occurs, liquidity simply reflects the last settled state.
• If a sync fails, it can be retried without risk of loss.
• dApps can optimize their own sync triggers for better responsiveness.