Testing Mosaic through our Arbitrum-Polygon Transferal System Proof of Concept
The recently announced proof of concept for the transferal system between Polygon and Arbitrum provides the first step in creating a fully functional cross-layer liquidity solution from Composable Finance — known as Mosaic. Valuing transparency and giving insight to our community, we want to provide you with the details of this proof of concept to outline how Composable is building a fully cross-layer interoperable ecosystem.
Steps in Our Arbitrum-Polygon Transferal System Proof of Concept
The infrastructure for our transferal system will have a layer 1 (L1) farming vault to facilitate liquidity provisioning across the Arbitrum and Polygon platforms. This links the two layer 2 (L2) solutions and the Ethereum mainnet within our proof of concept. Our proof of concept is designed to test and obtain more data on our dynamic fee model, which serves as the basis of the full solution that is Mosaic.
The transferal system, which goes live on Tuesday the 14th of September, will have a cap of $3 million in total value locked (TVL) for three days. These initial three open days will grant users the opportunity to provide liquidity for future cross-L2 transactions between Polygon, Arbitrum and the mainnet. During this initial staking period, WETH and USDC will be accepted.
Once this three-day period is over, a pause will be placed on liquidity provisioning into our vault. The deposited assets will be moved from our L1 pool into the vaults on Arbitrum and Polygon (i.e. our L2 vaults) by a multisig of the Composable team. After that transfer, we will launch our proof of concept (PoC).
During this proof of concept period, users will be able to make transfers between Polygon, Arbitrum and mainnet to complete cross-layer L2 swaps and fast exits from both L2s to L1.
The transfer system has an intentionally simple design for this proof of concept. In this PoC, assets are locked on their source layer. When a transfer is desired, a relayer transmits the transferal information to the destination layer. To make the transfer possible, the relayer catches the lock event and calls the release operation on the destination layer. A fail safe is put in place to allow the user to claim their locked assets on the source layer if the transfer is unsuccessful. This ensures funds are not “lost” if there are any issues with the transaction.
Fees will be monitored in real time and displayed in the user interface in USDC. However, we will also be converting these fees and distributing them in LAYR tokens.
Furthermore, the Composable team will be rebalancing the liquidity in the system so as to ensure proper liquidity distribution throughout the duration of the POC.
The purpose of this proof of concept is to complete the first phase of Mosaic’s testing, during which we will test our liquidity providing hypothesis and our dynamic fee model as we work to make passive liquidity providing for cross-layer movements automated and profitable.
Our Dynamic Fee Model
In the PoC period, we will be testing a dynamic fee ranging from 0.25% to 5%. The goal during this testing phase is to optimize liquidity provider (LP) fees as we aim to create a solution which maximizes the rewards for LPs.
The fee curve in our proof of concept is linear unless a trade size is greater than 30% of the destination layer’s available liquidity. If it surpasses that threshold, the fee becomes a flat rate of 5%. During phase one, liquidity providers will be able to monitor their earnings in real time through our platform.
Beyond that, people who swap from Arbitrum or L1 to Polygon will receive the native token of Polygon — Matic. This ensures that they will easily be able to transact upon this layer once they have transferred their other assets there.
Phase One Objectives
The hope of phase one is to obtain data from the proof of concept to incorporate into the design of Mosaic’s dynamic fee model. The information will also be instrumental in helping us design our Intelligent Liquidity Engine (ILE) for phase two. Moreover, we will be learning how we can further refine the model to maximize LPs earnings.
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