RFP 6 - Valuing Cross-Chain Bundles and MEV

Researcher: TBD


This research initiative aims to establish a model for pricing cross-domain transaction bundles in a manner that optimizes for cross-domain MEV extraction. Specifically, this necessitates understanding how cross-domain bundles and MEV are already being priced and extracted. This information will then be used to create a pricing model that is optimized by incorporating slot auctions and atomicity.

This initiative will contribute the following:

  • A greater understanding of cross-domain MEV
  • A mechanism for valuing cross-domain bundles
  • A mechanism for cross-domain MEV extraction

Background & Problem Statement


Core background concepts/definitions are as follows:

Cross-Domain MEV

A cross-domain intent settlement platform (such as that being developed by Composable) reshapes an emerging type of maximal extractible value (MEV): cross-domain MEV. As this is a relatively novel form of MEV, and MEV is still a poorly studied and reported phenomenon, a number of questions thus arise. In particular, we at Composable believe that cross-domain MEV could impact the price of intent settlement in a positive way, decreasing cost for users. In fact, this type of MEV is a positive for all levels of the supply chain.

Cross-domain MEV can be defined as the extraction of value from cross-chain transactions. This extractible value originates from two primary sources (McMenamin, 2023):

  1. Intrinsic-extractable value: expected value for an extractor at the precise time the state or transaction must be acted on (t = 0).

    • In an order, this is approximately the expected value of all front- and back-running opportunities combined.
    • In a pool, this is approximately the expected value from moving a price up or down at the time when orders are included in the chain.
  2. Time-extractable value: derived similarly to an option, this is the value derived because the extractor has the time between confirmation times/blocks to determine if they should act on a particular blockchain state.

    • For extractors, this is the sum of all paths with a positive extractable value at expiration, multiplied by the probability of that path happening.

This type of MEV has been previously mathematically defined by Obadia et al, 2021. To summarize, this research found that cross-domain MEV is the maximum of the sum of final balances across all considered domains into a single base asset (canonically the first domain considered), given there is some assortment of transactions across all those domains that are executed together. Importantly, this research also concluded that “We expect bridges to play an extremely important role in such an MEV ecosystem, as the cheaper, more ubiquitous, and faster bridges become, the more competitive these arbitrage transactions naturally become by decreasing the inequality of the action space across players as a function of their capital.”

Composable’s MANTIS:

Composable’s Multichain Agnostic Normalized Trust-minimized Intent Settlement (MANTIS) is an ecosystem-agnostic intent settlement framework. MANTIS facilitates settlement of cross-chain user intents, optimizing the supply chain to deliver upon our vision of a user-centric, ecosystem-agnostic future for DeFi.

In the Composable ecosystem specifically, cross-domain MEV is potentiated from cross-chain intent settlement. Composable’s MANTIS receives user transaction intents, which are then picked up by solvers who compete to find the best solution to execute these intents. Once the optimal solution is chosen via a scoring mechanism, the winning solver must then execute upon their proposed solution.

A single solution can involve a number of different domains. Searchers can access the orderflow from these solutions not only within each domain but also between domains by accessing the mempool:

This results in cross-domain MEV.

Problem Statement

The problem here is that cross-domain MEV, as a relatively new concept, is poorly understood and poorly quantified. Through the current research initiative, we aim to create a system for valuing cross-domain bundles that enables optimization of cross-domain MEV extraction.

Thus, the question that this research aims to address is as follows:

  • How are bundles of transactions currently being valued on different chains by builders?
  • What pricing model can be implemented to accurately value cross–chain bundles?
    • How can we optimize this pricing model, incorporating slot auctions and atomicity?
  • How can we maximize cross-domain MEV extraction in this model?
    • Is cross-domain MEV already being extracted?
    • If yes, what mechanisms are being used?
    • If yes, to what extent is this cross-domain MEV extraction occurring?

Plan & Deliverables

Expected outputs/deliverables are as follows:

  • An understanding/quantification of how bundles of transactions are valued on various chains
  • An optimized pricing model for valuing cross-chain bundles
  • A system for optimally extracting cross-domain MEV using this pricing model

The plan for achieving this output is outlined below:

Experiment 1: Determine how Ethereum bundles are handled by builders’ auctions, and how each individual builder prices these incoming bundles/swaps from the end to end relayer setup

Initially, we will most likely point the order flow towards an auction of builders to go to production. Longer-term, we need a robust model for pricing cross-domain MEV originated from Ethereum and other chains. Specifically, we want to target OFA (order flow auctions) like MEV-share or MEV-blocker, and how those auctions price these bundles.

To explore Experiment 1, we will complete mainnet testing. When our ETH <> IBC connection is ready, we will place a number of bundles with a large range of values, completing swaps over Ethereum to understand how components of bundles/swaps are currently being priced. This includes creating a valuation for swap aggregators.

Order flow auctions are currently facilitated by Flashbots Auctions. The paper produced reporting upon this phase could have a title similar to How People Price Incoming Bundles Using Existing Auctions on Ethereum.

Specific steps to implement this are:

  • Point a relayer to these auctions
  • Pull data regarding how much the relayer is making from others purchasing this order flow from it
  • Find a way to measure overall MEV and order flow associated with every transaction

Experiment 2: Assess how bundles are currently valued on chains other than Ethereum

We can do this by looking at LayerZero, Circle’s Cross-Chain Transfer Protocol (CCTP), and Squid Router (powered by Axelar), and Socket.tech. Then, we can look at how bundles on these platforms are valued when they hit Ethereum and then again what they are valued at when they leave Ethereum. We can also test this with MEV-Share. The corollary to this is examination of bridge aggregators and seeing how MEV is captured there (e.g. whether or not there is frontrunning).

The results of this experiment will be then used to develop a pricing model for cross-domain bundles.

Experiment 3: Pricing for intent settlement/cross-domain MEV as an influence on pricing of intent settlement

To complete Experiment 3, we must examine the cost of intent settlement by determining how best to price orders in the solver limit order book, which here is actually a searcher limit order book (as solvers on our infrastructure are also searchers, who come up with routes and maintain a limit order book on our Picasso Cosmos Chain, performing swaps, etc.). The paper produced reporting upon this phase could have a title similar to Pricing for Intent Settlement or Cross-Domain MEV as an Influence on Pricing of Intent Settlement.

Experiment 4: Optimizing the pricing model with slot auctions and atomicity

Experiment 4 involves figuring out how to incorporate slot auctions into the solution, and reaching an end to the optimal solution where we also gain atomicity of transactions. This should be generalized to pricing for intent settlement or cross-domain MEV as an influence on pricing of intent settlement (as determined in phase 3); we must consider how a cross-chain program and MEV recapture will be affected.


How to Participate

If you’re a researcher who believes that you would be a good fit to contribute to any of the Composable RFPs, please reach out to Composable’s Lead Research Associate, Sydney Sweck, at sydney@composable.finance. In the email, be sure to include:

  • The RFP number(s) you’d like to contribute to
  • Your relevant background experience
  • How you think you could contribute to the research