Context
Cross‑domain MEV (xMEV) refers to the economic incentives that emerge when transaction ordering across multiple blockchains—or even between blockchains and non‑blockchain systems—can be coordinated to extract value from their combined effects.
The most prominent examples today are arbitrage opportunities between centralized exchanges (CEXes) and decentralized exchanges (DEXes), and among DEXes deployed on different chains.
At present, xMEV is difficult to capture because transactions cannot be executed atomically across domains, exposing traders to execution risk. But new interoperability primitives—such as multi-chain bridges and shared sequencers—have the potential to reduce this friction.
As these technologies mature, xMEV is likely to become a commodified, system‑level incentive that could play a central role in the economics of a multi-chain future.
Open Research Questions
xMEV research space is broad, involving problems around incentives, security, and protocol design. Here we just name a few of them:
- Post‑August 2024 cross‑chain arbitrage dynamics (extending the empirical analyses in [2])
- How does the arbitrage volume evolve? Any change in market share of chains arbitraged?
- Which drivers—e.g., latency‑reduced bridges, new defi protocols—best explain the change?
- Do we observe convergence toward inventory-based execution or bridge-methods gain adoption?
- Is market centralization around the searcher 0xCA74 continuing? Any new participants? If so, what potentially affected this?
- Opportunity frequency, magnitude, and friction studies based on the theoretical model in [2]
- How often opportunities arise and how large for a given asset pair?
- How do inventory and bridging costs compare?-try to estimate using the costs derived in the model
- How does the theoretical opportunity set compare to the subset of arbitrages actually executed on‑chain?
- Can we find pnl/markouts of executed arbs? (eg focusing on a single pair) At the time of the trade, whats the state of different markets looking like?
- Detecting further xMEV practices
- Can we develop heuristics or ML classifiers to identify inventory rebalancing patterns-distinguishing them from real-time bridging for an arbitrage purpose?
- A starting point could be to focus on the transaction history of the known cross-chain arbitrageurs like 0xCA74
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- How prevalent is loan‑backed cross-chain arbitrage, and can it be fingerprinted reliably in public data? - an example is provided in [2]
- How often are they adopted? For which token pairs?
- Can we associate spamming and/or failed transactions on individual chains with e.g., cross-chain arbitrage extraction attempts?
- How can we infer that a failed transaction on any given blockchain was issued to execute a leg of a cross-chain arbitrage? - compare input data against successful ones?
- Which other strategies—e.g., cross‑chain liquidations, oracle manipulation—are happening?
- Ethereum ↔ Solana xMEV detection
- Can we identify xMEV (e.g., arbitrage, for start) activity between Ethereum and Solana, despite divergent execution and finality models?
- How to link entities? Using ML can be an option, looking at repeating patterns of actions on Ethereum and Solana
- When Bruno buys x amount of Token A on Ethereum, Burak sells x amount of Token A on Solana.
- When Bruno and Burak’s transactions are matched, they make profit from the spread.
- If we identify enough number of token A-token B profitable transaction pairs, can we link them to the same entity?
Resources
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