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We examine the current state of the cross-domain world, with particular focus on the protocols being used/planned for use by multi-domain users. We build on existing frameworks for analyzing how MEV is extracted, while also adding a new categorization of intrinsic-extractable value vs. time-extractable value to describe how MEV is generated for an extractor. Together, this provides us with a framework with which we compare classes of protocols enabling cross-domain MEV, and the MEV occurring within these classes. We analyze each protocol class separately and compare current implementations to an ideal functionality for each. We primarily focus on analyzing the MEV mitigations that these protocols provide, both now, and into the future. In each case, we also outline the technical barriers that current protocol implementations face. With this methodology, we identify sequencers and order-flow auctions as cross-domain protocols with the greatest potential to mitigate MEV, but also as protocols with some of the biggest technical barriers.

We present V0LVER, an AMM protocol which solves an incentivization trilemma between users, passive liquidity providers, and block producers. V0LVER enables users and passive liquidity providers to interact without paying MEV or incurring uncontrolled loss-versus-rebalancing to the block producer. V0LVER is an AMM protocol built on an encrypted transaction mempool, where transactions are decrypted after being allocated liquidity by the AMM. V0LVER ensures this liquidity, given some external market price, is provided at that price in expectancy. This is done by incentivizing the block producer to move the pool price to the external market price. With this, users transact in expectancy at the external market price in exchange for a fee, with AMMs providing liquidity in expectancy at the external market price. Under block producer and liquidity provider competition, all of the fees in V0LVER approach zero. Without block producer arbitrage, V0LVER guarantees fall back to those of an AMM, albeit free from loss-versus-rebalancing and user-level MEV.

In this work, we introduce the private, anonymous, collateralizable commitments (PACCs) framework. PACCs allow any smart contract wallet holder to collateralize a claim, request, or commitment in general, in a private and anonymous manner. PACCs can prove arbitrarily much or little about the wallet generating the commitment, and/or the transaction which is being committed. We demonstrate that PACCs can be applied to effectively eliminate maximal-extractable value (MEV) in DeFi where it currently occurs, shifting MEV instead to censorship. After describing our protocol with detail, we provide an implementation using the Ethereum blockchain, and whose benchmarks prove how PACCs are completely feasible.

The always-available liquidity of automated market makers (AMMs) has been one of the most important catalysts in early cryptocurrency adoption. However, it has become increasingly evident that AMMs in their current form are not viable investment options for passive liquidity providers. This is large part due to the cost incurred by AMMs providing stale prices to arbitrageurs against external market prices, formalized as loss-versus-rebalancing (LVR) [Milionis et al., 2022]. In this paper, we present Diamond, an automated market making protocol that aligns the incentives of liquidity providers and block producers in the protocol-level retention of LVR. In Diamond, block producers effectively auction the right to capture any arbitrage that exists between the external market price of a Diamond pool, and the price of the pool itself. The proceeds of these auctions are shared by the Diamond pool and block producer in a way that is proven to remain incentive compatible for the block producer. Given the participation of competing arbitrageurs to capture LVR, LVR is minimized in Diamond. We formally prove this result, and detail an implementation of Diamond. We also provide comparative simulations of Diamond to relevant benchmarks, further evidencing the LVR-protection capabilities of Diamond. With this new protection, passive liquidity provision on blockchains can become rationally viable, beckoning a new age for decentralized finance.

Decentralized computation outsourcing should allow anyone to access the large amounts of computational power that exists in the Internet of Things. Unfortunately, when trusted third parties are removed to achieve this decentralization, ensuring an outsourced computation is performed correctly remains a significant challenge. In this paper, we provide a solution to this problem. We outline Marvel DC, a fully decentralized blockchain-based distributed-computing protocol which formally guarantees that computers are strictly incentivized to correctly perform requested computations. Furthermore, Marvel DC utilizes a reputation management protocol to ensure that, for any minority of computers not performing calculations correctly, these computers are identified and selected for computations with diminishing probability. We then outline Privacy Marvel DC, a privacy-enhanced version of Marvel DC which decouples results from the computers which computed them, making the protocol suitable for computations such as Federated Learning, where results can reveal sensitive information about that computer that computed them. We provide an implementation of Marvel DC and analyses of both protocols, demonstrating that they are not only the first protocols to provide the aforementioned formal guarantees, but are also practical, competitive with prior attempts in the field, and ready to deploy.

We present FairTraDEX, a decentralized exchange (DEX) protocol based on frequent batch auctions (FBAs), which provides formal game-theoretic guarantees against extractable value. FBAs when run by a trusted third-party provide unique game-theoretic optimal strategies which ensure players are shown prices equal to the liquidity provider's fair price, excluding explicit, pre-determined fees. FairTraDEX replicates the key features of an FBA that provide these game-theoretic guarantees using a combination of set-membership in zero-knowledge protocols and an escrow-enforced commit-reveal protocol. We extend the results of FBAs to handle monopolistic and/or malicious liquidity providers. We provide real-world examples that demonstrate that the costs of executing orders in existing academic and industry-standard protocols become prohibitive as order size increases due to basic value extraction techniques, popularized as maximal extractable value. We further demonstrate that FairTraDEX protects against these execution costs, guaranteeing a fixed fee model independent of order size, the first guarantee of it's kind for a DEX protocol. We also provide detailed Solidity and pseudo-code implementations of FairTraDEX, making FairTraDEX a novel and practical contribution.

Existing standards for player characterisation in tokenised state machine replication protocols depend on honest players who will always follow the protocol, regardless of possible token increases for deviating. Given the ever-increasing market capitalisation of these tokenised protocols, honesty is becoming more expensive and more unrealistic. As such, this out-dated player characterisation must be removed to provide true guarantees of safety and liveness in a major stride towards universal trust in state machine replication protocols and a new scale of adoption. As all current state machine replication protocols are built on these legacy standards, it is imperative that a new player model is identified and utilised to reflect the true nature of players in tokenised protocols, now and into the future. To this effect, we propose the ByRa player model for state machine replication protocols. In the ByRa model, players either attempt to maximise their tokenised rewards, or behave adversarially. This merges the fields of game theory and distributed systems, an intersection in which tokenised state machine replication protocols exist, but on which little formalisation has been carried out. In the ByRa model, we identify the properties of strong incentive compatibility in expectation and fairness that all protocols must satisfy in order to achieve state machine replication. We then provide Tenderstake, a protocol which provably satisfies these properties, and by doing so, achieves state machine replication in the ByRa model., Comment: 14 pages