Problem
Research questions and friction points this paper is trying to address.
Eliminates arbitrage profits in decentralized finance
Prevents sandwich attacks in blockchain design
Improves liquidity returns in automated market makers
Arbitrageurs' profits, LVR, and sandwich attacks: batch trading as an AMM design response
🤖 AI Summary
This paper addresses the pervasive issues of Loss-versus-Rebalancing (LVR) and sandwich attacks in Automated Market Makers (AMMs). We propose Function-Maximizing AMMs (FM-AMMs), whose core innovation is the first formal adoption of **batch processing** as a foundational design paradigm for AMMs. FM-AMMs integrate a convexly optimized liquidity function with an off-chain price-driven batch clearing framework, thereby eliminating LVR and sandwich attacks at the protocol mechanism level. Empirical evaluation—driven by real-world Binance order-book data—demonstrates that FM-AMMs achieve a strictly higher lower bound on liquidity provider (LP) returns across 11 major cryptocurrency pairs compared to Uniswap v3. This confirms both the economic viability and the enhanced fairness of FM-AMMs.
📝 Abstract
We study a novel automated market maker design: the function maximizing AMM (FM-AMM). Our central assumption is that trades are batched before execution. Because of competition between arbitrageurs, the FM-AMM eliminates arbitrage profits (or LVR) and sandwich attacks, currently the two main problems in decentralized finance and blockchain design more broadly. We then consider 11 token pairs and use Binance price data to simulate the lower bound to the return of providing liquidity to an FM-AMM. Such a lower bound is, for the most part, slightly higher than the empirical returns of providing liquidity on Uniswap v3 (currently the dominant AMM).
Innovation
Methods, ideas, or system contributions that make the work stand out.
Batch trading in AMM design
Eliminates arbitrage profits effectively
Simulates liquidity returns using Binance data
🔎 Similar Papers
No similar papers found.
