链上主动流动性：跨链 PropAMM 的证据
Active Liquidity On Chain: Evidence from PropAMMs Across Chains
- ETH Zurich（苏黎世联邦理工学院）
- Category Labs
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究首次跨链测量propAMMs，发现其通过持续重新定价、风险收费、避免跨场所套利和欺骗报价，在成交后两秒优于传统AMMs，并为短期不知情流量提供更好价格。
AI中文摘要：
大多数典型自动做市商（AMMs）的流动性提供者是被动的，并且已知会遭受逆向选择。AMMs 传统上依赖在其上执行的交易来与外部市场（及其公允价值的观念）同步价格。因此，当外部公允价值变动时，套利者在 AMMs 上交易以摘取其过时的报价。2024 年出现了一种称为专有自动做市商（propAMMs）的新方法作为回应：这些链上程序由其单一操作者从自身库存报价，通过高效提供的价格更新在无需交易的情况下重新定价。到 2026 年，propAMMs 占 Solana 上 SOL/USDC 交易量的超过一半。我们首次对 Solana、Base 和 Monad 上的 propAMMs 进行了为期一年的纵向测量，并解码了 Base 上主导 propAMM 的链上逻辑。我们发现，在成交后两秒，propAMMs 在 Solana 上赚取 0.37 个基点，在 Base 上赚取 1.19 个基点，而 AMMs 分别损失 0.22 和 0.62 个基点。我们实证量化并分类其优势源于四个因素：propAMMs 持续重新定价而非等待交易；它们针对每个对手方可能带来的来源依赖风险收费；它们避免来自其他链上市场（如其他 AMMs）的跨场所套利；它们通过以比报价更差的价格成交来欺骗。最后，我们表明，在我们代理的零售流量（即参考价格不动时到达的成交）上，propAMMs 在 Solana 上收集 0.26 个基点，而 AMMs 收集 2.59 个基点，表明它们为短期不知情流量提供更好的价格。
英文摘要：
The liquidity providers of most typical automated market makers (AMMs) are passive and known to suffer from adverse selection. AMMs traditionally rely on trades executed on them to sync the price with external markets (and a notion of fair value thereof). As a result, when an external fair value moves, arbitrageurs trade on AMMs to pick off their stale quotes. A recent approach termed proprietary automated market makers (propAMMs) emerged as a response in 2024: these on-chain programs instead have their singular operator quote from its own inventory, repricing without a trade via efficiently provided price updates. By 2026, propAMMs accounted for more than half of SOL/USDC volume on Solana. We present the first year-long longitudinal measurement of propAMMs on Solana, Base and Monad, and decode the on-chain logic of the dominant propAMM on Base. We find that two seconds after a fill, propAMMs earn 0.37 bps on Solana and 1.19 bps on Base, while AMMs lose 0.22 and 0.62 bps. We empirically quantify and classify their edge as stemming from four factors: propAMMs continuously reprice rather than waiting for a trade, they charge for the source-dependent risk each counterparty might bring, they avoid cross-venue arbitrage from other on-chain markets (such as other AMMs), and they spoof by filling trades at a worse price than they quote. Finally, we show that on our proxy for retail flow (i.e., fills that arrive when the reference price is not moving) propAMMs collect 0.26 bps on Solana where AMMs collect 2.59 bps, indicating that they offer better prices for short-term uninformed flow.
