Pools as Portfolios: Observed arbitrage efficiency & LVR analysis of dynamic weight AMMs

TL;DR

This paper empirically evaluates dynamic-weight AMMs, showing they can outperform traditional rebalancing methods by exploiting arbitrage opportunities, especially on layer 2 networks, and demonstrates their potential as a competitive execution layer for tokenized funds.

Contribution

It provides the first live, real-world analysis of dynamic-weight AMMs' arbitrage efficiency and long-term rebalancing performance across different blockchain networks.

Findings

Rebalancing efficiency improves over time on mainnet, approaching CEX performance.

On Base, rebalancing remains effective even with minimal arbitrage profit, due to routing effects.

Dynamic-weight AMMs outperform traditional rebalancing benchmarks in certain conditions.

Abstract

Dynamic-weight AMMs (aka Temporal Function Market Makers, TFMMs) implement algorithmic asset allocation, analogous to index or smart beta funds, by continuously updating pools' weights. A strategy updates target weights over time, and arbitrageurs trade the pool back toward those weights. This creates a sequence of small, predictable mispricings that grow until taken, effectively executing rebalances as a series of Dutch reverse auctions. Prior theoretical and simulation work (Willetts & Harrington, 2024) predicted that this mechanism could outperform CEX-style rebalancing. We test that claim on two live pools on the QuantAMM protocol, one on Ethereum mainnet and one on Base, across two short rebalancing windows six months apart (July 2025 and January 2026). We perform block-level arbitrage analysis, and then measure long term outcomes using Loss-vs-Rebalancing (LVR) and Rebalancing-vs-Rebalancing (RVR) benchmarks. On mainnet, rebalancing becomes markedly more efficient over time (more frequent arbitrage trades with lower value extracted per trade), reaching performance comparable to or better than CEX-based models. On Base, rebalancing persists even when per-trade extraction is near (or below) zero, consistent with routing-driven execution, and achieves efficiencies that meet or exceed standard "perfect rebalancing" LVR baselines. These results demonstrate dynamic-weight AMMs as a competitive execution layer for tokenised funds, with superior performance on L2s where routing and lower data costs compress arbitrage spreads.