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

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.

Figures (14)

• Figure 1: The Dutch reverse auction mechanism. Each block, the arb opportunity (red bars) grows as weight changes accumulate. The dashed lines mark arbitrageur cost thresholds; the trade occurs when the opportunity exceeds the leanest arbitrageur's cost. After the trade the cycle resets.
• Figure 2: No-arb band mechanics and the weight-driven auction cycle.
• Figure 3: Safe Haven pool, July 22 2025 (10:00--12:00 UTC), 606 blocks, 20 arb trades. Top: Allocation drift sawtooth; dashed line = 0.3% pool fee. Second: Per-block arb profit (green) with floor and calibrated thresholds; red triangles = arbitrage trades. All 20 exceed the realistic threshold.
• Figure 4: Safe Haven pool, January 8 2026 (01:00--03:00 UTC), 591 blocks, 78 trades (${\sim}4\times$ July). The sawtooth is compressed: drift rarely exceeds 0.45% and trades occur every ${\sim}$90 seconds. The y-axis scale is ${\sim}4\times$ smaller than Figure \ref{['fig:safe_haven_july']}; 32% of trades fall below the standalone threshold.
• Figure 5: Base Macro pool, January 14 2026 (08:00--10:00 UTC). The sawtooth is present but individual resets are tiny. A small number of arb trades here are driven by changes in external asset prices during this weight interpolation, marked in yellow, where we have found them by taking a 'what for' approach: if we can still find an arbitrage opportunity in that moment from the current pool reserves, current market prices, and stale weights, then we say the trade is 'price drive' and is not a key part of our weight-change-rebalance analysis. Trades resulting in negative profit for the arbitrageur (a zero time markout) we label as 'incidental routing'; these are legs of arb transactions whose profit is generated elsewhere.