Paper: arXiv 2602.22069
Authors: Matthew Willetts, Christian Harrington
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.
Complexity vs Empirical Score
- Math Complexity: 5.5/10
- Empirical Rigor: 8.5/10
- Quadrant: Holy Grail — high math complexity, high empirical rigor
Why this score: This paper presents a strong empirical analysis of dynamic-weight AMMs on live protocols (Mainnet and Base), evaluating block-level MEV arbitrage dynamics and LVR/RVR benchmarks. It bridges theoretical TFMM mechanics with granular real-world blockchain data, showing how Dutch reverse auctions lower rebalancing friction over time.
Research Flowchart
flowchart TD
A["<b>Research Goal:</b> Evaluate dynamic-weight AMM (TFMM)<br/>rebalancing efficiency vs. CEX/LVR baselines"] --> B["<b>Data Inputs:</b> Block-level data from 2 QuantAMM pools<br/>(Ethereum & Base | July 2025 & Jan 2026)"]
B --> C["<b>Arbitrage Efficiency Analysis:</b><br/>Measure trade frequency, size, and value extracted per trade"]
B --> D["<b>Long-Term Benchmark Calculation:</b><br/>Compute Loss-vs-Rebalancing (LVR) & Rebalancing-vs-Rebalancing (RVR)"]
C --> E["<b>Mainnet Outcome:</b> Efficiency improved over time<br/>(smaller, more frequent trades; matches/beats CEX models)"]
D --> F["<b>Base (L2) Outcome:</b> Routing-driven execution near zero spread;<br/>meets or exceeds perfect rebalancing baselines"]
E & F --> G["<b>Conclusion:</b> Dynamic AMMs are competitive fund execution layers,<br/>performing best on L2s due to lower gas & compressed spreads"]