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AgileRate: Bringing Adaptivity and Robustness to DeFi Lending Markets

Mahsa Bastankhah, Viraj Nadkarni, Xuechao Wang, Pramod Viswanath

TL;DR

AgileRate tackles the brittleness of DeFi lending under static rate curves by introducing a dynamic, adaptive model of demand and supply coupled with a Recursive Least Squares-based interest-rate controller. The approach provides theoretical guarantees on rate convergence and utilization stability, and bounds adversarial manipulation relative to static curves, while offering two mitigation paths: robust data-filtering and market-structure elasticity via derivatives. Empirical validation on Aave/Compound-like data shows a best-fit error around 5% for the dynamic demand model and superior utilization performance versus static curves, with robust RLS maintaining rate stability under adversarial signals. The work demonstrates a practical pathway to more resilient, efficient DeFi lending through on-chain adaptivity, cross-venue liquidity, and risk-aware control.

Abstract

Decentralized Finance (DeFi) has revolutionized lending by replacing intermediaries with algorithm-driven liquidity pools. However, existing platforms like Aave and Compound rely on static interest rate curves and collateral requirements that struggle to adapt to rapid market changes, leading to inefficiencies in utilization and increased risks of liquidations. In this work, we propose a dynamic model of the lending market based on evolving demand and supply curves, alongside an adaptive interest rate controller that responds in real-time to shifting market conditions. Using a Recursive Least Squares algorithm, our controller tracks the external market and achieves stable utilization, while also controlling default and liquidation risk. We provide theoretical guarantees on the interest rate convergence and utilization stability of our algorithm. We establish bounds on the system's vulnerability to adversarial manipulation compared to static curves, while quantifying the trade-off between adaptivity and adversarial robustness. We propose two complementary approaches to mitigating adversarial manipulation: an algorithmic method that detects extreme demand and supply fluctuations and a market-based strategy that enhances elasticity, potentially via interest rate derivative markets. Our dynamic curve demand/supply model demonstrates a low best-fit error on Aave data, while our interest rate controller significantly outperforms static curve protocols in maintaining optimal utilization and minimizing liquidations.

AgileRate: Bringing Adaptivity and Robustness to DeFi Lending Markets

TL;DR

AgileRate tackles the brittleness of DeFi lending under static rate curves by introducing a dynamic, adaptive model of demand and supply coupled with a Recursive Least Squares-based interest-rate controller. The approach provides theoretical guarantees on rate convergence and utilization stability, and bounds adversarial manipulation relative to static curves, while offering two mitigation paths: robust data-filtering and market-structure elasticity via derivatives. Empirical validation on Aave/Compound-like data shows a best-fit error around 5% for the dynamic demand model and superior utilization performance versus static curves, with robust RLS maintaining rate stability under adversarial signals. The work demonstrates a practical pathway to more resilient, efficient DeFi lending through on-chain adaptivity, cross-venue liquidity, and risk-aware control.

Abstract

Decentralized Finance (DeFi) has revolutionized lending by replacing intermediaries with algorithm-driven liquidity pools. However, existing platforms like Aave and Compound rely on static interest rate curves and collateral requirements that struggle to adapt to rapid market changes, leading to inefficiencies in utilization and increased risks of liquidations. In this work, we propose a dynamic model of the lending market based on evolving demand and supply curves, alongside an adaptive interest rate controller that responds in real-time to shifting market conditions. Using a Recursive Least Squares algorithm, our controller tracks the external market and achieves stable utilization, while also controlling default and liquidation risk. We provide theoretical guarantees on the interest rate convergence and utilization stability of our algorithm. We establish bounds on the system's vulnerability to adversarial manipulation compared to static curves, while quantifying the trade-off between adaptivity and adversarial robustness. We propose two complementary approaches to mitigating adversarial manipulation: an algorithmic method that detects extreme demand and supply fluctuations and a market-based strategy that enhances elasticity, potentially via interest rate derivative markets. Our dynamic curve demand/supply model demonstrates a low best-fit error on Aave data, while our interest rate controller significantly outperforms static curve protocols in maintaining optimal utilization and minimizing liquidations.

Paper Structure

This paper contains 53 sections, 7 theorems, 31 equations, 5 figures, 3 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Our Contributions
  3. Problem formulation
  4. Market participants and pool mechanics
  5. Asset price model
  6. Users' incentives
  7. Protocol objectives
  8. Demand-supply balancing
  9. Adversarial robustness
  10. Liquidation and default risk control
  11. Baseline
  12. Protocol design
  13. Interest rate controller
  14. Risk parameters
  15. Adversarial analysis
  16. ...and 38 more sections

Key Result

theorem 1

Consider using Algorithm alg:optimizer to regulate the utilization of a resource pool, where the demand and supply functions are defined by Equations eq:demand-curve and eq:supply-curve, respectively. If the utilization rate $U_t\xspace > 0;\,\forall t$, then the rate deviation satisfies the followi with probability at least $1 - \delta$, where $\limsup_{t\to\infty}\psi_t(\rho\xspace)=\frac{\nu^2

Figures (5)

  • Figure 1: Impact of forgetting factor on the mean square error of the estimated parameters by the RLS-based algorithms.
  • Figure 2: Comparison of supply, used as a proxy for revenue, between the RLS-based controller and Aave's static curves. The parameters evolve according to a random walk with Gaussian noise. The x-axis represents the relative standard deviation of the noise in percentage. The fixed factor is set to $\rho\xspace=0.8$.
  • Figure 3: ETH volatility over time, with the controller dynamically adjusting the collateral factor. The bottom plot compares the actual liquidations resulting from this adjusted collateral factor with the target expected liquidations, $LT\xspace_t = 0.9$.
  • Figure :
  • Figure :

Theorems & Definitions (7)

  • theorem 1
  • theorem 2
  • lemma 1
  • proposition 1
  • theorem 3
  • theorem 4
  • theorem 5