Control-Theoretic Liquidity Optimization in Decentralized Finance: The Aeon Protocol

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1. Verfasser: Bennawit, Caelin
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Veröffentlicht: Zenodo 2026
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author Bennawit, Caelin
author_facet Bennawit, Caelin
contents <p>Decentralized finance (DeFi) systems currently rely on static parameters and reactive mechanisms that fail to adapt to rapidly changing market conditions. These limitations contribute to systemic inefficiencies including yield instability, capital fragmentation, and the extraction of value through adversarial mechanisms such as maximal extractable value (MEV).</p> <p>This paper introduces <strong>The Aeon Protocol</strong>, a control-theoretic framework for adaptive financial infrastructure. The protocol models decentralized liquidity management as a closed-loop control system in which economic variables are continuously monitored, predicted, and regulated through feedback mechanisms derived from classical control theory.</p> <p>The Aeon architecture integrates four primary system layers:</p> <p>• <strong>KENDRA</strong> — predictive forecasting and regime detection from on-chain data streams<br>• <strong>NOEMA</strong> — model predictive control for economic orchestration<br>• <strong>AURA</strong> — ethical routing layer that captures and redistributes MEV through sealed-bid auctions<br>• <strong>LEIA</strong> — liquidity management engine governing protocol-owned liquidity across decentralized markets</p> <p>At the core of the system is a <strong>PID-controlled adaptive yield mechanism</strong> designed to regulate total value locked (TVL) and stabilize protocol yield within bounded ranges. A complementary <strong>Burn-and-Mint Equilibrium (BME)</strong> mechanism dynamically adjusts token supply to maintain long-term economic balance.</p> <p>A central implication of the Aeon architecture is the emergence of a <strong>self-reinforcing liquidity ecosystem</strong>. By integrating predictive forecasting, control optimization, and ethical MEV capture into a closed-loop economic system, the protocol continuously identifies inefficiencies in decentralized markets and redirects the associated value back into the protocol’s liquidity layer. This process transforms otherwise extractive market dynamics into a productive feedback cycle, where captured value is redistributed through liquidity provisioning, treasury reserves, and reflection mechanisms.</p> <p>Empirical simulations and historical replay experiments demonstrate that this feedback architecture materially increases capital utilization across the system. In controlled Monte Carlo simulations spanning <strong>10,000 market scenarios</strong>, the protocol achieved improvements of <strong>50–180% in capital efficiency</strong>, while redirecting approximately <strong>68% of extractable value</strong> to protocol participants rather than external arbitrage actors. These results suggest that adaptive control systems can convert structural market inefficiencies into a persistent source of liquidity and yield generation, enabling decentralized financial networks to operate as <strong>self-regulating economic environments rather than static rule-based infrastructures</strong>.</p> <p>Formal analysis establishes asymptotic stability conditions for the controller using the <strong>Routh–Hurwitz criterion</strong> and <strong>Lyapunov stability methods</strong>, providing theoretical guarantees that the system converges toward equilibrium under defined parameter constraints.</p> <p>Collectively, the results demonstrate that control-theoretic economic architectures can provide a principled foundation for designing <strong>stable, transparent, and adaptive decentralized financial infrastructure</strong>. The Aeon Protocol represents a broader research direction toward <strong>autonomous economic systems</strong>, where financial networks operate as self-regulating feedback environments capable of maintaining equilibrium under dynamic market conditions.</p>
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spellingShingle Control-Theoretic Liquidity Optimization in Decentralized Finance: The Aeon Protocol
Bennawit, Caelin
Control Theory
Cybernetics/economics
Blockchain/economics
Algorithmic Trading
Financial instrument
Computational intelligence
Autonomous Financial Systems
<p>Decentralized finance (DeFi) systems currently rely on static parameters and reactive mechanisms that fail to adapt to rapidly changing market conditions. These limitations contribute to systemic inefficiencies including yield instability, capital fragmentation, and the extraction of value through adversarial mechanisms such as maximal extractable value (MEV).</p> <p>This paper introduces <strong>The Aeon Protocol</strong>, a control-theoretic framework for adaptive financial infrastructure. The protocol models decentralized liquidity management as a closed-loop control system in which economic variables are continuously monitored, predicted, and regulated through feedback mechanisms derived from classical control theory.</p> <p>The Aeon architecture integrates four primary system layers:</p> <p>• <strong>KENDRA</strong> — predictive forecasting and regime detection from on-chain data streams<br>• <strong>NOEMA</strong> — model predictive control for economic orchestration<br>• <strong>AURA</strong> — ethical routing layer that captures and redistributes MEV through sealed-bid auctions<br>• <strong>LEIA</strong> — liquidity management engine governing protocol-owned liquidity across decentralized markets</p> <p>At the core of the system is a <strong>PID-controlled adaptive yield mechanism</strong> designed to regulate total value locked (TVL) and stabilize protocol yield within bounded ranges. A complementary <strong>Burn-and-Mint Equilibrium (BME)</strong> mechanism dynamically adjusts token supply to maintain long-term economic balance.</p> <p>A central implication of the Aeon architecture is the emergence of a <strong>self-reinforcing liquidity ecosystem</strong>. By integrating predictive forecasting, control optimization, and ethical MEV capture into a closed-loop economic system, the protocol continuously identifies inefficiencies in decentralized markets and redirects the associated value back into the protocol’s liquidity layer. This process transforms otherwise extractive market dynamics into a productive feedback cycle, where captured value is redistributed through liquidity provisioning, treasury reserves, and reflection mechanisms.</p> <p>Empirical simulations and historical replay experiments demonstrate that this feedback architecture materially increases capital utilization across the system. In controlled Monte Carlo simulations spanning <strong>10,000 market scenarios</strong>, the protocol achieved improvements of <strong>50–180% in capital efficiency</strong>, while redirecting approximately <strong>68% of extractable value</strong> to protocol participants rather than external arbitrage actors. These results suggest that adaptive control systems can convert structural market inefficiencies into a persistent source of liquidity and yield generation, enabling decentralized financial networks to operate as <strong>self-regulating economic environments rather than static rule-based infrastructures</strong>.</p> <p>Formal analysis establishes asymptotic stability conditions for the controller using the <strong>Routh–Hurwitz criterion</strong> and <strong>Lyapunov stability methods</strong>, providing theoretical guarantees that the system converges toward equilibrium under defined parameter constraints.</p> <p>Collectively, the results demonstrate that control-theoretic economic architectures can provide a principled foundation for designing <strong>stable, transparent, and adaptive decentralized financial infrastructure</strong>. The Aeon Protocol represents a broader research direction toward <strong>autonomous economic systems</strong>, where financial networks operate as self-regulating feedback environments capable of maintaining equilibrium under dynamic market conditions.</p>
title Control-Theoretic Liquidity Optimization in Decentralized Finance: The Aeon Protocol
topic Control Theory
Cybernetics/economics
Blockchain/economics
Algorithmic Trading
Financial instrument
Computational intelligence
Autonomous Financial Systems
url https://doi.org/10.5281/zenodo.18947232