Enabling Decentralized, Programmable Order Flow Auctions for MEV Mitigation in DeFi
- Authors
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Muneeb Uddin Syed
Author
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- Keywords:
- Decentralized Finance, MEV Mitigation, Order Flow Auctions, Account Abstraction, Permissionless Protocols, DeFi Optimization
- Abstract
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A decentralized auction system that is decentralized is proposed to solve the many limitations that occur with traditional DeFi order flow management. Those limitations often come from centralized coordination and poor transaction execution. The framework enables the development of intent-driven auctions to improve liquidity discovery while allowing independent solvers to join seamlessly, separating the execution logic from application-specific functionality. As a result, one does not have to depend on intermediaries for routing or executing the transaction. The architecture is also compatible with alternative auction mechanisms. This not only permits a broad range of customizable decentralized applications (dApps) but also provides a path to incorporate future developments, such as secure computation and cryptographic advances, ensuring the continuous evolution of ecosystems. Its modular design fosters interoperability between applications, allowing different parts to evolve independently but still work well together in the larger ecosystem. Simultaneously, it encourages transparency in competition among solvers, strengthens decentralization across the MEV supply chain, and mitigates opportunities for rent extraction by centralised actors. Faster execution of transactions and increased coordination of liquidity will reduce costs and improve efficiency across blockchain networks. Moreover, the architecture provides a flexible and workable mechanism to manage cross-chain order flows. It enables safe, reliable and efficient execution, while respecting the foundational decentralized principles of modern blockchain systems.
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- Published
- 2026-06-29
- Issue
- Vol. 1 No. 3 (2026)
- Section
- Articles
- License
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Copyright (c) 2026 International Journal of Intelligent Systems and Data Science

This work is licensed under a Creative Commons Attribution 4.0 International License.
