Advanced Strategies for Mitigating MEV

The TETRICS implements cutting-edge techniques rooted in declarative programming and formal methods to effectively mitigate Maximal Extractable Value (MEV) exploitation. This document outlines the comprehensive strategies utilized by the TETRICS Protocol to enhance transaction security and efficiency.

Formal Verification and Advanced Optimization

Rigorous Formal Verification

• Semantic Preservation: Our system employs formal verification techniques to ensure that the transformation of high-level intents into atomic transactions maintains the intended semantics. This critical step reinforces the integrity of transactions by minimizing unforeseen vulnerabilities that could be exploited by MEV attackers.

Dynamic Optimization Modules

• Solver, Searcher, and Resolver System: The solver module is designed to optimize execution paths by incorporating real-time market data and potential liquidity sources. Our infrastructure includes dedicated searchers that continuously feed the optimization engine with the latest market insights, while resolvers address and reconcile discrepancies just prior to execution. This dynamic integration ensures that the final execution plan is both optimal and resilient against MEV extraction techniques.

Transaction Execution and Privacy Enhancements

Atomic Execution

• Complete Transaction Integrity: When intents are batched, they are executed atomically—either all transactions occur or none do. This atomicity guarantees that attackers cannot exploit partial execution scenarios, effectively neutralizing a range of common MEV strategies and ensuring secure transactions.

Batch Aggregation for Enhanced Privacy

• Obfuscation and Anonymity: By aggregating multiple user intents into a single batch, the system successfully obscures the details of individual orders. This obfuscation is crucial, as it significantly complicates efforts by adversaries to identify and target specific transactions for front-running or sandwich attacks.

Efficient Batch Processing and Transaction Cost Reduction

Strategic Batch Aggregation

• Collective Analysis and Optimization: The GOEMON batches individual transaction intents for collective examination, ensuring optimal execution paths. This approach not only boosts efficiency and privacy but also leads to reduced transaction fees, enhancing the overall user experience.

Anonymity and Liquidity Pooling

• Complicated Targeting: By pooling liquidity and obscuring transaction details, the protocol makes it increasingly challenging for malicious actors to target specific transactions, thereby enhancing security and user confidentiality.

Intra-Batch Optimization and Matching

• Optimal Matching: Within each batch, the solver module identifies optimal matching opportunities. For instance, if Alice intends to buy tokens at a specific price and Bob has a corresponding sell intent, both can be matched efficiently within the same batch.

Algorithmic Sequencing and Efficiency

• Efficient Operations: The optimization engine employs advanced algorithms to determine the most efficient sequence of operations. This step minimizes gas fees while maximizing execution efficiency, using data structures like hash maps or trees for rapid indexing and matching.

Merging and Cost Reduction

• Transaction Merging: By analyzing user intents collectively, smaller orders can be combined into larger transactions, thereby reducing on-chain interactions and cutting down individual user gas costs significantly.

Comprehensive MEV Mitigation

The aggregation and optimization of transaction intents in batches form the cornerstone of the protocol's strategy to mitigate MEV. This proactive approach effectively hinders external entities from exploiting individual transactions through tactics like front-running, thereby safeguarding users from MEV exploitation and ensuring fairer transaction outcomes.