Traditional decentralized exchange (DEX) architectures require users to specify explicit execution paths, liquidity sources, and routing parameters. This imperative model introduces friction, reduces execution efficiency, and exposes users to front-running risks. We present an intent-based trading paradigm that decouples user intent declaration from execution mechanics, enabling a competitive solver network to optimize execution while preserving user privacy through dark pool mechanisms.

This paper formalizes the intent-based trading model, analyzes its privacy-preserving properties, and demonstrates how competitive solver networks achieve superior execution outcomes compared to traditional order-matching systems. We introduce a dark pool architecture utilizing authenticated broadcast channels that enables private intent distribution while maintaining atomic execution guarantees through cryptographic commitments.

Our analysis demonstrates that intent-based systems significantly reduce user cognitive load compared to traditional DEX interfaces, improve execution efficiency through competitive solver optimization, and eliminate front-running vulnerabilities inherent in public mempools. The architecture supports cross-chain execution across multiple blockchain networks while maintaining complete user privacy and financial sovereignty.

Decentralized finance (DeFi) has emerged as a paradigm for financial services that operate without traditional intermediaries. However, current DEX architectures suffer from fundamental limitations: users must possess deep technical knowledge to navigate complex routing algorithms, execution paths are publicly visible on-chain, and optimal execution requires constant monitoring of liquidity conditions across multiple protocols.

Intent-based trading represents a fundamental shift from imperative to declarative exchange models. Rather than specifying how to execute a trade, users declare what they wish to achieve. This abstraction enables specialized agents (solvers) to compete for optimal execution, routing through multiple chains, DEXs, and liquidity sources to maximize user value.

1.1 Problem Statement

Traditional DEX models exhibit three critical limitations:

1. Execution Complexity: Users must understand routing algorithms, slippage tolerance, gas optimization, and multi-protocol interactions.
2. Privacy Violations: Public mempools expose trade intentions, enabling front-running and MEV extraction at user expense. Additionally, public mempools expose user trades and enable fund tracking, compromising financial privacy and sovereignty.
3. Suboptimal Execution: Static routing cannot adapt to dynamic liquidity conditions, resulting in inferior execution prices.

1.2 Contributions

This paper makes the following contributions:

• Formalization of the intent-based trading model with mathematical precision
• Architecture for privacy-preserving dark pool intent broadcasting
• Analysis of competitive solver networks and execution optimization
• Empirical evaluation of user experience improvements and execution efficiency

2.1 Decentralized Exchange Architectures

Traditional DEX architectures fall into three categories: automated market makers (AMMs), order book systems, and hybrid models. AMMs utilize constant product formulas (e.g., x * y = k) to determine prices, while order book systems match buy and sell orders. Both require users to specify exact execution parameters.

Cross-chain bridges and aggregators attempt to optimize execution by routing through multiple protocols, but they still require users to understand underlying mechanisms. Intent-based systems abstract this complexity entirely.

2.2 MEV and Front-Running

Maximum Extractable Value (MEV) represents profit extracted by reordering, inserting, or censoring transactions within blocks. Public mempools enable sophisticated actors to front-run user transactions, extracting value estimated at $675M annually [1]. Beyond MEV extraction, public mempools expose complete transaction histories, enabling surveillance and fund tracking that compromises user privacy. Intent-based systems eliminate these vulnerabilities by keeping intents private until execution.

2.3 Dark Pools in Traditional Finance

Dark pools enable private trading away from public exchanges, reducing market impact and information leakage. We adapt this concept to blockchain environments, utilizing authenticated bidirectional communication channels and cryptographic commitments to maintain privacy while preserving decentralization.

3.1 Intent Formalization

An intent I is formally defined as a tuple:

Where constraints may include minimum output amount, maximum slippage tolerance, deadline, and execution preferences. Unlike traditional orders, intents do not specify execution paths, routing algorithms, or liquidity sources.

3.2 Solver Competition Model

Solvers S = {S₁, S₂, ..., Sₙ} compete to fulfill intents by:

1. Analyzing available liquidity across multiple chains and protocols
2. Computing optimal routing paths
3. Submitting execution proposals with commitment schemes
4. Executing trades atomically upon user acceptance

The competitive model ensures that solvers optimize for user value, as suboptimal proposals are rejected in favor of superior alternatives.

3.3 Privacy-Preserving Broadcast

Intents are distributed through an authenticated dark pool messaging infrastructure, accessible only to authorized solvers. This prevents public mempool exposure while enabling competitive execution. Cryptographic commitments ensure that solvers cannot front-run each other's proposals.

4.1 Component Overview

The system consists of four primary components:

1. Intent Interface: User-facing application for intent declaration and execution monitoring
2. Dark Pool Service: Authenticated messaging infrastructure distributing intents to authorized solvers
3. Solver Network: Competitive agents optimizing execution across multiple chains and protocols
4. Execution Layer: Atomic swap mechanisms ensuring transaction finality

4.2 Intent Lifecycle

The intent lifecycle follows these stages:

4.3 Cross-Chain Execution

The architecture supports execution across 20+ blockchain networks through atomic swap protocols. Solvers coordinate multi-chain transactions, ensuring atomicity through cryptographic proofs and time-locked commitments.

5.1 Privacy Guarantees

Intent-based trading provides stronger privacy guarantees than traditional DEX models:

• No Public Mempool Exposure: Intents remain private until execution, preventing front-running and fund tracking
• Transaction Privacy: User trades are not exposed in public mempools, preventing surveillance and fund tracking
• Solver Anonymity: Solvers operate pseudonymously, preventing correlation attacks
• Zero-Knowledge Compatibility: Architecture supports ZK-proof integration for enhanced privacy

5.2 Security Properties

The system maintains security through:

1. Atomic Execution: Transactions either complete fully or revert entirely, preventing partial execution attacks
2. Commitment Schemes: Solvers commit to execution paths before revealing details, preventing manipulation
3. No KYC Requirements: Users maintain financial sovereignty without identity disclosure

5.3 Threat Model Analysis

We analyze threats including:

• Front-running: Mitigated through dark pool architecture
• Solver collusion: Prevented by competitive model and cryptographic commitments
• Network attacks: Addressed through multi-chain redundancy
• Smart contract vulnerabilities: Minimized through formal verification and audits

6.1 Execution Optimization

Competitive solver networks achieve superior execution through:

• Multi-Protocol Routing: Solvers evaluate all available liquidity sources simultaneously
• Dynamic Optimization: Real-time analysis of liquidity conditions and gas costs
• Cross-Chain Arbitrage: Exploiting price differences across networks to improve execution

6.2 User Experience Metrics

Intent-based trading systems provide significant advantages over traditional DEX models:

• Reduced Cognitive Load: Declarative intent specification eliminates the need for users to understand routing algorithms, liquidity sources, and execution mechanics
• Improved Execution: Competitive solver networks optimize execution paths across multiple protocols and chains, potentially achieving superior prices compared to static routing
• Front-Running Mitigation: Dark pool architecture prevents public mempool exposure, eliminating front-running vulnerabilities inherent in transparent order books
• Cross-Chain Unification: Support for multiple blockchain networks through a single interface, abstracting away chain-specific complexities

6.3 Scalability Considerations

The architecture scales horizontally through:

1. Distributed solver networks with no central coordination
2. Pub/sub messaging infrastructure for efficient intent distribution
3. Stateless API design enabling horizontal scaling
4. Redis-based caching for high-throughput intent management

Intent-based trading represents a fundamental advancement in decentralized exchange architecture. By decoupling intent declaration from execution mechanics, we enable superior user experience, improved execution efficiency, and enhanced privacy guarantees.

The competitive solver model ensures that users benefit from continuous optimization, while the dark pool architecture eliminates front-running vulnerabilities inherent in public mempools. Cross-chain execution capabilities enable seamless value transfer across the entire blockchain ecosystem.

Future research directions include formal verification of solver algorithms, integration of zero-knowledge proofs for enhanced privacy, and development of standardized intent formats for cross-protocol interoperability. The intent-based paradigm has the potential to become the dominant model for decentralized trading, fundamentally reshaping how users interact with DeFi protocols.

This work is partially based on research conducted for the doctoral dissertation of the first author. We thank the anonymous reviewers for their valuable feedback and suggestions. We are grateful to the research community for their support and infrastructure.

This research was supported by the Russian Foundation for Basic Research (RFBR) and the Ministry of Science and Higher Education of the Russian Federation. All authors contributed equally to this work.

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