The increasingly complex Web3 ecosystem and decentralized finance (DeFi) landscape demand ever higher levels of technical expertise and financial literacy from participants. The Intent-Centric paradigm in DeFi has thus emerged in response, which allows users to focus on their trading intents rather than the underlying execution details. However, existing approaches, including Typed-intent design and LLM-driven solver, trade off expressiveness, trust, privacy, and composability. We present OMNIINTENT, a language-runtime co-design that reconciles these requirements. OMNIINTENT introduces ICL, a domain-specific Intent-Centric Language for precise yet flexible specification of triggers, actions, and runtime constraints; a Trusted Execution Environment (TEE)-based compiler that compiles intents into signed, state-bound transactions inside an enclave; and an execution optimizer that constructs transaction dependency graphs for safe parallel batch submission and a mempool-aware feasibility checker that predicts execution outcomes. Our full-stack prototype processes diverse DeFi scenarios, achieving 89.6% intent coverage, up to 7.3x throughput speedup via parallel execution, and feasibility-prediction accuracy up to 99.2% with low latency.

翻译：日益复杂的Web3生态系统与去中心化金融（DeFi）格局对参与者的技术专业性和金融素养提出了更高要求。DeFi领域的意图中心化范式应运而生，该范式允许用户专注于交易意图而非底层执行细节。然而，现有方法（包括类型化意图设计和基于大语言模型的求解器）在表达力、可信性、隐私性和可组合性之间存在权衡。本文提出OMNIINTENT——一种通过语言-运行时协同设计来协调这些需求的框架。OMNIINTENT包含三个核心组件：ICL（一种领域特定意图中心化语言），用于精确而灵活地定义触发条件、操作及运行时约束；基于可信执行环境（TEE）的编译器，在安全飞地内将意图编译为经签名且状态绑定的交易；以及执行优化器，该优化器通过构建交易依赖图实现安全的并行批量提交，并配备内存池感知的可行性检查器以预测执行结果。我们的全栈原型系统可处理多样化DeFi场景，实现89.6%的意图覆盖率，通过并行执行获得最高7.3倍的吞吐量提升，可行性预测准确率高达99.2%且保持低延迟。