OMNIINTENT: A Trusted Intent-Centric Framework for User-Friendly Web3

2026-03-04

AI Summary

This work addresses the high technical barriers that hinder mainstream user participation in Web3 and DeFi, as well as the limitations of existing intent-centric approaches in simultaneously achieving expressiveness, trustworthiness, privacy, and composability. To overcome these challenges, the authors propose a co-designed language-runtime framework that enables users to express high-level transactional intents via a domain-specific language (ICL), which are then securely compiled and executed within a trusted execution environment (TEE). The system innovatively integrates transaction dependency graph construction, parallel batched submission, and mempool-aware feasibility prediction to enable efficient and accurate intent fulfillment. Experimental evaluation demonstrates that the prototype achieves 89.6% intent coverage across diverse DeFi scenarios, a 7.3× throughput improvement, and a 99.2% accuracy in feasibility prediction with low latency.

Technology Category

• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Planning, Routing, and Scheduling: Planning with Language Models
• Multiagent Systems: Mechanism Design

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Decentralized Web and Fediverse systems
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Economics, Online Markets and Human Computation: Sustainability of Web economics

Abstract

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.

Problem

• Intent-Centric
• Web3
• DeFi
• trust
• privacy

Innovation

• Intent-Centric
• Trusted Execution Environment (TEE)
• Domain-Specific Language
• Parallel Transaction Execution
• Feasibility Prediction