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RAGas：结合持续知识集成的智能合约检索增强Gas优化方案

文章背景与核心概要

随着以太坊不断拓展至金融、医疗和供应链管理等关键任务领域，优化智能合约的执行成本（即 Gas 消耗）已变得至关重要。计算复杂性与执行费用直接相关，这意味着优化不佳的代码会导致高昂的部署和运维开销。

本文介绍了 RAGas，这是一种新颖的三阶段检索增强生成（RAG）框架，旨在持续整合不断演进的 Gas 使用模式并自动优化智能合约。通过分析导致过度消耗 Gas 的语法和语义驱动因素，作者归纳出了 6 个高级别分类下的 12 种细粒度反模式（antipatterns）。在实际部署的智能合约上的评估表明，RAGas 在保持高检测准确率和召回率的同时，实现了高达 11% 的 Gas 消耗降低。

📌 Executive Summary

As Ethereum expands into mission-critical sectors such as finance, healthcare, and supply chain management, optimizing smart contract execution costs (Gas) has become essential. Computational complexity directly correlates with execution fees, meaning poorly optimized code results in prohibitive deployment and operational expenses.

This paper introduces RAGas, a novel three-stage retrieval-augmented generation framework designed to continuously integrate evolving gas-usage patterns and automatically optimize smart contracts. By analyzing syntactic and semantic drivers of excessive gas consumption, the authors identify 12 fine-grained antipatterns grouped into 6 high-level categories. Evaluated on deployed smart contracts, RAGas achieves up to an 11% reduction in gas usage while maintaining high precision and recall in detecting gas inefficiencies.

📄 Metadata

Field Detail
arXiv ID arXiv:2608.15857 [cs.AI]
Primary Subject Artificial Intelligence (cs.AI)
Authors Yishun Wang, Wenjin Yi, Wenkai Li, Zongwei Li, Xiaoqi Li
Submission Date August 16, 2026
Length 14 pages, 3 figures
DOI 10.48550/arXiv.2608.15857

🔍 Abstract & Core Contribution

以太坊智能合约会产生与其计算复杂度成正比的执行费用（Gas）。虽然优化这些代码可以降低部署成本并保持功能等效性，但当前的系统无法持续适应现代、不断演进的 Gas 使用模式。

RAGas 的核心贡献：

  1. 全面的反模式分析： 系统地对导致过度 Gas 消耗的语法和语义结构进行分类，建立了一个精选的知识库，包含：
  2. 6 个高级别分类
  3. 12 种细粒度反模式
  4. RAGas 框架： 一个三阶段的检索增强生成框架，利用大语言模型（LLM）精准定位并自动解决 Gas 低效问题。
  5. 实证验证： 证明了其在实际部署的合约上的有效性，在实现高达 11% 的 Gas 消耗降低 的同时，具备极高的检测准确率和召回率。

Ethereum smart contracts incur execution fees (Gas) that scale proportionally with their computational complexity. While optimizing this code lowers deployment costs and preserves functional equivalence, current systems fail to continuously adapt to modern, evolving gas usage patterns.

Key Contributions of RAGas:

  1. Comprehensive Antipattern Analysis: Systematic categorization of syntactic and semantic constructs leading to excessive gas use, establishing a curated knowledge base comprising:
  2. 6 High-level categories
  3. 12 Fine-grained antipatterns
  4. The RAGas Framework: A three-stage retrieval-augmented generation framework leveraging Large Language Models (LLMs) to pinpoint and automatically resolve gas inefficiencies.
  5. Empirical Validation: Demonstrated effectiveness on real-world deployed contracts, achieving up to an 11% reduction in Gas usage alongside high detection precision and recall.

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