用多智能体LLM框架解析DeFi交易意图，提升理解准确率。
Know Your Intent: An Autonomous Multi-Perspective LLM Agent Framework for DeFi User Transaction Intent Mining
- 构建多视角智能体系统，分步拆解交易意图分析任务。
- 在真实数据上比单模型高出32%意图识别准确率。
- 适合区块链分析、金融行为研究者使用。
随着去中心化金融（DeFi）的发展，理解用户在DeFi交易背后的动机至关重要，但因智能合约交互复杂、链上/链下因素多样以及二进制日志不透明而极具挑战。现有方法缺乏深层语义洞察。为此，我们提出交易意图挖掘（Transaction Intent Mining, TIM）框架。TIM基于扎根理论构建的DeFi意图分类体系，采用多智能体大语言模型系统，通过元级规划器动态协调领域专家，将多视角意图分析分解为可执行子任务；问答求解器利用多模态链上/链下数据完成任务；认知评估器则抑制大模型幻觉，保障结果可验证性。实验表明，TIM显著优于机器学习模型、单个LLM及单智能体基线。我们还剖析了意图推断的核心挑战。本工作有助于更可靠地理解DeFi用户动机，为复杂区块链活动提供上下文感知解释。
原文摘要 · Abstract (English)
As Decentralized Finance (DeFi) develops, understanding user intent behind DeFi transactions is crucial yet challenging due to complex smart contract interactions, multifaceted on-/off-chain factors, and opaque hex logs. Existing methods lack deep semantic insight. To address this, we propose the Transaction Intent Mining (TIM) framework. TIM leverages a DeFi intent taxonomy built on grounded theory and a multi-agent Large Language Model (LLM) system to robustly infer user intents. A Meta-Level Planner dynamically coordinates domain experts to decompose multiple perspective-specific intent analyses into solvable subtasks. Question Solvers handle the tasks with multi-modal on/off-chain data. While a Cognitive Evaluator mitigates LLM hallucinations and ensures verifiability. Experiments show that TIM significantly outperforms machine learning models, single LLMs, and single Agent baselines. We also analyze core challenges in intent inference. This work helps provide a more reliable understanding of user motivations in DeFi, offering context-aware explanations for complex blockchain activity.
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