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Know Your Intent: An Autonomous Multi-Perspective LLM Agent Framework for DeFi User Transaction Intent Mining

Author

Listed:
  • Qian'ang Mao
  • Yuxuan Zhang
  • Jiaman Chen
  • Wenjun Zhou
  • Jiaqi Yan

Abstract

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.

Suggested Citation

  • Qian'ang Mao & Yuxuan Zhang & Jiaman Chen & Wenjun Zhou & Jiaqi Yan, 2025. "Know Your Intent: An Autonomous Multi-Perspective LLM Agent Framework for DeFi User Transaction Intent Mining," Papers 2511.15456, arXiv.org.
    • Handle: RePEc:arx:papers:2511.15456
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    References listed on IDEAS

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    1. Paola Agnese Bongini & Francesca Mattassoglio & Alessia Pedrazzoli & Silvio Vismara, 2025. "Crypto ecosystem: navigating the past, present, and future of decentralized finance," The Journal of Technology Transfer, Springer, vol. 50(5), pages 2054-2075, October.
    2. Aiman Hairudin & Azhar Mohamad, 2024. "The isotropy of cryptocurrency volatility," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 29(3), pages 3779-3810, July.
    3. Roman Kräussl & Alessandro Tugnetti, 2024. "Non‐Fungible Tokens (NFTs): A Review of Pricing Determinants, Applications and Opportunities," Journal of Economic Surveys, Wiley Blackwell, vol. 38(2), pages 555-574, April.
    4. Cheraghali, Hamid & Molnár, Peter & Storsveen, Mattis & Veliqi, Florent, 2024. "The impact of cryptocurrency-related cyberattacks on return, volatility, and trading volume of cryptocurrencies and traditional financial assets," International Review of Financial Analysis, Elsevier, vol. 95(PB).
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    Cited by:

    1. Yichen Luo & Yebo Feng & Jiahua Xu & Yang Liu, 2026. "Resisting Manipulative Bots in Meme Coin Copy Trading: A Multi-Agent Approach with Chain-of-Thought Reasoning," Papers 2601.08641, arXiv.org, revised Feb 2026.

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