# Blockchain Data Analysis in the Era of Large-Language Models

arXiv: https://arxiv.org/abs/2412.09640 · HTML: https://mlsanity.com/paper/2412.09640/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 76 references that are in the map.
- First posted on arXiv 9 Dec 2024.

## Builds on (most cited first, 8 of 8)

- [LLM-SmartAudit: Advanced Smart Contract Vulnerability Detection](https://mlsanity.com/paper/2410.09381/) (2024)
- [Smart Contract Vulnerability Detection Technique: A Survey](https://mlsanity.com/paper/2209.05872/) (2022)
- [AuditGPT: Auditing Smart Contracts with ChatGPT](https://mlsanity.com/paper/2404.04306/) (2024)
- [Blockchain for Large Language Model Security and Safety: A Holistic Survey](https://mlsanity.com/paper/2407.20181/) (2024)
- [Transformer-based approach for Ethereum Price Prediction Using Crosscurrency correlation and Sentiment Analysis](https://mlsanity.com/paper/2401.08077/) (2024)
- [Blockchain and Artificial Intelligence: Synergies and Conflicts](https://mlsanity.com/paper/2405.13462/) (2024)
- [BlockScan: Detecting Anomalies in Blockchain Transactions](https://mlsanity.com/paper/2410.04039/) (2024)
- [An Empirical Analysis of Traceability in the Monero Blockchain](https://mlsanity.com/paper/1704.04299/) (2017)

## Built on by (most cited first, 2 of 2)

- [SoK: Security and Privacy of AI Agents for Blockchain](https://mlsanity.com/paper/2509.07131/) (2025)
- [DMind Benchmark: Toward a Holistic Assessment of LLM Capabilities across the Web3 Domain](https://mlsanity.com/paper/2504.16116/) (2025)

## Abstract (arXiv)

Blockchain data analysis is essential for deriving insights, tracking transactions, identifying patterns, and ensuring the integrity and security of decentralized networks. It plays a key role in various areas, such as fraud detection, regulatory compliance, smart contract auditing, and decentralized finance (DeFi) risk management. However, existing blockchain data analysis tools face challenges, including data scarcity, the lack of generalizability, and the lack of reasoning capability. We believe large language models (LLMs) can mitigate these challenges; however, we have not seen papers discussing LLM integration in blockchain data analysis in a comprehensive and systematic way. This paper systematically explores potential techniques and design patterns in LLM-integrated blockchain data analysis. We also outline prospective research opportunities and challenges, emphasizing the need for further exploration in this promising field. This paper aims to benefit a diverse audience spanning academia, industry, and policy-making, offering valuable insights into the integration of LLMs in blockchain data analysis.