# Blockchain Meets LLMs: A Living Survey on Bidirectional Integration

arXiv: https://arxiv.org/abs/2411.16809 · HTML: https://mlsanity.com/paper/2411.16809/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 44 of its 78 references that are in the map.
- First posted on arXiv 25 Nov 2024.

## Builds on (most cited first, 30 of 44)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions](https://mlsanity.com/paper/2311.05232/) (2023)
- [Extracting Training Data from Diffusion Models](https://mlsanity.com/paper/2301.13188/) (2023)
- [Blockchained On-Device Federated Learning](https://mlsanity.com/paper/1808.03949/) (2018)
- [GIT-Mol: A Multi-modal Large Language Model for Molecular Science with Graph, Image, and Text](https://mlsanity.com/paper/2308.06911/) (2023)
- [A Survey of GPT-3 Family Large Language Models Including ChatGPT and GPT-4](https://mlsanity.com/paper/2310.12321/) (2023)
- [Lifelong Federated Reinforcement Learning: A Learning Architecture for Navigation in Cloud Robotic Systems](https://mlsanity.com/paper/1901.06455/) (2019)
- [Large Language Models for Cyber Security: A Systematic Literature Review](https://mlsanity.com/paper/2405.04760/) (2024)
- [Decentralized Federated Learning through Proxy Model Sharing](https://mlsanity.com/paper/2111.11343/) (2021)
- [Large Language Models for Forecasting and Anomaly Detection: A Systematic Literature Review](https://mlsanity.com/paper/2402.10350/) (2024)
- [Large Language Model-Powered Smart Contract Vulnerability Detection: New Perspectives](https://mlsanity.com/paper/2310.01152/) (2023)
- [Exploring Human-Like Translation Strategy with Large Language Models](https://mlsanity.com/paper/2305.04118/) (2023)
- [Revolutionizing Cyber Threat Detection with Large Language Models: A privacy-preserving BERT-based Lightweight Model for IoT/IIoT Devices](https://mlsanity.com/paper/2306.14263/) (2023)
- [BAFFLE : Blockchain Based Aggregator Free Federated Learning](https://mlsanity.com/paper/1909.07452/) (2019)
- [Do you still need a manual smart contract audit?](https://mlsanity.com/paper/2306.12338/) (2023)
- [Experiments of Federated Learning for COVID-19 Chest X-ray Images](https://mlsanity.com/paper/2007.05592/) (2020)
- [Federated Imitation Learning: A Novel Framework for Cloud Robotic Systems with Heterogeneous Sensor Data](https://mlsanity.com/paper/1912.12204/) (2019)
- [GradientCoin: A Peer-to-Peer Decentralized Large Language Models](https://mlsanity.com/paper/2308.10502/) (2023)
- [Decentralized & Collaborative AI on Blockchain](https://mlsanity.com/paper/1907.07247/) (2019)
- [opML: Optimistic Machine Learning on Blockchain](https://mlsanity.com/paper/2401.17555/) (2024)
- [An Overview on Smart Contracts: Challenges, Advances and Platforms](https://mlsanity.com/paper/1912.10370/) (2019)
- [Blockchain Large Language Models](https://mlsanity.com/paper/2304.12749/) (2023)
- [Blockchain for Internet of Things: A Survey](https://mlsanity.com/paper/1906.00245/) (2019)
- [Fedstellar: A Platform for Decentralized Federated Learning](https://mlsanity.com/paper/2306.09750/) (2023)
- [Efficient Avoidance of Vulnerabilities in Auto-completed Smart Contract Code Using Vulnerability-constrained Decoding](https://mlsanity.com/paper/2309.09826/) (2023)
- [Incentive Mechanism Design for Joint Resource Allocation in Blockchain-based Federated Learning](https://mlsanity.com/paper/2202.10938/) (2022)
- [Large Language Models for Blockchain Security: A Systematic Literature Review](https://mlsanity.com/paper/2403.14280/) (2024)
- [Checking Smart Contracts with Structural Code Embedding](https://mlsanity.com/paper/2001.07125/) (2020)
- [LLM-SmartAudit: Advanced Smart Contract Vulnerability Detection](https://mlsanity.com/paper/2410.09381/) (2024)

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

- [Autonomous Agents on Blockchains: Standards, Execution Models, and Trust Boundaries](https://mlsanity.com/paper/2601.04583/) (2026)
- [InfiCoEvalChain: A Blockchain-Based Decentralized Framework for Collaborative LLM Evaluation](https://mlsanity.com/paper/2602.08229/) (2026)

## Abstract (arXiv)

In the domain of large language models, considerable advancements have been attained in multimodal large language models and explainability research, propelled by the continuous technological progress and innovation. Nonetheless, security and privacy concerns continue to pose as prominent challenges in this field. The emergence of blockchain technology, marked by its decentralized nature, tamper-proof attributes, distributed storage functionality, and traceability, has provided novel approaches for resolving these issues. Both of these technologies independently hold vast potential for development; yet, their combination uncovers substantial cross-disciplinary opportunities and growth prospects. The current research tendencies are increasingly concentrating on the integration of blockchain with large language models, with the aim of compensating for their respective limitations through this fusion and promoting further technological evolution. In this study, we evaluate the advantages and developmental constraints of the two technologies, and explore the possibility and development potential of their combination. This paper primarily investigates the technical convergence in two directions: Firstly, the application of large language models to blockchain, where we identify six major development directions and explore solutions to the shortcomings of blockchain technology and their application scenarios; Secondly, the application of blockchain technology to large language models, leveraging the characteristics of blockchain to remedy the deficiencies of large language models and exploring its application potential in multiple fields.