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This page presents an AI-generated summary of a published research paper. The original authors did not write or review this article. [See full disclosure ↓]

SmartCoder-R1 improves secure smart contract generation

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Research area:finance-marketsfinancial-markets

What the study found

The study reports that SmartCoder-R1, a model for generating smart contracts, achieved new best results among the compared systems. It produced code and reasoning that were rated highly for functionality, security, and clarity.

Why the authors say this matters

The authors say this matters because smart contracts manage high-value assets, and vulnerabilities can cause large financial losses. The study suggests that making LLM-generated smart contracts both more secure and more explainable could address two linked problems: hidden reasoning and insecure code.

What the researchers tested

The researchers built SmartCoder-R1 on Qwen2.5-Coder-7B, a large language model for code generation. They used continual pre-training on smart contract code, long chain-of-thought supervised fine-tuning on 7,998 expert-validated reasoning-and-code samples, and a reinforcement learning stage called Security-Aware Group Relative Policy Optimization on 1,691 samples.

What worked and what didn't

Against 18 state-of-the-art baselines on 756 real-world functions from 289 deployed contracts, SmartCoder-R1 achieved the top scores on five metrics: ComPass, VulRate, SafeAval, FuncRate, and FullRate. The abstract states that FullRate reached 50.53% and was 45.79% higher than the strongest baseline, DeepSeek-R1; it also reports human evaluation ratings of 82.7% for functionality, 85.3% for security, and 90.7% for clarity. The abstract does not report which specific cases remained difficult beyond the fact that performance was not perfect.

What to keep in mind

The summary provided does not describe external replication, deployment, or testing beyond the stated benchmark and human evaluation. The abstract also does not give details on the validation criteria beyond compilability, functionality, security, and reasoning completeness.

Key points

  • SmartCoder-R1 is presented as a secure and explainable smart contract generation framework.
  • The model uses continual pre-training, supervised fine-tuning, and reinforcement learning.
  • The study reports top performance on five benchmark metrics across 18 baselines.
  • FullRate was 50.53%, which the abstract says is a 45.79% relative improvement over DeepSeek-R1.
  • Human evaluations rated the generated reasoning highly for functionality, security, and clarity.

Disclosure

Research title:
SmartCoder-R1 improves secure smart contract generation
Authors:
Lei Yu, Jingyuan Zhang, Xin Wang, Li Yang, Fengjun Zhang, Jiajia Ma
Institutions:
Chinese Academy of Sciences, Chinese Academy of Sciences, Chinese Academy of Sciences, Chinese Academy of Sciences, Chinese Academy of Sciences, Chinese Academy of Sciences, Institute of Software, Institute of Software, Institute of Software, Institute of Software, Institute of Software, Institute of Software, University of Chinese Academy of Sciences, University of Chinese Academy of Sciences, University of Chinese Academy of Sciences
Publication date:
2026-06-30
DOI:
10.1145/3808104
OpenAlex record:
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AI provenance: This post was generated by gpt-5.4-mini (OpenAI). The original authors did not write or review this post.

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