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https://www.coze.cn/s/C9xkXQ5_YBI/

// 1. https://www.nist.gov/publications/exploring-prompt-patterns-effective-vulnerability-repair-real-world-code-large-language ​ while LLMs have inherent limitations in handling complex vulnerabilities independently, they can become effective tools for automated vulnerability repair when guided by carefully crafted prompts

// 2. https://arxiv.org/abs/2503.15341 ​ ​We propose two confidence-based uncertainty measures: Entropy-based and Probability Differential-based methods. When uncertainty is high, UnCert-CoT activates CoT-decoding to generate multiple reasoning paths and selects the final code that exhibits the highest likelihood of correctness.

In contrast, LLM directly generates the code when uncertainty is low.

This uncertainty judgment mechanism allows LLMs to prioritize complex tasks and avoid unnecessary steps in simpler cases, thereby improving overall efficiency and accuracy in code generation.