Large Language Models (LLMs) have demonstrated impressive capabilities in code generation. Like human programmers, LLMs tend to call high-level APIs and libraries to program efficiently. However, this shortcut may hinder LLMs from learning the essential algorithm reasoning, leading instead to rote memorization of API usage. As a result, LLMs often struggle to generalize to new or domain-specific algorithms that lack ready-made library support. In this work, we propose ARBench, a novel benchmark for evaluating LLMs’ ability to generate machine learning algorithms from scratch, beyond merely invoking high-level APIs. It emphasizes algorithmic reasoning and implementation, distinguishing genuine understanding from superficial API usage. It covers fundamental and advanced machine learning tasks, rigorously assessing current LLMs’ capacity to implement these algorithms from scratch. Our evaluation reveals the strengths and weaknesses of state-of-the-art LLMs in algorithmic reasoning and generalization, offering valuable insights to guide future research and development.

Large language models (LLMs) show significant improvement in code generation. A common practice is sampling multiple candidate codes to increase the likelihood of producing an accurate solution. However, effectively identifying the best candidate from the pool is a significant challenge. Although existing code consensus methods attempt to solve this issue, they suffer from a critical problem: relying on test cases generated by LLMs, which can be flawed or provide incomplete coverage. This problem can result in erroneous validations, causing correct code to fail flawed tests and preventing the detection of functional differences in candidate code solutions. To address these issues, we present the Dynamic-Static Synergistic Selection Method, a novel framework that combines two complementary analytical approaches. First, it uses the abstract syntax tree (AST) to detect and filter candidate solutions and test cases. Second, the method statically analyzes the quality of the solutions and then dynamically validates functional consistency based on the execution results of the extracted inputs, thereby neutralizing the impact of faulty tests. Extensive experiments demonstrate that this synergistic approach significantly outperforms existing methods, substantially enhancing the correctness of the selected code.