Rethinking Code Similarity for Automated Algorithm Design with LLMs
The rise of Large Language Model-based Automated Algorithm Design (LLM-AAD) has transformed algorithm development by autonomously generating code implementations of expert-level algorithms. Unlike traditional expert-driven algorithm development, in the LLM-AAD paradigm, the main design principle behind an algorithm is often implicitly embedded in the generated code. Therefore, assessing algorithmic similarity directly from code, distinguishing genuine algorithmic innovation from mere syntactic variation, becomes essential. While various code similarity metrics exist, they fail to capture algorithmic similarity, as they focus on surface-level syntax or output equivalence rather than the un...
| Authors | Rui Zhang, Zhichao Lu |
|---|---|
| Primary category | cs.AI |
| Published | 3 March 2026 |
| Last updated at source | 3 March 2026 |
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