CliffSearch: Structured Agentic Co-Evolution over Theory and Code for Scientific Algorithm Discovery

CliffSearch emerges as a novel framework for scientific algorithm discovery, addressing the limitations of current large language model (LLM)-guided search systems. By integrating theory and code, CliffSearch enables a more structured approach to hypothesis generation, implementation, and testing. This agentic evolutionary framework incorporates core evolution operators that prioritize correctness and originality, resulting in more robust and innovative algorithmic solutions. Unlike existing methods that focus solely on code optimization, CliffSearch's dual emphasis on theoretical and practical aspects facilitates a more comprehensive search process¹. By bridging the gap between scientific structure and code-based artifacts, CliffSearch has the potential to accelerate breakthroughs in various fields. This development matters to practitioners because it can lead to more efficient and effective algorithm discovery, ultimately driving advancements in fields like cybersecurity, where novel algorithms can be leveraged to stay ahead of emerging threats.