[2604.01210] CliffSearch: Structured Agentic Co-Evolution over Theory and Code for Scientific Algorithm Discovery

Computer Science > Machine Learning arXiv:2604.01210 (cs) [Submitted on 1 Apr 2026] Title:CliffSearch: Structured Agentic Co-Evolution over Theory and Code for Scientific Algorithm Discovery Authors:Youssef Mroueh, Carlos Fonseca, Brian Belgodere, David Cox View a PDF of the paper titled CliffSearch: Structured Agentic Co-Evolution over Theory and Code for Scientific Algorithm Discovery, by Youssef Mroueh and 3 other authors View PDF HTML (experimental) Abstract:Scientific algorithm discovery is iterative: hypotheses are proposed, implemented, stress-tested, and revised. Current LLM-guided search systems accelerate proposal generation, but often under-represent scientific structure by optimizing code-only artifacts with weak correctness/originality gating. We present CliffSearch, an agentic evolutionary framework in which the core evolution operators (pair selection, crossover, mutation, and review) are implemented as LLM agents, and the loop is designed around three principles: (1) each node is a structured scientific artifact, instantiated in either theory+code or code_only mode, (2) reviewer judgments of correctness and originality are first-class selection gates alongside optimization of the benchmark metric of interest, and (3) mutation is split into exploration and correction pathways with distinct objectives. Exploration mutation imports ideas from adjacent scientific domains to increase novelty, while correction mutation performs targeted evidence-guided repair usi...