The paradigm of automated program generation is shifting from one-shot generation to inference-time search, where Large Language Models (LLMs) function as semantic mutation operators within evolutionary loops. While effective, these systems are currently governed by static schedules that fail to account for the non-stationary dynamics of the search process. This rigidity results in substantial computational waste, as resources are indiscriminately allocated to stagnating populations while promising frontiers remain under-exploited. We introduce AdaEvolve, a framework that reformulates LLM-driven evolution as a hierarchical adaptive optimization problem. AdaEvolve uses an "accumulated improvement signal" to unify decisions across three levels: Local Adaptation, which dynamically modulates the exploration intensity within a population of solution candidates; Global Adaptation, which routes the global resource budget via bandit-based scheduling across different solution candidate populations; and Meta-Guidance which generates novel solution tactics based on the previously generated solutions and their corresponding improvements when the progress stalls. We demonstrate that AdaEvolve consistently outperforms the open-sourced baselines across 185 different open-ended optimization problems including combinatorial, systems optimization and algorithm design problems.

翻译：自动化程序生成的范式正从一次性生成转向推理时搜索，其中大语言模型（LLMs）在进化循环中充当语义变异算子。尽管有效，这些系统目前由静态调度机制控制，未能考虑搜索过程的非平稳动态特性。这种僵化性导致大量计算资源浪费，因为资源被不加区分地分配给停滞的种群，而有潜力的前沿领域却未被充分探索。本文提出AdaEvolve框架，将LLM驱动的进化重新构建为分层自适应优化问题。AdaEvolve利用"累积改进信号"统一三个层级的决策：局部适应——动态调节候选解种群内的探索强度；全局适应——通过基于多臂老虎机的调度机制在不同候选解种群间分配全局资源预算；元引导——当进展停滞时，基于先前生成的解及其对应改进生成新的求解策略。我们在185个开放式优化问题（包括组合优化、系统优化和算法设计问题）上验证了AdaEvolve始终优于开源基线方法。