We introduce Evolutionary Ensemble (EvE), a decentralized framework that organizes existing, highly capable coding agents into a live, co-evolving system for algorithmic discovery. Rather than reinventing the wheel within the "LLMs as optimizers" paradigm, EvE fixes the base agent substrate and focuses entirely on evolving the cumulative guidance and skills that dictate agent behaviors. By maintaining two co-evolving populations, namely functional code solvers and agent guidance states, the system evaluates agents through a synchronous race, updating their empirical Elo ratings based on the marginal gains they contribute to the current solver state. When applied to a research bottleneck in In-Context Operator Networks (ICON), EvE autonomously discovered a robust rescale-then-interpolate mechanism that enables reliable example-count generalization. Crucially, controlled ablations reveal the absolute necessity of stage-dependent agent adaptation to navigate the shifting search landscapes of complex codebases. Compared to variants driven by a fixed initial agent or even a frozen "best-evolved" agent, EvE uniquely avoids phase mismatch, demonstrating that organizing agents into a self-revising ensemble is the fundamental driver for breaking through static performance ceilings.

翻译：我们提出进化式集成（EvE），这是一种去中心化框架，将现有的高性能编码智能体组织成实时协同进化的系统，用于算法发现。与在"大语言模型作为优化器"范式中重新发明轮子不同，EvE固定基础智能体基底，专注于演化决定智能体行为的累积性指导与技能。通过维护两个协同进化的种群——即功能性代码求解器与智能体指导状态——系统通过同步竞赛评估智能体，根据其对当前求解器状态的边际贡献更新其经验性Elo评分。当应用于上下文算子网络（ICON）的研究瓶颈时，EvE自主发现了一种鲁棒的"缩放-插值"机制，实现了可靠的示例计数泛化能力。关键消融实验表明，为应对复杂代码库中不断变化的搜索空间，阶段依赖的智能体自适应绝对必要。与由固定初始智能体甚至冻结"最优进化"智能体驱动的变体相比，EvE独有的相位失配规避能力证明：将智能体组织成自我修订的集成系统，是突破静态性能天花板的核心驱动力。