EvoMaestro：迈向可解释、可引导的LLM驱动程序进化
EvoMaestro: Toward Interpretable and Steerable LLM-Driven Program Evolution
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中文总结 AI 辅助
EvoMaestro是一个交互式可视化分析系统，通过语义监督框架帮助领域专家理解和引导LLM驱动的程序进化过程，提升理解并支持专家引导。
中文摘要 AI 辅助
近期的程序进化系统利用大型语言模型（LLM）来生成并迭代改进程序种群，在数学、算法设计和科学计算领域取得了显著进展。然而，这些系统大多以全自动黑箱方式运行。随着种群规模的增长，领域专家需要理解众多程序中的得分、代码变更、推理过程和算法思想，而当前界面在支持理解或引导进化方面的能力有限。我们将这种理解和引导进化中算法思想种群的需求定义为语义监督。一项包含8位领域专家的形成性研究为这一新兴的人机交互问题提出了六项设计要求。随后，我们提出了一种可引导的程序进化框架，使专家判断能够影响后续进化。基于该框架，EvoMaestro是一个交互式可视化分析系统，将进化信息从种群概览组织到源代码层面。它帮助专家定位和比较值得关注的程序，用自然语言引导进化，结合有前景的想法，并剪除低效方向。为期七天的系统演示展示了一位专家如何在长期进化过程中应用这些能力。一项包含12名参与者的受试者内研究显示，EvoMaestro提高了用户对进化过程的理解，减少了认知负担，并支持专家引导。这些发现表明，在开放式的LLM驱动搜索中保持专家能动性，需要同时支持知情判断和塑造后续自动化的能力。
英文摘要
Recent program evolution systems use large language models (LLMs) to generate and iteratively improve populations of programs, producing striking advances in mathematics, algorithm design, and scientific computing. Yet these systems largely operate as fully automated black boxes. As populations grow, domain experts must make sense of the scores, code changes, reasoning, and algorithmic ideas across many programs, while current interfaces provide limited support for understanding or redirecting the evolution. We characterize this need to understand and steer populations of evolving algorithmic ideas as semantic oversight. A formative study with 8 domain experts yields six design requirements for this emerging human-computer interaction problem. We then propose a steerable program evolution framework that lets expert judgments shape subsequent evolution. Built on this framework, EvoMaestro is an interactive visual analytics system that organizes evolution information from population overview to source code. It helps experts locate and compare noteworthy programs, guide evolution with natural language, combine promising ideas, and prune unproductive directions. A seven-day system demonstration illustrates how an expert applied these capabilities throughout a long-running evolution process. A within-subjects study with 12 participants shows that EvoMaestro improves users' understanding of evolution processes, reduces cognitive workload, and supports expert steering. These findings suggest that preserving expert agency in open-ended LLM-driven search requires support for both informed judgment and the ability to shape subsequent automation.
发表机构
- Nanyang Technological University（南洋理工大学）
- Tsinghua University（清华大学）
机构由 AI 辅助整理，请以论文原文为准。
