AtumAI 能自动写数据中心控制策略，你输入一句话它就出方案，论文里三个任务都赢过专家手调基线。
AtumAI 是 arXiv 上的一篇论文提出的数据中心控制平面策略生成框架，能把策略设计从数月工程缩短为撰写一段文字描述。它通过 Datacenter Task Compiler 把自然语言目标编译为形式化、可搜索的规格，再由 Evolutionary Design Discovery Loop 结合扩散模型、进化算法和代理模型搜索候选策略。论文在负载放置、资源扩缩和功耗管理三个控制任务上测试，AtumAI 生成的策略均优于专家手工设计的基线。
AtumAI: A Principled Framework for Agentic Generation of Datacenter Control-Plane Policies
The efficiency of a datacenter rests on its control plane policies. Designing these policies is increasingly hard: the hardware-software stack grows fast, the design space is vast and interdependent, and prototyping a single policy takes months. Agentic AI promises to automate this search. Off the shelf, however, it falls short on three fronts. It is not formal: with no structured, searchable statement of the problem, the search has little structure to exploit and hard constraints are not guaranteed. It is not transferable: each task is solved from scratch, so nothing learned on one task carries to the next. Finally, it is not systematic: relying on the LLM as the sole source of candidates, it explores a narrow slice of the design space and settles into local optima. We introduce AtumAI, a framework that generates datacenter control-plane policies with agentic AI, making the process formal, transferable, and systematic. From a goal stated in plain language, AtumAI autonomously proposes, tests, and refines candidate policies until one satisfies the request. It does so through two components. The Datacenter Task Compiler automates problem formulation: it compiles the request into a formal, machine-checkable, and searchable specification of the task's objectives, constraints, decision variables, and evaluation methodology. The Evolutionary Design Discovery Loop then searches this specification, expanding the search beyond the LLM itself via a diffusion model, an evolutionary algorithm, and a surrogate model. Together, they reduce onboarding a new task from months of engineering to writing its description. We evaluate AtumAI on three control-plane tasks with distinct problem scopes, design spaces, and trade-offs: workload placement, resource scaling, and power management. Across all tasks, the policies generated by AtumAI consistently outperform expert-engineered baselines.