面向小区边缘功率控制的智能体自动研究：从根本上重新定义研究者的角色
Agentic Autoresearch for Cell-Edge Power Control: Radically Redefining the Researcher's Role
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中文总结 AI 辅助
该研究提出智能体自动研究协议，将无线资源管理的机器学习算法设计交由AI编码智能体完成，在小区边缘功率控制任务中实现高性能与低推理成本，还恢复了可证明的最优结构。
中文摘要 AI 辅助
为无线资源管理设计机器学习算法十分繁琐：架构、损失函数和训练方案均需手动指定。我们证明可将整个设计层完全交由自主智能体处理。我们采用自动研究协议，其中AI编码智能体编辑训练脚本、运行固定预算实验，并根据单一不变指标保留或丢弃变更。我们赋予智能体对架构族、输入表示、输出参数化、损失函数和任务采样规则的控制权，并为其设定了一个颇具难度的目标：多小区网络中的最小百分率速率总和功率控制。该公式针对小区边缘吞吐量，且在远离其最大-最小顶点时是非凸、非光滑且强NP难的。保障措施确保结果可信：每个实验均有哈希绑定评估器、强制推理契约和预注册的证伪器。在26小时内完成的81次无人值守实验中，智能体在一次固定成本推理传递中达到了收敛的最小化-最大化参考值的99.5%，推理成本降低了约600倍，从首个可行架构起缩小了94%的差距，且有一组参数适用于所有网络规模和百分率目标。它恢复了可证明的结构而非调优常数：其发现的输出参数化在最小百分率下可再现精确的最大-最小最优分配，无论训练权重取值如何。
英文摘要
Designing machine learning algorithms for wireless resource management is labour-intensive: the architecture, the loss function and the training recipe are all specified by hand. We demonstrate that this design layer can be surrendered to an autonomous agent in its entirety. We adopt the autoresearch protocol, in which an AI coding agent edits a training script, runs a fixed-budget experiment, and retains or discards the change according to a single immutable metric. We grant the agent authority over the architecture family, the input representation, the output parameterization, the loss function and the task-sampling law, and set it a target chosen for its difficulty: sum-least-percentile-rate power control across a multicell network. The formulation targets cell-edge throughput and is non-convex, non-smooth and strongly NP-hard away from its max-min vertex. Safeguards render the results trustworthy: a hash-pinned evaluator, an enforced inference contract and a pre-registered falsifier per experiment. In eighty-one unattended experiments over twenty-six hours, the agent reached $99.5\%$ of a converged minorization-maximization reference in one fixed-cost inference pass, at roughly $600\times$ lower inference cost, closing $94\%$ of the gap from its first working architecture, with one parameter set serving every network size and percentile target. It recovered provable structure rather than tuned constants: the output parameterization it discovered reproduces the exact max-min-optimal allocation at the minimum percentile, for every value of the trained weights.
发表机构
- Ericsson R&D（爱立信研发部门）
- University of Toronto（多伦多大学）
机构由 AI 辅助整理，请以论文原文为准。
