Wi-Fi rate adaptation remains a persistent challenge in wireless networking. Deployed algorithms like Minstrel-HT have remained largely stagnant for over a decade, relying on hand-tuned heuristics that fail to generalize to the complexity of modern wireless environments. We present \name, an autonomous research system that closes the loop on rate control development. IteRate uses a multi-agent AI architecture to conduct the full scientific cycle: formulating hypotheses, writing eBPF programs that run inside the Linux kernel, deploying them over-the-air to Wi-Fi devices, collecting fine-grained telemetry for analysis, and iterating based on experimental evidence, all without human intervention. IteRate makes three contributions. (1) a novel kernel module that exposes per-frame hardware telemetry including modulation and coding schemes (MCS) and retry counts to eBPF programs, (2) a structured agentic AI architecture employing specialized agents for algorithm design, experiment execution, and data analysis, coordinated via a hypothesis-driven research protocol with persistent knowledge, and (3) a closed-loop pipeline that automates the cross-compilation, deployment, and evaluation of in-kernel logic onto embedded Wi-Fi targets. On a 58-node testbed running five workloads. relative to the well-known Minstrel algorithm, IteRate achieves 21% faster web-page loads, 7% higher video quality of experience (QoE), and 21% higher peak throughput. Our work demonstrates that AI agents, when equipped with appropriate kernel-level hooks and a disciplined scientific workflow, can effectively automate the research required to design Wi-Fi rate controllers.

翻译：Wi-Fi速率自适应仍然是无线网络中的持续挑战。像Minstrel-HT这类已部署算法在过去十余年间基本停滞不前，其依赖的手工调优启发式方法无法泛化到现代无线环境的复杂性。我们提出自主研究系统IteRate，它闭环完成了速率控制的开发流程。IteRate采用多智能体AI架构进行完整的科学循证：形成假设、编写在Linux内核中运行的eBPF程序、通过无线方式部署至Wi-Fi设备、收集细粒度遥测数据用于分析，并基于实验证据进行迭代优化，整个过程无需人工干预。IteRate有三项贡献：(1) 新型内核模块，向eBPF程序暴露包含调制与编码策略（MCS）和重试计数在内的每帧硬件遥测数据；(2) 结构化智能体AI架构，采用专业智能体分别负责算法设计、实验执行和数据分析，通过基于假设的持久知识研究协议进行协调；(3) 闭环流水线，自动完成针对嵌入式Wi-Fi目标的内核逻辑交叉编译、部署与评估。在运行五种工作负载的58节点测试平台上，与知名Minstrel算法相比，IteRate实现网页加载速度提升21%、视频体验质量（QoE）提升7%、峰值吞吐量提升21%。本研究表明，当AI智能体配备适当的内核级钩子与严谨的科学工作流程时，能够有效自动化设计Wi-Fi速率控制器所需的研究工作。