发表机构
University of California, Santa Barbara; University of California, Irvine; Northwestern University（加州大学圣巴巴拉分校; 加州大学欧文分校; 西北大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出面向实证网络研究的可组合领域专用后端Pramana，其通过单一意图规范实现跨载体运行，概念验证可满足34%的数据生成意图，为加速网络研究提供了支撑。
AI 中文摘要
网络研究的进步依赖于将假设转化为实证证据，因此加速这一进程意味着缩短构思（合成假设）与生成验证数据之间的滞后时间。以一个具体案例为例：批量BBR下载是否会与竞争的实时Google Meet流量公平共享其瓶颈链路？验证这一点需要配置真实的瓶颈链路、同时生成BBR的批量传输和Meet的实时流量，并收集相关的服务质量指标。如今，这类 overhead 很高，通常迫使研究人员为每个新想法从头开始。在智能体AI时代，构思到数据生成的差距只会进一步扩大，因为AI辅助构思呈指数级加速，但其输出若没有数据生成后端就无法验证。本文探讨如何弥合这一差距。我们设想了一种可组合的领域专用后端Pramana，其结构类似细腰，顶部是多样化的研究意图，底部是不同的执行载体。Pramana通过单一契约（即意图规范）实现这一“腰部”结构，该契约将实验分解为三个独立维度：意图（生成何种数据）、载体（在何处生成数据）和机制（如何生成数据），因此一个规范可在任何载体上运行。我们通过构建首个由66篇已发表论文中挖掘出的255个数据生成意图组成的语料库，证明了Pramana的实用性，结果显示意图规范满足所有这些意图，而现有工具中没有一款能满足超过13%的意图。我们当前的概念验证实现已满足34%的此类意图，是现有最佳工具的两倍多，我们还规划了路线图，旨在通过更广泛的社区努力弥合抽象与实现之间的差距，构建设想的数据生成后端，加速实证网络研究。
英文摘要
Networking research advances by turning hypotheses into empirical evidence, so accelerating it means reducing the lag between ideation (synthesizing a hypothesis) and generating the data that tests it. Consider a concrete case: does a bulk BBR download fairly share its bottleneck with competing real-time Google Meet traffic? Validating this requires configuring a realistic bottleneck link, concurrently generating BBR's bulk transfer and Meet's real-time traffic, and collecting relevant service-quality metrics. Today this overhead is high, often forcing researchers to start from scratch for every new idea. This ideation-to-data-generation gap will only worsen in the agentic AI era, where AI-assisted ideation accelerates exponentially, yet its outputs cannot be validated without a data-generation backend. This paper explores how to bridge this gap. We envision a composable, domain-specific backend, Pramana, shaped as a thin waist, with diverse research intents at the top and disparate execution substrates at the bottom. Pramana realizes this waist through a single contract, the intent specification, which disaggregates an experiment into three independent axes: the intent (what data to generate), the substrate (where to generate it), and the mechanism (how to produce it), so one specification runs on any substrate. We demonstrate Pramana's utility by building a first-of-its-kind corpus of 255 data-generation intents mined from 66 published papers, and show the intent specification satisfies all of them, where no existing tool satisfies more than 13%. Our current proof-of-concept implementation already satisfies 34% of these intents, more than twice the best existing tool, and we lay out a roadmap for closing this abstraction-implementation gap through a broader community effort to build the envisioned data-generation backend and accelerate empirical networking research.
