[Submitted on 28 Jul 2026]

Authors:Jaber Daneshamooz, Eugene Vuong, Alagappan Ramanathan, Manni Moghimi, Haarika Manda, Satyam Kumar, Snithik Thode, Satyandra Guthula, Sylee Beltiukov, Dongsu Han, Tarun Mangla, Sangeetha Abdu Jyothi, Walter Willinger, Arpit Gupta

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Abstract:網路研究的進展仰賴將假設轉化為實證，因此加速研究意味著縮短從提出想法（綜合假設）到產生測試資料之間的延遲。以一個具體情境來說：大量 BBR 下載是否能與競爭的即時 Google Meet 流量公平分享瓶頸？驗證此假設需要設定真實的瓶頸連結、同時產生 BBR 的批量傳輸與 Meet 的即時流量，並蒐集相關的服務品質指標。目前這項開銷極高，研究人員往往必須為每個新想法從頭開始。在代理式 AI 時代，此從構思到資料產生的落差只會更加嚴重，因為 AI 輔助的構思速度呈指數成長，但其輸出卻無法在沒有資料產生後端的情況下獲得驗證。
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.

Submission history

From: Jaber Daneshamooz [view email]
[v1] Tue, 28 Jul 2026 23:51:57 UTC (559 KB)