Airavat用智能代理自动生成并验证互联网测量流程，解决专家依赖和方法错误问题。
Airavat: An Agentic Framework for Internet Measurement
- 采用多智能体协作，自动分解任务、设计方案并生成代码。
- 在4个案例中生成的方案与专家水平相当，且能发现传统测试遗漏的问题。
- 适合需要可靠测量流程的研究者或自动化系统开发者。
互联网测量面临双重挑战：复杂分析需专家级工具编排，而即使语法正确的实现也可能存在方法学缺陷且难以验证。推动测量能力普及，需同时自动化工作流生成与方法学验证。我们提出Airavat，首个用于互联网测量工作流生成的智能体框架，具备系统性验证与验证能力。Airavat协调一组模仿专家推理的智能体：三个智能体分别负责问题分解、解决方案设计和代码实现，并借助现有工具注册表支持。两个专用引擎确保方法学正确性：验证引擎基于编码五十年研究知识的图谱评估工作流，验证引擎则基于成熟方法论识别合适的验证技术。通过四个互联网测量案例研究，我们证明Airavat（i）生成的流程达到专家水平，（ii）做出合理架构决策，（iii）在无真实标签情况下应对新问题，（iv）识别出标准执行测试所遗漏的方法学缺陷。
原文摘要 · Abstract (English)
Internet measurement faces twin challenges: complex analyses require expert-level orchestration of tools, yet even syntactically correct implementations can have methodological flaws and can be difficult to verify. Democratizing measurement capabilities thus demands automating both workflow generation and verification against methodological standards established through decades of research. We present Airavat, the first agentic framework for Internet measurement workflow generation with systematic verification and validation. Airavat coordinates a set of agents mirroring expert reasoning: three agents handle problem decomposition, solution design, and code implementation, with assistance from a registry of existing tools. Two specialized engines ensure methodological correctness: a Verification Engine evaluates workflows against a knowledge graph encoding five decades of measurement research, while a Validation Engine identifies appropriate validation techniques grounded in established methodologies. Through four Internet measurement case studies, we demonstrate that Airavat (i) generates workflows matching expert-level solutions, (ii) makes sound architectural decisions, (iii) addresses novel problems without ground truth, and (iv) identifies methodological flaws missed by standard execution-based testing.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。