arXiv cs.AI· Hongyu H\`e, Alexander Krentsel, Sylvia Ratnasamy, Maria Apostolaki·· 11 小时前AI 评分31
Autogram：用 LLM 引导语法归纳自动发现网络系统不变量
Invariant Discovery for Networked Systems
AI 导读
Autogram 让 LLM 检查变量元数据、分类计数器数据类型并预测候选语法关系，再在生成的紧凑语法内做确定性统计搜索，若无法恢复已知不变量则让 LLM 迭代扩宽语法。该方法将 LLM 限定在语法归纳与再归纳环节，候选验证交给逻辑与统计检查，从而把非确定性 AI 提议转为可审计、有形式化保证的不变量。在公开与生产 WAN 遥测数据上，Autogram 以完整召回率和低误报恢复了专家推导的不变量。
正文 · 原文
Abstract:Invariants, the relations expected to hold among measured signals of a network, underpin applications from verification to traffic generation, telemetry imputation, and input validation. Yet, writing them by hand demands rare expertise in both formal logic and networking. Testing all possible combinations of telemetry variables statistically is intractable at network scale, while existing automatic tools still require a human-authored grammar of admissible invariants and learn only exact, "hard" rules that break under real-world noise. Our key idea is to use an LLM to inspect variable metadata, classify counter data types, and predict candidate syntactic relationships that form a compact grammar, followed by a deterministic statistical search within that grammar. If the initial grammar fails to recover trusted known invariants on held-out telemetry, Autogram instructs the LLM to iteratively widen the grammar until recovery succeeds. By confining the LLM to grammar induction and re-induction while delegating candidate verification to logical and statistical checks, Autogram turns non-deterministic AI proposals into auditable invariants with formal guarantees within the induced grammar. We design and prototype Autogram and evaluate it on both public and production WAN telemetry, recovering expert-derived invariants with full recall and low false positives. We close with a discussion of open problems on the path toward fully open-ended invariant discovery.
| Comments: | ACM HotNets 2026 |
| Subjects: | Networking and Internet Architecture (cs.NI); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Symbolic Computation (cs.SC) |
| MSC classes: | 68M15 (Primary), 68T27, 68T05 (Secondary) |
| ACM classes: | C.2.3; I.2.6; I.2.3 |
| Cite as: | arXiv:2607.22944 [cs.NI] |
| (or arXiv:2607.22944v2 [cs.NI] for this version) | |
| https://doi.org/10.48550/arXiv.2607.22944 arXiv-issued DOI via DataCite |
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| Related DOI: | https://doi.org/10.1145/3847290.3859952
DOI(s) linking to related resources |
Submission history
From: Hongyu Hè [view email]
[v1]
Fri, 24 Jul 2026 23:07:31 UTC (519 KB)
[v2]
Thu, 5 Nov 2026 00:15:25 UTC (306 KB)
来源：arXiv cs.AI · arxiv.org