用AI发现网络信号不变式，自动识别可靠规则。
Invariant Discovery for Networked Systems
- 先用大模型生成可能的规则语法，再在语法内搜索统计有效的不变式。
- 在真实数据上恢复专家规则，覆盖率高且误报少。
- 适合需要自动化验证和故障诊断的网络系统研发人员。
不变式是网络中测量信号间预期保持关系的核心概念，广泛用于验证、流量生成、遥测补全和输入校验等场景，但手工编写需同时具备形式逻辑与网络知识的专家级能力。现有自动挖掘工具仍需人工提供不变式语法规则，且仅能学习精确（硬性）规则，难以处理现实数据中的噪声导致的近似情况。大语言模型虽具语义推理能力，但其学习过程非确定且不可解释。本文核心思想是将不变式搜索分解为两步：首先由AI驱动发现规则语法，再在该语法框架内进行统计搜索。该方法使易产生幻觉的AI可生成可审计、具形式保证的不变式。我们设计并实现了名为Autogram的系统，在公开与生产环境遥测数据上评估，成功恢复了专家制定的不变式，覆盖率达高且误报率低。最后讨论了迈向完全开放探索路径中的若干挑战。
原文摘要 · Abstract (English)
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. Automatic miners can help but fall short on two fronts: they still require the hardest input (the grammar of admissible invariants) and they learn only exact, ``hard'' rules, struggling with real-world approximation caused by inherent noise in data. LLMs are tools that can provide semantic reasoning over data, but are non-deterministic and opaque in their learning. Our key idea is to partition the invariant search problem into an AI-driven grammar ``discovery'' problem, followed by a statistics-driven ``search'' problem within the learned grammar. Taken together, this allows non-deterministic, hallucination-prone AI to help produce auditable invariants with formal guarantees. We design and implement such a system, Autogram, and evaluate it on both public and production telemetry data, recovering expert-derived invariants with high coverage and low false positives. We close with discussion on open problems on the path toward fully open-ended discovery.
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