Abstract:Neural network verification is an active and rapidly maturing research area, with a growing ecosystem of solvers and tools. The VNN-LIB standard was introduced to support interoperability in this ecosystem, but Version~1.0 has several serious short-comings as a formal foundation: it lacks a precise syntax, semantics, and type system, offers limited expressivity, and relies on externally defined ONNX models whose semantics are informal and constantly evolving. The latter distinguishes VNN-LIB from established standards such as SMT-LIB, where queries are self-contained and have fixed semantics.
In this paper we address these challenges by developing the theoretical foundations of VNN-LIB~2.0. Our key contribution is the introduction of the notion of a \emph{network theory}, which abstractly characterises the minimal semantic interface required from a neural network model format. This abstraction enables VNN-LIB to be defined independently of any specific ONNX version while remaining compatible with evolving model representations. Building on this foundation, we present a formal syntax for a more expressive query language, a type system for it over the numeric domains provided by the network theory, and finally a formal semantics. To ensure internal consistency, the standard is mechanised in the Agda theorem prover. VNN-LIB~2.0 therefore provides robust and rigorous foundations for trustworthy neural network verification.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2605.07451 [cs.LG] |
| (or arXiv:2605.07451v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.07451 arXiv-issued DOI via DataCite (pending registration) |
From: Matthew Daggitt Dr [view email]
[v1]
Fri, 8 May 2026 08:56:59 UTC (71 KB)
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