Learning-Infused Formal Reasoning: From Contract Synthesis to Artifact Reuse and Formal Semantics

TL;DR

This paper proposes a long-term vision for formal methods enhanced by AI, integrating automated contract synthesis, semantic artifact reuse, and hybrid reasoning to create evolving, knowledge-driven verification systems.

Contribution

It introduces a hybrid framework combining large language models and graph-based representations for scalable semantic matching and artifact reuse in formal verification.

Findings

Conceptual framework for AI-augmented formal methods.

Hybrid approach combining neural and symbolic reasoning.

Path toward evolving verification ecosystems.

Abstract

This vision paper articulates a long-term research agenda for formal methods at the intersection with artificial intelligence, outlining multiple conceptual and technical dimensions and reporting on our ongoing work toward realising this agenda. It advances a forward-looking perspective on the next generation of formal methods based on the integration of automated contract synthesis, semantic artifact reuse, and refinement-based theory. We argue that future verification systems must move beyond isolated correctness proofs toward a cumulative, knowledge-driven paradigm in which specifications, contracts, and proofs are continuously synthesised and transferred across systems. To support this shift, we outline a hybrid framework combining large language models with graph-based representations to enable scalable semantic matching and principled reuse of verification artifacts. Learning-based components provide semantic guidance across heterogeneous notations and abstraction levels, while symbolic matching ensures formal soundness. Grounded in compositional reasoning, this vision points toward verification ecosystems that evolve systematically, leveraging past verification efforts to accelerate future assurance.