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

2026-02-02

AI Summary

Traditional formal verification lacks mechanisms for knowledge accumulation and cross-system reuse, making it difficult to transfer specifications, contracts, and proofs. This work proposes a novel paradigm that integrates artificial intelligence with formal methods, pioneering the combination of large language models and graph-based representations to enable semantic guidance across heterogeneous notations and abstraction levels. By leveraging automated contract synthesis, semantic artifact reuse, and compositional refinement theory, the authors construct a hybrid reasoning framework that ensures formal reliability while supporting continuous synthesis and migration of verification artifacts. This approach lays the foundation for a cumulative and evolvable verification ecosystem, paving the way toward scalable, knowledge-driven next-generation verification systems.

Technology Category

• Knowledge Representation and Reasoning: Automated Reasoning and Theorem Proving
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories
• Computer Vision: Visual Reasoning & Symbolic Representations

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

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.

Problem

• formal reasoning
• contract synthesis
• artifact reuse
• semantic matching
• verification ecosystem

Innovation

• learning-infused formal reasoning
• contract synthesis
• semantic artifact reuse
• graph-based representation
• compositional reasoning