Short Version of VERIFAI2026 Paper -- Learning Infused Formal Reasoning: Contract Synthesis, Artefact Reuse and Semantic Foundations

2026-04-14

AI Summary

Existing formal methods incur high costs in specification construction and maintenance and lack scalability, making them ill-suited for verifying modern AI systems. This work proposes a Learning-Integrated Formal Reasoning (LIFR) framework that innovatively combines machine learning with formal verification: it employs natural language processing to automatically generate contracts, leverages graph matching and representation learning to achieve semantic alignment and cross-system reuse of verification artifacts, and establishes a rigorous semantic foundation grounded in Unifying Theories of Programming (UTP) and institution theory. By shifting formal verification from isolated proofs toward a cumulative, knowledge-driven paradigm, the LIFR framework substantially enhances automation and scalability while preserving formal rigor.

Technology Category

• Knowledge Representation and Reasoning: Automated Reasoning and Theorem Proving
• Machine Learning: Life-Long and Continual Learning
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Artificial intelligence systems have achieved remarkable capability in natural language processing, perception and decision-making tasks. However, their behaviour often remains opaque and difficult to verify, limiting their applicability in safety-critical systems. Formal methods provide mathematically rigorous mechanisms for specifying and verifying system behaviour, yet the creation and maintenance of formal specifications remains labour intensive and difficult to scale. This paper outlines a research vision called Learning-Infused Formal Reasoning (LIFR), which integrates machine learning techniques with formal verification workflows. The framework focuses on three complementary research directions: automated contract synthesis from natural language requirements, semantic reuse of verification artifacts using graph matching and learning-based embeddings, and mathematically grounded semantic foundations based on the Unifying Theories of Programming (UTP) and the Theory of Institutions. Together these research threads aim to transform verification from isolated correctness proofs into a cumulative knowledge-driven process where specifications, contracts and proofs can be synthesised, aligned and reused across systems.

Problem

• formal verification
• AI safety
• specification synthesis
• verification scalability
• system transparency

Innovation

• Learning-Infused Formal Reasoning
• Contract Synthesis
• Verification Artifact Reuse
• Semantic Embeddings
• Unifying Theories of Programming